This PDF file contains the front matter associated with SPIE Proceedings Volume 7626, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

The strain encoding (SENC) technique directly encodes regional strain of the heart into the acquired MR images and produces two images with two different tunings so that longitudinal strain, on the short-axis view, or circumferential strain on the long-axis view, are measured. Interleaving acquisition is used to shorten the acquisition time of the two tuned images by 50%, but it suffers from errors in the strain calculations due to inter-tunings motion of the heart. In this work, we propose a method to correct for the inter-tunings motion by estimating the motion-induced shift in the spatial frequency of the encoding pattern, which depends on the strain rate. Numerical data was generated to test the proposed method and real images of human subjects were used for validation. The proposed method corrected the measured strain values so they became nearly identical to the original ones. The results show an improvement in strain calculations so as to relax the imaging constraints on spatial and temporal resolutions and improve image quality.

Spatial correspondence between histology and multi sequence MRI can provide information about the capabilities of non-invasive imaging to characterize cancerous tissue. However, shrinkage and deformation occurring during the excision of the tumor and the histological processing complicate the co registration of MR images with histological sections. This work proposes a methodology to establish a detailed 3D relation between histology sections and in vivo MRI tumor data. The key features of the methodology are a very dense histological sampling (up to 100 histology slices per tumor), mutual information based non-rigid B-spline registration, the utilization of the whole 3D data sets, and the exploitation of an intermediate ex vivo MRI. In this proof of concept paper, the methodology was applied to one tumor. We found that, after registration, the visual alignment of tumor borders and internal structures was fairly accurate. Utilizing the intermediate ex vivo MRI, it was possible to account for changes caused by the excision of the tumor: we observed a tumor expansion of 20%. Also the effects of fixation, dehydration and histological sectioning could be determined: 26% shrinkage of the tumor was found. The annotation of viable tissue, performed in histology and transformed to the in vivo MRI, matched clearly with high intensity regions in MRI. With this methodology, histological annotation can be directly related to the corresponding in vivo MRI. This is a vital step for the evaluation of the feasibility of multi-spectral MRI to depict histological groundtruth.

Indocyanine green (ICG) is widely used for its clearance test in the evaluation of liver function. Gadoxetate disodium (Gd-EOB-DTPA) is a targeted MR contrast agent partially taken up by hepatocytes. The objective of this study was to evaluate the feasibility of an estimation of the liver function corresponding to plasma disappearance rate of indocyanine green (ICG-PDR) by use of the signal intensity of the liver alone in Gd-EOB-DTPA enhanced MR imaging (EOB-MRI). We evaluated fourteen patients who had EOB-MRI and ICG clearance test within 1 month. 2D-GRE T1 weighted images were obtained at pre contrast, 3 min (equilibrium phase) and 20 min (hepatobiliary phase) after the intravenous administration of Gd-EOB-DTPA, and the mean signal intensity of the liver was measured. The correlation between ICG-PDR and many parameters derived from the signal intensity of the liver in EOB-MRI was evaluated. The correlation coefficient between ICG-PDR and many parameters derived from the signal intensity of the liver in EOBMRI was low and not significant. The estimation of the liver function corresponding to ICG-PDR by use of the signal intensity of the liver alone in EOB-MRI would not be reliable.

Hyperpolarized helium-3 (³He) magnetic resonance imaging (MRI) has emerged as a non-invasive research method for quantifying lung structural and functional changes, enabling direct visualization in vivo at high spatial and temporal resolution. Here we described the development of methods for quantifying ventilation dynamics in response to salbutamol in Chronic Obstructive Pulmonary Disease (COPD). Whole body 3.0 Tesla Excite 12.0 MRI system was used to obtain multi-slice coronal images acquired immediately after subjects inhaled hyperpolarized ³He gas. Ventilated volume (VV), ventilation defect volume (VDV) and thoracic cavity volume (TCV) were recorded following segmentation of ³He and ¹H images respectively, and used to calculate percent ventilated volume (PVV) and ventilation defect percent (VDP). Manual segmentation and Otsu thresholding were significantly correlated for VV (r=.82, p=.001), VDV (r=.87 p=.0002), PVV (r=.85, p=.0005), and VDP (r=.85, p=.0005). The level of agreement between these segmentation methods was also evaluated using Bland-Altman analysis and this showed that manual segmentation was consistently higher for VV (Mean=.22 L, SD=.05) and consistently lower for VDV (Mean=-.13, SD=.05) measurements than Otsu thresholding. To automate the quantification of newly ventilated pixels (NVp) post-bronchodilator, we used translation, rotation, and scaling transformations to register pre-and post-salbutamol images. There was a significant correlation between NV_p and VDV (r=-.94 p=.005) and between percent newly ventilated pixels (PNV_p) and VDP (r=- .89, p=.02), but not for VV or PVV. Evaluation of ³He MRI ventilation dynamics using Otsu thresholding and landmark-based image registration provides a way to regionally quantify functional changes in COPD subjects after treatment with beta-agonist bronchodilators, a common COPD and asthma therapy.

The existence of the potential non-independency between task-related components in multi-task functional magnetic resonance imaging (fMRI) studies limits the general application of Independent Component Analysis (ICA) method. The ICA with projection (ICAp) method proposed by Long (2009, HBM) demonstrated its capacity to solve the interaction among task-related components of multi-task fMRI data. The basic idea of projection is to remove the influence of the uninteresting tasks through projection in order to extract one interesting task-related component. However, both the stimulus paradigm of each task and the homodynamic response function (HRF) are essential for the projection. Due to the noises in the data and the variability of the HRF across the voxels and subjects, the ideal time course of each task for projection would be deviant from the true value, which might worsen the ICAp results. In order to make the time courses for projection closer to the true value, the iterative ICAp is proposed in this study. The iterative ICAp is based on the assumption that the task-related time courses extracted from the fMRI data by ICAp is more approximate to the true value than the ideal reference function. Simulated experiment proved that both the spatial detection power and the temporal accuracy of time course were increased for each task-related component. Moreover, the results of the real two-task fMRI data were also improved by the iterative ICAp method.

Brain connectivity patterns are useful in understanding brain function and organization. Anatomical brain connectivity is largely determined using the physical synaptic connections between neurons. In contrast statistical brain connectivity in a given brain population refers to the interaction and interdependencies of statistics of multitudes of brain features including cortical area, volume, thickness etc. Traditionally, this dependence has been studied by statistical correlations of cortical features. In this paper, we propose the use of Bayesian network modeling for inferring statistical brain connectivity patterns that relate to causal (directed) as well as non-causal (undirected) relationships between cortical surface areas. We argue that for multivariate cortical data, the Bayesian model provides for a more accurate representation by removing the effect of confounding correlations that get introduced due to canonical dependence between the data. Results are presented for a population of 466 brains, where a SEM (structural equation modeling) approach is used to generate a Bayesian network model, as well as a dependency graph for the joint distribution of cortical areas.

The alterations of the fornix in mild traumatic brain injury (mTBI) were investigated using diffusion tensor imaging (DTI) and T1-weighetd anatomical imaging. The primary goal of this study was to test that hypothesis that the fornix might play a major role in the memory and learning dysfunctions in the post-concussion syndrome, which may related to the white matter (WM) degradations following mild traumatic brain injury. N=24 mTBI patients were longitudinally studied in two time points with 6-month intervals using a 4-Tesla MRI scanner to measure the WM integrity of fornix and the fornix-to-brain ratio (FBR), and compared with matched healthy controls. Our data show that the WM degradation in fornix onset in the acute stage after mild TBI when the post-injury time was less than 6 weeks, and that this WM degradation continued during the following 6-month period of recovery. In summary, using DTI and structural MRI together can effectively detect the fornix changes in both cross-sectional and longitudinal investigations. Further studies are warranted to exam the association between the fornix alterations and neurocognitive performance of TBI patients.

This paper aims at analyzing gender differences in the 3D shapes of lateral ventricles, which will provide reference for the analysis of brain abnormalities related to neurological disorders. Previous studies mostly focused on volume analysis, and the main challenge in shape analysis is the required step of establishing shape correspondence among individual shapes. We developed a simple and efficient method based on anatomical landmarks. 14 females and 10 males with matching ages participated in this study. 3D ventricle models were segmented from MR images by a semiautomatic method. Six anatomically meaningful landmarks were identified by detecting the maximum curvature point in a small neighborhood of a manually clicked point on the 3D model. Thin-plate spline was used to transform a randomly selected template shape to each of the rest shape instances, and the point correspondence was established according to Euclidean distance and surface normal. All shapes were spatially aligned by Generalized Procrustes Analysis. Hotelling T² twosample metric was used to compare the ventricle shapes between males and females, and False Discovery Rate estimation was used to correct for the multiple comparison. The results revealed significant differences in the anterior horn of the right ventricle.

Statistical shape analysis of brain structures has gained increasing interest from neuroimaging community because it can precisely locate shape differences between healthy and pathological structures. The most difficult and crucial problem is establishing shape correspondence among individual 3D shapes. This paper proposes a new algorithm for 3D shape correspondence. A set of landmarks are sampled on a template shape, and initial correspondence is established between the template and the target shape based on the similarity of locations and normal directions. The landmarks on the target are then refined by iterative thin plate spline. The algorithm is simple and fast, and no spherical mapping is needed. We apply our method to the statistical shape analysis of the corpus callosum (CC) in phenylketonuria (PKU), and significant local shape differences between the patients and the controls are found in the most anterior and posterior aspects of the corpus callosum.

Gray matter volume and cortical thickness are two important indices widely used to detect neuropathological changes in brain structural magnetic resonance imaging. Using optimized voxel-based morphometry (VBM) protocol and surface-based cortical thickness measure, this study comprehensively investigated the regional changes in cortical gray matter volume and cortical thickness in Alzheimer's disease (AD). Thirteen patients with AD and fourteen age- and gender-matched healthy controls were included in this study. Results showed that voxel-based gray matter volume and cortical thickness reductions were highly correlated in the temporal lobe and its medial structure in AD. Moreover significant reduced cortical regions of gray matter volume were obviously more than that of cortical thickness. These findings suggest that gray matter volume and cortical thickness, as two important imaging markers, are effective indices for detecting the neuroanatomical alterations and help us understand the neuropathology from different views in AD.

Resting-state functional MRI (fMRI) studies have suggested the posterior cingulate cortex (PCC) plays a pivotal role in the default mode network (DMN), a set of co-activated brain regions characterizing the resting-state brain. Concerning this finding we propose the following questions in this study: Does PCC consistently play the equally crucial role in the DMN across different subjects, such as healthy young and healthy old subjects? Whether the fMRI scan environments or parameters would affect the results? To address these questions, we collected resting-state fMRI data on four groups of subjects: two healthy young groups scanned under 3-T and 1.5-T MRI systems respectively, and two healthy elderly groups both scanned under 3-T MRI system but with different scan parameters. Then group independent component analysis was used to isolate the DMN, and partial correlation analysis was employed to reveal the direct interactions between brain regions from the DMN. Finally, we measured the connectivity between brain regions based on the number of significantly interacted links to every region within this network. We found that PCC was the brain region consistently having the largest number of directly interacted regions in the four groups, suggesting the pivotal role of PCC in the DMN was stable and consistent across healthy subjects. The results also suggested the function of PCC would be more critical in healthy elderly subjects compared with healthy young subjects. And the factors of scan environments and parameters did not show any obvious impact on the above conclusions in this investigation.

On joint analysis of multi-task brain imaging data sets, a variety of multivariate methods have shown their strengths and been applied to achieve different purposes based on their respective assumptions. In this paper, we provide a comprehensive review on optimization assumptions of six data fusion models, including 1) four blind methods: joint independent component analysis (jICA), multimodal canonical correlation analysis (mCCA), CCA on blind source separation (sCCA) and partial least squares (PLS); 2) two semi-blind methods: parallel ICA and coefficient-constrained ICA (CC-ICA). We also propose a novel model for joint blind source separation (BSS) of two datasets using a combination of sCCA and jICA, i.e., 'CCA+ICA', which, compared with other joint BSS methods, can achieve higher decomposition accuracy as well as the correct automatic source link. Applications of the proposed model to real multitask fMRI data are compared to joint ICA and mCCA; CCA+ICA further shows its advantages in capturing both shared and distinct information, differentiating groups, and interpreting duration of illness in schizophrenia patients, hence promising applicability to a wide variety of medical imaging problems.

Bony structures at the skull base were the main obstacle to detection and estimation of arterial stenoses and aneurysms for CT angiography in the brain. Direct subtraction and the matched mask bone elimination (MMBE) have become two standard methods for removing bony structures. However, clinicians regularly find that calcified plaques at or near the carotid canal cannot be removed satisfactorily by existing methods. The blood-plaque boundary tends to be blurred by subtraction operation while plaque size is constantly overestimated by the bone mask dilation operation in the MMBE approach. In this study, we propose using the level of enhancement to adjust the MMBE bone mask more intelligently on the artery- and tissue-bone/plaque boundaries. The original MMBE method is only applied to the tissue-bone boundary voxels; while the artery-bone/blood-plaque boundary voxels, identified by a higher enhancement level, are processed by direct subtraction instead. A dataset of 6 patients (3 scanned with a regular dose and 3 scanned with a reduced dose) with calcified plaques at or near the skull base is used to examine our new method. Preliminary results indicate that the visualization of intracranial arteries with calcified plaques at the skull base can be improved effectively and efficiently.

Magnetic resonance spectroscopic imaging (MRSI) has been shown to provide valuable information about the biochemistry of the anatomy of interest and thus has been increasingly used in clinical research. However, the long acquisition time associated with multidimensional MRSI is a barrier for translation of this technology to the clinic. A novel approach using the application of compressive sensing, to reduce the acquisition time of MRSI is proposed. Reconstruction of data, simulated to be acquired through compressed sensing is implemented on a computer generated phantom simulating two metabolites of the human brain. The effect of Gaussian noise on this phantom is evaluated. A retrospective analysis of the application of such a reconstruction method for ¹H MRSI of previously acquired in vitro brain phantom MRSI data is performed for the first time. On comparison of the reconstruction of the in vitro and computer generated phantoms from undersampled data to that performed from complete k-space; the errors in reconstruction was less than 1%. This indicates that our approach has a significant potential to reduce acquisition times for MRSI studies by 50% which could aid in MRSI being routinely used in the clinic.

Parametric images can represent both spatial distribution and quantification of the biological and physiological parameters of tracer kinetics. The linear least square (LLS) method is a well-estimated linear regression method for generating parametric images by fitting compartment models with good computational efficiency. However, bias exists in LLS-based parameter estimates, owing to the noise present in tissue time activity curves (TTACs) that propagates as correlated error in the LLS linearized equations. To address this problem, a volume-wise principal component analysis (PCA) based method is proposed. In this method, firstly dynamic PET data are properly pre-transformed to standardize noise variance as PCA is a data driven technique and can not itself separate signals from noise. Secondly, the volume-wise PCA is applied on PET data. The signals can be mostly represented by the first few principle components (PC) and the noise is left in the subsequent PCs. Then the noise-reduced data are obtained using the first few PCs by applying 'inverse PCA'. It should also be transformed back according to the pre-transformation method used in the first step to maintain the scale of the original data set. Finally, the obtained new data set is used to generate parametric images using the linear least squares (LLS) estimation method. Compared with other noise-removal method, the proposed method can achieve high statistical reliability in the generated parametric images. The effectiveness of the method is demonstrated both with computer simulation and with clinical dynamic FDG PET study.

The Asymmetric Vascular Stent (AVS) for intracranial aneurysm (IA) treatment is an experimental device, specially designed for intra-aneurysmal blood flow diversion and thrombosis promotion. The stent has a low-porous patch to cover only the aneurysm neck while the rest of the stent is very porous to avoid blockage of adjacent branches. The latest AVS design is similar to state-of-art, closed-cell, self-expanding, neurovascular stent. The stents were used to treat sixteen rabbit-elastase aneurysm models. The treatment effect was analyzed using normalized-time-density-curves (NTDC) measured by pixel-value integration over a region-of-interest containing the aneurysm. Normalization constant was the total bolus injection determined angiographically. Based on NTDC measurement, five quantities were derived to describe the contrast flow. Two are related to the amount of contrast entering the aneurysm: NTDC peak and NTDC input slope. The other three are related to contrast presence in the aneurysmal dome: time-to-peak (TTP), wash-out-time (WOT) and mean-transit-time (MTT). Flow modification descriptions using the contrast related quantities were expressed as a pre-/post-stented NTDC parameter ratio, while the time related quantities were expressed as a post-/prestented ratio, so that ratios smaller than one indicate a desired effect. Thirteen aneurysms were treated successfully and achieved significant aneurysm occlusion. For these cases, the resulting average parameters were: peak-ratio=0.17+0.21; input-slope-ratio=0.19±0.24, TTP-ratio=0.17+0.21, WOT-ratio=0.58±0.73 and MTT-ratio=0.65±0.97). All the quantities revealed decreased aneurysmal flow due to blood flow diversion using the new self-expanding asymmetrical vascular stent (SAVS). Treatment outcome results and angiographic analysis indicate that the new self-deploying stent design has great potential for clinical implementation.

Coarctation of the aorta (CoA) is associated with reduced life expectancy despite successful surgical treatment. Interestingly, much of the related long-term morbidity can be explained by abnormal hemodynamics, vascular biomechanics and cardiac function. MRI has played an important role in assessing coarctation severity, but the heterogeneity and small number of patients at each center presents an obstacle for determining causality. This work describes optimized imaging parameters to create computational fluid dynamics (CFD) models revealing changes in hemodynamics and vascular biomechanics from a rabbit model. CoA was induced surgically at 10 weeks using silk or dissolvable ligatures to replicate native and end-to-end treatment cases, respectively. Cardiac function was evaluated at 32 weeks using a fastcard SPGR sequence in 6-8 two-chamber short-axis views. Left ventricular (LV) volume, ejection fraction, and mass were quantified and compared to control rabbits. Phase contrast (PC) and angiographic MRI were used to create CFD models. Ascending aortic PCMRI data were mapped to the model inflow and outflow boundary conditions replicated measured pressure (BP) and flow. CFD simulations were performed using a stabilized finite element method to calculate indices including velocity, BP and wall shear stress (WSS). CoA models displayed higher velocity through the coarctation region and decreased velocity elsewhere, leading to decreased WSS above and below the stenosis. Pronounced wall displacement was associated with CoA-induced changes in BP. CoA caused reversible LV hypertrophy. Cardiac function was maintained, but caused a persistent hyperdynamic state. This model may now be used to investigate potential mechanisms of long-term morbidity.

Abnormal blood flow conditions and structural fatigue within stented vessels may lead to undesired failure causing death to the patient. Image-based computational modeling provides a physical and realistic insight into the patientspecific biomechanics and enables accurate predictive simulations of development, growth and failure of cardiovascular diseases as well as associated risks. Controlling the efficiency of an endovascular treatment is necessary for the evaluation of potential complications and predictions on the assessment of the pathological state. In this paper we investigate the effects of stent-graft implantation on the biomechanics in a patient-specific thoracic aortic model. The patient geometry and the implanted stent-graft are obtained from morphological data based on a CT scan performed during a controlling routine. Computational fluid dynamics (CFD) and computational structure mechanics (CSM) simulations are conducted based on the finite volume method (FVM) and on the finite element method (FEM) to compute the hemodynamics and the elastomechanics within the aortic model, respectively. Physiological data based on transient pressure and velocity profiles are used to set the necessary boundary conditions. Further, the effects of various boundary conditions and definition of contact interactions on the numerical stability of the blood flow and the vessel wall simulation results are also investigated. The quantification of the hemodynamics and the elastomechanics post endovascular intervention provides a realistic controlling of the state of the stented vessel and of the efficiency of the therapy. Consequently, computational modeling would help in evaluating individual therapies and optimal treatment strategies in the field of minimally invasive endovascular surgery.

A flexible flow phantom system was designed and fabricated for the purpose of validation of i) CFD models proposed in conjunction with vascular imaging and ii) medical imaging techniques (such as MRI) that can produce flow velocities. In particular, one of the most challenging flows for both CFD models when modeling flow velocities and imaging techniques when measuring flow velocities are stenotic flows. Particle Image Velocimetry (PIV) is an optical technique for accurate measurement of in-vitro flow velocities and visualization of fluid flow. The fluid is seeded with tracer particles and the motion of the particles, illuminated with a laser light sheet, reveal particle velocities. Particle Image Velocimetry (PIV) was used to measure the flow fields across a Gaussian-shaped 90% area stenosis phantom. The flow parameters were adjusted to the phantom geometry to mimic the blood flow through the human common iliac artery. In addition, Computational Fluid Dynamics (CFD) simulation of the same flow was performed and the results were validated with those from PIV measurements. Steady flow rate of 46.9 ml/s was used, which corresponds to a Reynolds number of 188 and 595 at the inlet and stenosis throat, respectively. A maximum discrepancy of 15% in peak velocity was observed between the two techniques.

Magnetic resonance imaging has been widely used in measuring cardiac motion due to its ability to non-invasively alter tissue magnetization and produce visible tags in the deforming tissue. Additionally, phase from spectral peaks of tagged images has been used for estimation of myocardial motion. In this paper, we propose integration of displacement information obtained from tagged images in the spatial domain with displacement information obtained from spectral peaks in the frequency domain in order to improve the accuracy of motion tracking. B-splines have been used extensively in temporal registration and reconstruction of myocardial deformations due to their ability to conform to local deformations while enforcing continuity. By considering the real tag intersections (in the spatial domain) and virtual tag intersections (from the frequency domain) as scattered data, multilevel B-splines (MBS) can result in accurate and fast approximations without the need to specify the control point locations explicitly. The accuracy and the effectiveness of the proposed method has been validated by using simulated data from the 13-parameter kinematic model of Arts et al.1 and by using in vivo canine data.

In this paper, we combine a ventricular kinematic model and an ultrasound simulation model in order to simulate the echocardiographic imaging process. In addition to its capability to generate raw RF data, when compared to previous echocardiography simulation models, the result achieves more realistic B-Mode images. Several echocardiography parameters were taken into account including central frequency, apodization, number of elements in the array, speed of sound, and number of scatterers. The proposed improvements are due to the use of a shift-variant Point Spread Function (PSF) and more accurate cardiac motion assumptions. One attribute of the simulator is also that it provides the groundtruth vector field of actual "ventricular deformations'' which may be used to strictly validate motion estimation and myocardial elastography algorithms. The paper presents the first application of optical flow to normalized data in piece-wise segments of RF images. Different optical flow motion estimation techniques such as Lucas-Kanade, Horn-Schunck, Brox et al., Black and Anandan, and Block Matching (BM) were applied to the simulated B-mode images and RF data. The estimated motion fields from the RF data as well as the B-mode images were validated with the ground-truth motion fields derived from the simulator. The validation results show that the Brox et al. method performs better than other motion estimation techniques when applied to B-Mode and RF data. Also, as intuitively expected, use of RF data results in more accurate displacement fields than when B-mode images alone are used.

Infrared thermography has been shown to be a useful adjunctive tool for breast cancer detection. Previous thermography modeling techniques generally dealt with the "forward problem", i.e., to estimate the breast thermogram from known properties of breast tissues. The present study aims to deal with the so-called "inverse problem", namely to estimate the thermal properties of the breast tissues from the observed surface temperature distribution. By comparison, the inverse problem is a more direct way of interpreting a breast thermogram for specific physiological and/or pathological information. In tumor detection, for example, it is particularly important to estimate the tumor-induced thermal contrast, even though the corresponding non-tumor thermal background usually is unknown due to the difficulty of measuring the individual thermal properties. Inverse problem solving is technically challenging due to its ill-posed nature, which is evident primarily by its sensitivity to imaging noise. Taking advantage of our previously developed forward-problemsolving techniques with comprehensive thermal-elastic modeling, we examine here the feasibility of solving the inverse problem of the breast thermography. The approach is based on a presumed spatial constraint applied to three major thermal properties, i.e., thermal conductivity, blood perfusion, and metabolic heat generation, for each breast tissue type. Our results indicate that the proposed inverse-problem-solving scheme can be numerically stable under imaging noise of SNR ranging 32 ~ 40 dB, and that the proposed techniques can be effectively used to improve the estimation to the tumor-induced thermal contrast, especially for smaller and deeper tumors.

Microwave imaging for biomedical applications, especially for early detection of breast cancer and effective treatment monitoring, has attracted increasing interest in last several decades. This fact is due to the high contrast between the dielectric properties of the normal and malignant breast tissues at microwave frequencies ranging from high megahertz to low gigahertz. The available range of dielectric properties for different soft tissue can provide considerable functional information about tissue health. Nonetheless, one of the limiting weaknesses of microwave imaging is, unlike that for conventional modalities such as X-ray CT or MRI, it cannot inherently provide high-resolution images. The conventional modalities can produce highly resolved anatomical information but often cannot provide the functional information required for diagnoses. We have developed a soft prior regularization strategy that can incorporate the prior anatomical information from X-ray CT, MR or other sources, and use it in a way to exploit the resolution of these images while also retaining the functional nature of the microwave images. The anatomical information is first used to create an imaging zone mesh, which segments separate internal substructures, and an associated weighting matrix that numerically groups the values of closely related nodes within the mesh. This information is subsequently used as a regularizing term for the Gauss-Newton reconstruction algorithm. This approach exploits existing technology in a systematic way without making potentially biased assumptions about the properties of visible structures. In this paper we continue our initial investigation on this matter with a series of breast-shaped simulation and phantom experiments.

A novel approach is introduced for clustering tumor regions with similar signal-time series measured by dynamic contrast-enhanced (DCE) MRI to segment the tumor area in breast cancer. Each voxel of the DCE-MRI dataset is characterized by a signal-time curve. The clustering process uses two describer values for each pixel. The first value is L2-norm of each time series. The second value r is calculated as sum of differences between each pair of S(n-i) and S(i) for i = {0...n/2} where S is the intensity and n the number of values in a time series. We call r reverse value of a time series. Each time series is considered as a vector in an n-dimensional space and the L2-norm and reverse value of a vector are used as similarity measures. The curves with similar L2-norms and similar reverse values are clustered together. The method is tested on breast cancer DCE-MRI datasets with N = 256 x 256 spatial resolution and n = 128 temporal resolution. The quality of each cluster is described through the variance of Euclidean distances of the vectors to the mean vector of the corresponding cluster. The combination of both similarity measures improves the segmentation compared to using each measure alone.

Bioluminescence tomography (BLT) is one of the most important non-invasive optical molecular imaging modalities. The model for the bioluminescent photon propagation plays a significant role in the bioluminescence tomography study. Due to the high computational efficiency, diffusion approximation (DA) is generally applied in the bioluminescence tomography. But the diffusion equation is valid only in highly scattering and weakly absorbing regions and fails in non-scattering or low-scattering tissues, such as a cyst in the breast, the cerebrospinal fluid (CSF) layer of the brain and synovial fluid layer in the joints. A hybrid Radiosity-diffusion model is proposed for dealing with the non-scattering regions within diffusing domains in this paper. This hybrid method incorporates a priori information of the geometry of non-scattering regions, which can be acquired by magnetic resonance imaging (MRI) or x-ray computed tomography (CT). Then the model is implemented using a finite element method (FEM) to ensure the high computational efficiency. Finally, we demonstrate that the method is comparable with Mont Carlo (MC) method which is regarded as a 'gold standard' for photon transportation simulation.

Optical coherence tomography (OCT), being a noninvasive imaging modality, has begun to find vast use in the diagnosis and management of ocular diseases such as glaucoma, where the retinal nerve fiber layer (RNFL) has been known to thin. Furthermore, the recent availability of the considerably larger volumetric data with spectral-domain OCT has increased the need for new processing techniques. In this paper, we present an automated 3-D graph-theoretic approach for the segmentation of 7 surfaces (6 layers) of the retina from 3-D spectral-domain OCT images centered on the optic nerve head (ONH). The multiple surfaces are detected simultaneously through the computation of a minimum-cost closed set in a vertex-weighted graph constructed using edge/regional information, and subject to a priori determined varying surface interaction and smoothness constraints. The method also addresses the challenges posed by presence of the large blood vessels and the optic disc. The algorithm was compared to the average manual tracings of two observers on a total of 15 volumetric scans, and the border positioning error was found to be 7.25 ± 1.08 μm and 8.94 ± 3.76 μm for the normal and glaucomatous eyes, respectively. The RNFL thickness was also computed for 26 normal and 70 glaucomatous scans where the glaucomatous eyes showed a significant thinning (p < 0.01, mean thickness 73.7 ± 32.7 μm in normal eyes versus 60.4 ± 25.2 μm in glaucomatous eyes).

Segmentation of retinal blood vessels can provide important information for detecting and tracking retinal vascular diseases including diabetic retinopathy, arterial hypertension, arteriosclerosis and retinopathy of prematurity (ROP). Many studies on 2-D segmentation of retinal blood vessels from a variety of medical images have been performed. However, 3-D segmentation of retinal blood vessels from spectral-domain optical coherence tomography (OCT) volumes, which is capable of providing geometrically accurate vessel models, to the best of our knowledge, has not been previously studied. The purpose of this study is to develop and evaluate a method that can automatically detect 3-D retinal blood vessels from spectral-domain OCT scans centered on the optic nerve head (ONH). The proposed method utilized a fast multiscale 3-D graph search to segment retinal surfaces as well as a triangular mesh-based 3-D graph search to detect retinal blood vessels. An experiment on 30 ONH-centered OCT scans (15 right eye scans and 15 left eye scans) from 15 subjects was performed, and the mean unsigned error in 3-D of the computer segmentations compared with the independent standard obtained from a retinal specialist was 3.4 ± 2.5 voxels (0.10 ± 0.07 mm).

We applied the Case cryo-imaging system to detect signals of developmental toxicity in transgenic mouse fetuses resulting from maternal exposure to a developmental environmental toxicant (2,3,7,8-tetrachlorodibenzo-p-dioxin, TCDD). We utilized a fluorescent transgenic mouse model that expresses Green Fluorescent Protein (GFP) exclusively in smooth muscles under the control of the smooth muscle gamma actin (SMGA) promoter (SMGA/EGFP mice kindly provided by J. Lessard, U. Cincinnati). Analysis of cryo-image data volumes, comprising of very high-resolution anatomical brightfield and molecular fluorescence block face images, revealed qualitative and quantitative morphological differences in control versus exposed fetuses. Fetuses randomly chosen from pregnant females euthanized on gestation day (GD) 18 were either manually examined or cryo-imaged. For cryo-imaging, fetuses were embedded, frozen and cryo-sectioned at 20 μm thickness and brightfield color and fluorescent block-face images were acquired with an in-plane resolution of ≈15 μm. Automated 3D volume visualization schemes segmented out the black embedding medium and blended fluorescence and brightfield data to produce 3D reconstructions of all fetuses. Comparison of Treatment groups TCDD GD13, TCDD GD14 and control through automated analysis tools highlighted differences not observable by prosectors performing traditional fresh dissection. For example, severe hydronephrosis, suggestive of irreversible kidney damage, was detected by cryoimaging in fetuses exposed to TCDD. Automated quantification of total fluorescence in smooth muscles revealed suppressed fluorescence in TCDD-exposed fetuses. This application demonstrated that cryo-imaging can be utilized as a routine high-throughput screening tool to assess the effects of potential toxins on the developmental biology of small animals.

The purpose of this study is to present a non invasive method for the marginal adaptation evaluation in class V composite restorations. Standardized class V cavities prepared in human extracted teeth were filled with composite resin (Premise, Kerr). The specimens were thermocycled. The interfaces were examined by Optical Coherence Tomography (OCT) combined with confocal microscopy and fluorescence. The optical configuration uses two single mode directional couplers with a superluminiscent diode as the source at 1300 nm. The scanning procedure is similar to that used in any confocal microscope, where the fast scanning is en-face (line rate) and the depth scanning is much slower (at the frame rate). Gaps at the interfaces as well as on the inside of the composite resin were identified. OCT has numerous advantages that justify its in vivo and in vitro use compared to conventional techniques. One of the main concerns was the fact that at the adhesive layer site it was very hard to tell the adhesive apart from material defects. For this reason the adhesive was optimized in order to be more scattering. This way we could make a difference between the adhesive layer and the material defects that could lead to microleakages.

It has been known for several years that airflow limitations in the small airways may be an important contributor to Chronic Obstructive Pulmonary Disease (COPD). Quantification of wall thickness has lately gained attention thanks to the use of high resolution CT, with novel approaches focusing on automated methods that can substitute for visual assessment [1, 2]. While increased thickening of the wall is considered evidence of inflammatory disease, we hypothesize that there may be additional ways to detect and quantify inflammation, specifically the uptake of contrast material. In this preliminary investigation, we selected patients with documented chronic airway inflammation, and for whom pre and post contrast datasets were available. On targeted reconstruction of right upper and lower lobes, we selected airways with no connections to surrounding structures, and used a modified Full-Width-Half-Max method for quantification of lumen diameter, wall thickness, and wall density. Matching airway locations on the pre- and postcontrast cases were compared. Airways from patients without airway disease served as a control. Results for the airway disease cases showed an average enhancement of 72 HU within the airway walls, with a standard deviation of 59 HU. In the control group the average enhancement was 16 HU with standard deviation of 22 HU. While this study is limited in number of cases, we hypothesize that quantification of contrast uptake is an additional factor to consider in assessing airway inflammation. At the same time we are currently investigating whether enhancement can be measured via a "contrast" map created with dual energy scanning, where a 3-value decomposition algorithm differentiates iodine from other materials. This technique would eliminate both the need for a pre-contrast scan, and the task of matching airway locations on pre- and post- scans.

The recognition of abnormalities relative to the lobular anatomy has become increasingly important in the diagnosis and differential diagnosis of lung abnormalities at clinical routines of CT examinations. The purpose of this study is to analyze microstructure of the lobular anatomy with isotropic spatial resolution in the range of several micrometers to quantitatively describe relation between the architectures and abnormalities. Recent commercial micro CT scanners play a vital role in imaging the lung micro-architectures. However, only a limited number of attempts have been conducted because of difficulties to image the secondary pulmonary lobule beyond the scan field of view and the limited contrast lung parenchyma. This paper demonstrates the ability of synchrotron radiation micro CT (SRμCT) using offset scan mode in microstructural analysis of the secondary pulmonary lobule. The inflated and fixed lung specimen was imaged with resolution of 5.87x5.87x5.87 μm³ by using offset scan mode of the SRμCT (15 keV) at the synchrotron radiation facility (SPring-8). The 3-D SRμCT image which was stacked 2624 slices (each slice:7287x7287 voxels) covered the secondary pulmonary lobule being included in the lung specimen. A proper threshold value for appropriate segmentation was interactively determined to the volume of interest representing the secondary pulmonary lobule. Following transformation of the segmented binary image to a skeletonized surface representation, each voxel was classified as a curve, surface, or junction. The interlobular septa region was extracted interactively by using the voxel classification result which offered geometrical information. Each component of lobular airway, artery, and vein were extracted by using a seeding technique, considering equal attenuation values and connectivity. The resulting volumetric image from the SRμCT using offset scan mode made 3-D microstructural analysis of the lobular anatomy possible.

A queryable electronic atlas was developed to quantitatively characterize the normal human lung airway tree and to provide a better understanding of the lung for diagnosing diseases and evaluating treatments. The atlas consists of airway measurements taken from CT images using the Pulmonary Workstation II (PW2) software package. These measurements include airway cross-sectional area at midpoint between branch points; maximum and minimum diameter of a particular airway cross section at segment midpoint; average, maximum, and minimum wall thickness per branch; and wall thickness uniformity within a branch. The atlas provides user friendly interfaces for interrogating population statistics, comparing populations, comparing individuals to populations, and comparing individuals to other individuals. Populations can be selected based on age, gender, race, ethnicity, and normalcy/disease.

Although 3-D airway tree segmentation permits analysis of airway tree paths of practical lengths and facilitates visual inspection, our group developed and tested an automated computer scheme that was operated on individual 2-D CT images to detect airway sections and measure their morphometry and/or dimensions. The algorithm computes a set of airway features including airway lumen area (A_i), airway cross-sectional area (A_w), the ratio (R_a) of Ai to Aw, and the airway wall thickness (T_w) for each detected airway section depicted on the CT image slice. Thus, this 2-D based algorithm does not depend on the accuracy of 3-D airway tree segmentation and does not require that CT examination encompasses the entire lung or reconstructs contiguous images. However, one disadvantage of the 2-D image based schemes is the lack of the ability to identify the airway generation (G_b) of the detected airway section. In this study, we developed and tested a new approach that uses 2-D airway features to assign a generation number to an airway. We developed and tested two probabilistic neural networks (PNN) based on different sets of airway features computed by our 2-D based scheme. The PNNs were trained and tested on 12 lung CT examinations (8 training and 4 testing). The accuracy for the PNN that utilized Ai and Ra for identifying the generation of airway sections varies from 55.4% - 100%. The overall accuracy of the PNN for all detected airway sections that are spread over all generations is 76.7%. Interestingly, adding wall thickness feature (T_w) to PNN did not improve identification accuracy. This preliminary study demonstrates that a set of 2-D airway features may be used to identify the generation number of an airway with reasonable accuracy.

We developed image analysis tools to evaluate spatial and temporal ³He magnetic resonance imaging (MRI) ventilation in asthma and cystic fibrosis. We also developed temporal ventilation probability maps to provide a way to describe and quantify ventilation heterogeneity over time, as a way to test respiratory exacerbations or treatment predictions and to provide a discrete probability measurement of ³He ventilation defect persistence.

Microfocal CT was used to image the pulmonary arterial (PA) tree in rodent models of pulmonary hypertension (PH). CT images were used to measure the arterial tree diameter along the main arterial trunk at several hydrostatic intravascular pressures and calculate distensibility. High-resolution planar angiographic imaging was also used to examine distal PA microstructure. Data on pulmonary artery tree morphology improves our understanding of vascular remodeling and response to treatments. Angiotensin II (ATII) has been identified as a mediator of vasoconstriction and proliferative mitotic function. ATII has been shown to promote vascular smooth muscle cell hypertrophy and hyperplasia as well as stimulate synthesis of extracellular matrix proteins. Available ATII is targeted through angiotensin converting enzyme inhibitors (ACEIs), a method that has been used in animal models of PH to attenuate vascular remodeling and decrease pulmonary vascular resistance. In this study, we used rat models of chronic hypoxia to induce PH combined with partial left pulmonary artery occlusion (arterial banding, PLPAO) to evaluate effects of the ACEI, captopril, on pulmonary vascular hemodynamic and morphology. Male Sprague Dawley rats were placed in hypoxia (FiO2 0.1), with one group having underwent PLPAO three days prior to the chronic hypoxia. After the twenty-first day of hypoxia exposure, treatment was started with captopril (20 mg/kg/day) for an additional twenty-one days. At the endpoint, lungs were excised and isolated to examine: pulmonary vascular resistance, ACE activity, pulmonary vessel morphology and biomechanics. Hematocrit and RV/LV+septum ratio was also measured. CT planar images showed less vessel dropout in rats treated with captopril versus the non-treatment lungs. Distensibility data shows no change in rats treated with captopril in both chronic hypoxia (CH) and CH with PLPAO (CH+PLPAO) models. Hemodynamic measurements also show no change in the pulmonary vascular resistance with captopril treatment in both CH and CH+PLPAO.

Pulmonary hypertension (PH) is an incurable condition inevitably resulting in death because of increased right heart workload and eventual failure. PH causes pulmonary vascular remodeling, including muscularization of the arteries, and a reduction in the typically large vascular compliance of the pulmonary circulation. We used a rat model of monocrotaline (MCT) induced PH to evaluated and compared Captopril (an angiotensin converting enzyme inhibitor with antioxidant capacity) and N-acetylcysteine (NAC, a mucolytic with a large antioxidant capacity) as possible treatments. Twenty-eight days after MCT injection, the rats were sacrificed and heart, blood, and lungs were studied to measure indices such as right ventricular hypertrophy (RVH), hematocrit, pulmonary vascular resistance (PVR), vessel morphology and biomechanics. We implemented microfocal X-ray computed tomography to image the pulmonary arterial tree at intravascular pressures of 30, 21, 12, and 6 mmHg and then used automated vessel detection and measurement algorithms to perform morphological analysis and estimate the distensibility of the arterial tree. The vessel detection and measurement algorithms quickly and effectively mapped and measured the vascular trees at each intravascular pressure. Monocrotaline treatment, and the ensuing PH, resulted in a significantly decreased arterial distensibility, increased PVR, and tended to decrease the length of the main pulmonary trunk. In rats with PH induced by monocrotaline, Captopril treatment significantly increased arterial distensibility and decrease PVR. NAC treatment did not result in an improvement, it did not significantly increase distensibility and resulted in further increase in PVR. Interestingly, NAC tended to increase peripheral vascular density. The results suggest that arterial distensibility may be more important than distal collateral pathways in maintaining PVR at normally low values.

We introduce a new model-based approach for automatic quantification of colocalizations in multi-channel 3D microscopy images. The approach is based on different 3D parametric intensity models in conjunction with a model fitting scheme to localize and quantify subcellular structures with high accuracy. The central idea is to determine colocalizations between different channels based on the estimated geometry of subcellular structures as well as to differentiate between different types of colocalizations. Furthermore, we perform a statistical analysis to assess the significance of the determined colocalizations. We have successfully applied our approach to about 400 three-channel 3D microscopy images of human soft-tissue tumors.

We present a framework for multi-level statistical shape analysis, applied to the study of anatomical variability of abdominal organs. Statistical models were built hierarchically, allowing the representation of different levels of detail. Principal factor analysis was used for decomposition of deformation fields obtained from non-rigid registration at different levels, and provided a compact model to study shape variability within the abdomen. To assess and ease the interpretability of the resulting deformation modes, a clustering technique of the deformation vectors was proposed. The analysis of deformation fields showed a strong correlation with anatomical landmarks and known mechanical deformations in the abdomen. Clusters of modes of deformation from fine-to-coarse levels explain tissue properties, and inter-organ relationships. Our method further presents the automated hierarchical partitioning of organs into anatomically significant components that represent potentially important constraints for abdominal diagnosis and modeling, and that may be used as a complement to multi-level statistical shape models.

Among many molecular imaging modalities, bioluminescence tomography (BLT) stands out as an effective approach for in vivo imaging because of its noninvasive molecular and cellular level detection ability, high sensitivity and low cost in comparison with other imaging technologies. However, there exists the case that large scale problem with large number of points and elements in the structure of mesh standing for the small animal or phantom. And the large scale problem's system matrix generated by the diffuse approximation (DA) model using finite element method (FEM) is large. So there wouldn't be enough random access memory (RAM) for the program and the related inverse problem couldn't be solved. Considering the sparse property of the BLT system matrix, we've developed a new sparse matrix (ZSM) to overcome the problem. And the related algorithms have all been speeded up by multi-thread technologies. Then the inverse problem is solved by Tikhonov regularization method in adaptive finite element (AFE) framework. Finally, the performance of this method is tested on a heterogeneous phantom and the boundary data is obtained through Monte Carlo simulation. During the process of solving the forward model, the ZSM can save more processing time and memory space than the usual way, such as those not using sparse matrix and those using Triples or Cross Linked sparse matrix. Numerical experiments have shown when more CPU cores are used, the processing speed is increased. By incorporating ZSM, BLT can be applied to large scale problems with large system matrix.

Multispectral bioluminescence tomography is becoming a promising tool because it can resolve the biodistibution of bioluminescent reporters associated with cellular and subcellular function through several millimeters with to centimeters of tissues in vivo. Generally, to recover the bioluminescent sources, the source reconstruction problem is formulated as a nonlinear least-squares-type bounds constrained optimization problem. However, bioluminescence tomography (BLT) is an ill-posed problem. For the sake of stability and uniqueness of BLT, many algorithms have been proposed to regularize the problem, such as L₂ norm and L₁ norm. Here, we proposed a new regularization method with Huber function to regularize BLT problem to obtain robustness like L₁ and rapid convergence of L₂. Furthermore, the computational burden is largely increased with the use of spectral data. Therefore, there is a critical need to develop a fast reconstruction algorithm for solving multispectral bioluminescence tomography. In the paper, a limited memory quasi-Newton algorithm for solving the large-scale optimization problem is proposed to fast localize the bioluminescent source. In the numerical simulation, a heterogeneous phantom was used to evaluate the performance of the proposed algorithm with the Monte Carlo based synthetic data. Additionally, the real mouse experiments were conducted to further evaluate the proposed algorithm. The results demonstrate the potential and merits of the proposed algorithm.

Dynamic positron emission tomography (PET) is a promising diagnostic tool to quantitatively predict biological and physiological changes in vivo through estimation of kinetic parameters. In this work, several popular linear and non-linear estimation methods for determining kinetic parameters using PET imaging with Fluorine-18 fluorodeoxyglucose ([¹⁸F]FDG) are compared and evaluated. The simulation studies are presented. The linear estimation methods include linear least squares (LLS), generalized linear least squares (GLLS) and total least squares (TLS), while the non-linear estimation methods include non-linear least squares (NLS), weighted nonlinear least squares using noisy tissue time activity data (WNLS-N), weighted non-linear least squares using noise-free tissue time activity data (WNLS-NF) and iteratively re-weighted non-linear least squares (IRWNLS). There are several findings: 1. Compared with non-linear estimation methods, GLLS performs well when noise level is low, but worse especially in determining k₃ and k₄ when noise level is high. What's more, GLLS does not show obvious advantage in running time. 2. The choice of weights plays an important role in nonlinear estimation methods. Weighting using noisy data should be avoided. WNLS-NF and IRWNLS perform best. Since the noise-free data can not be obtained in clinical and IRWNLS is time-consuming, NLS is most recommended. 3. Non-linear estimation methods are prone to produce lower-biased, higher-precision parameter estimates, however, also more easily affected by noise. Linear estimation methods are prone to be more biased, however, much more computational efficient and noise robust.

Quantitative analysis of the left ventricular shape and motion patterns associated with left ventricular mechanical dyssynchrony (LVMD) is essential for diagnosis and treatment planning in congestive heart failure. Real-time 3D echocardiography (RT3DE) used for LVMD analysis is frequently limited by heavy speckle noise or partially incomplete data, thus a segmentation method utilizing learned global shape knowledge is beneficial. In this study, the endocardial surface of the left ventricle (LV) is segmented using a hybrid approach combining active shape model (ASM) with optimal graph search. The latter is used to achieve landmark refinement in the ASM framework. Optimal graph search translates the 3D segmentation into the detection of a minimum-cost closed set in a graph and can produce a globally optimal result. Various information-gradient, intensity distributions, and regional-property terms-are used to define the costs for the graph search. The developed method was tested on 44 RT3DE datasets acquired from 26 LVMD patients. The segmentation accuracy was assessed by surface positioning error and volume overlap measured for the whole LV as well as 16 standard LV regions. The segmentation produced very good results that were not achievable using ASM or graph search alone.

Determination of constitutive properties of cells is important for quantitative description of cellular mechanics. Existing approaches to mechanical cell manipulation are based on experimental techniques that do not allow unsupervised analysis of large number of cells and/or probing of intracellular structures that are not directly exposed to external loads. Alternatively, mechanical behavior of cellular matter can be studied in time-series of microscopic images. In this work, we present an image- and model-based framework for determination of constitutive properties of living cells. Our experimental studies demonstrate application of this approach for quantitative analysis of cellular mechanics on the basis of image data assessed by different experimental techniques, including microplate stretching, optical stretching and contactless cellular deformation induction using cytoskeleton-disrupting drugs.

The MADEIRA Project (Minimizing Activity and Dose with Enhanced Image quality by Radiopharmaceutical Administrations), aims to improve the efficacy and safety of 3D functional imaging by optimizing, among others, the knowledge of the temporal variation of the radiopharmaceuticals' uptake in and clearance from tumor and healthy tissues. With the help of compartmental modeling it is intended to optimize the time schedule for data collection and improve the evaluation of the organ doses to the patients. Administration of ¹⁸F-choline to screen for recurrence or the occurrence of metastases in prostate cancer patients is one of the diagnostic applications under consideration in the frame of the project. PET and CT images have been acquired up to four hours after injection of ¹⁸F-choline. Additionally blood and urine samples have been collected and measured in a gamma counter. The radioactivity concentration in different organs and data of plasma clearance and elimination into urine were used to set-up a compartmental model of the biokinetics of the radiopharmaceutical. It features a central compartment (blood) exchanging with organs. The structure describes explicitly liver, kidneys, spleen, plasma and bladder as separate units with a forcing function approach. The model is presented together with an evaluation of the individual and population kinetic parameters, and a revised time schedule for data collection is proposed. This optimized time schedule will be validated in a further set of patient studies.

Magnetic particle imaging (MPI) is a new tomographic imaging technique capable of determining the spatial distribution of superparamagnetic iron oxide particles at high temporal and spatial resolution. Reconstruction of the particle distribution requires the system function to be known. In almost all other tomographic imaging techniques, a basic mathematical model of the system function exists, so that for reconstruction of an image, only measured data from the object under examination have to be provided. Due to the complex behavior of the particle dynamics, this is more complicated in MPI. Therefore, to date, the system function is measured in a tedious calibration procedure. To this end, a small delta sample is moved to each position inside the measuring field, while the magnetization response is acquired consecutively. However, although this measurement-based approach provides a good estimate of the system function, it has several drawbacks. Most important, the measured system function contains noise, which limits the size of the delta sample and in turn the resolution of the sampling grid. In this work, the noise induced limitations of the measurement-based system function are investigated in a simulation study. More precisely, the influence of the system function noise and the size of the delta sample on the resulting image quality after reconstruction are analyzed.

An in-vivo feasibility study of potentially improved atherosclerosis CT imaging is presented. By administration of two different contrast agents to rabbits with induced atherosclerotic plaques we aim at identifying both soft plaque and vessel lumen simultaneously. Initial injection of iodinated nanoparticle (INP) contrast agent (N1177 - Nanoscan Imaging), two to four hours before scan, leads to its later accumulation in macrophage-rich soft plaque, while a second gadolinium contrast agent (Magnevist) injected immediately prior to the scan blends with the aortic blood. The distinction between the two agents in a single scan is achieved with a double-layer dual-energy MDCT (Philips Healthcare) following material separation analysis using the reconstructed images of the different x-ray spectra. A single contrast agent injection scan, where only INP was injected two hours prior to the scan, was compared to a double-contrast scan taken four hours after INP injection and immediately after gadolinium injection. On the single contrast agent scan we observed along the aorta walls, localized iodine accumulation which can point on INP uptake by atherosclerotic plaque. In the double-contrast scan the gadolinium contributes a clearer depiction of the vessel lumen in addition to the lasting INP presence. The material separation shows a good correlation to the pathologies inferred from the conventional CT images of the two different scans while performing only a single scan prevents miss-registration problems and reduces radiation dose. These results suggest that a double-contrast dual-energy CT may be used for advanced clinical diagnostic applications.

We present an approach for the quantification of fluorescent spots in time series of 3-D confocal microscopy images of endoplasmic reticulum exit sites of dividing cells. Fluorescent spots are detected based on extracted image regions of highest response using the HMAX transform and prior convolution of the 3-D images with a Gaussian kernel. The sensitivity of the involved parameters was studied and a quantitative evaluation using both 3-D synthetic and 3-D real data was performed. The approach was successfully applied to more than one thousand 3-D confocal microscopy images.

Automated counting of photoreceptor cells in high-resolution retinal images generated by adaptive optics (AO) imaging systems is important due to its potential for screening and diagnosis of diseases that affect human vision. A drawback in recently reported photoreceptor cell counting methods is that they require user input of cell structure parameters. This paper introduces a method that overcomes this shortcoming by using content-adaptive filtering (CAF). In this method, image frequency content is initially analyzed to design a customized filter with a passband to emphasize cell structures suitable for subsequent processing. The McClellan transform is used to design a bandpass filter with a circularly symmetric frequency response since retinal cells have no preferred orientation. The automated filter design eliminates the need for manual determination of cell structure parameters, such as cell spacing. Following the preprocessing step, cell counting is performed on the binarized filtered image by finding regional points of high intensity. Photoreceptor cell count estimates using this automated procedure were found to be comparable to manual counts (gold standard). The new counting method when applied to test images showed overall improved performance compared with previously reported methods requiring user-supplied input. The performance of the method was also examined with retinal images with variable cell spacing.

The microscopic rigidity of structural elements of the cell and of the extracellular matrix control the genetic expression of factors that control critical aspects of malignancy including metastasis and neoangiogenesis. Methods of measuring the rigidity in vitro are being developed and exploited to explore the mechanisms involved. But no methods that function in vivo are available. We demonstrate proof of concept that the stiffness of the microenvironment surrounding bound magnetic nanoparticles can be measured using the shape of the spectra of the magnetization induced by a harmonic applied field. The microscopic region where the stiffness is measured can be selected by selecting the antibody binding sites for which the nanoparticles are targeted. In other applications, the same signal from the nanoparticles has been measured in vivo at very low concentrations so the methods demonstrated here should be capable of measuring low concentrations in vivo as well. The ability to measure the rigidity in vivo will enable the links between genetic control and rigidity to be explored in the complex in vivo environment for the first time.

Understanding bone remodeling and mechanical property characteristics is important for assessing treatments to accelerate healing or in developing diagnostics to evaluate successful return to function. The murine system whereby mid-diaphaseal tibia fractures are imparted on the subject and fracture healing is assessed at different time points and under different therapeutic conditions is a particularly useful model to study. In this work, a novel inverse geometric nonlinear elasticity modeling framework is proposed that can reconstruct multiple mechanical properties from uniaxial testing data. To test this framework, the Lame' constants were reconstructed within the context of a murine cohort (n=6) where there were no differences in treatment post tibia fracture except that half of the mice were allowed to heal 4 days longer (10 day, and 14 day healing time point, respectively). The properties reconstructed were a shear modulus of G=511.2 ± 295.6 kPa, and 833.3± 352.3 kPa for the 10 day, and 14 day time points respectively. The second Lame' constant reconstructed at λ=1002.9 ±42.9 kPa, and 14893.7 ± 863.3 kPa for the 10 day, and 14 day time points respectively. An unpaired Student t-test was used to test for statistically significant differences among the groups. While the shear modulus did not meet our criteria for significance, the second Lame' constant did at a value p<0.0001. Traditional metrics that are commonly used within the bone fracture healing research community were not found to be statistically significant.

According to Wolff's law bone remodels in response to the mechanical stresses it experiences so as to produce a minimal-weight structure that is adapted to its applied stresses. Here, we investigate the relations between bone volume and structure for the trabecular bone using 3D μCT images taken from different skeletal sites in vitro, namely from the distal radii (96 specimens), thoracic (73 specimens) and lumbar vertebrae (78 specimens). We determine the local structure of the trabecular network by calculating isotropic and anisotropic scaling indices (α, α_z). These measures have been proven to be able to discriminate rod- from sheet-like structures and to quantify the alignment of structures with respect to a preferential direction as given by the direction of the external force. Comparing global structure measures derived from the scaling indices (mean, standard deviation) with the bone mass (BV/TV) we find that all correlations obey very accurately power laws with scaling exponents of 0.14, 0.12, 0.15 (<α⪆), -0.2, -017, -0.17 (σ(α_z)), 0.09, 0.05, 0.07 (⪅α_z⪆) and -0.20, -0.11 ,-0.13 (σ(α_z)); distal radius, thoracic vertebra and lumbar vertebra respectively. Thus, these relations turn out to be site-independent, albeit the mechanical stresses to which the bones of the forearm and the spine are exposed, are quite different. The similar alignment might not be in agreement with a universal validity of Wolff's law. On the other hand, such universal power law relations may allow to develop additional diagnostic means to better assess healthy and osteoporotic bone.

For some authors trabecular bone is highly visible in intraoral radiographs. For other authors, the observed intrabony trabecular pattern is a representation of only the endosteal surface of cortical bone, not of intermedullary striae. The purpose of this preliminary study was to investigate the true anatomical structures that are visible in routine dental radiographs and classically denoted trabecular bone. This is a major point for bone texture analysis on radiographs. Computed radiography (CR) images of dog mandible section in molar region were compared with simulations calculated from high-resolution micro-CT volumes. Calculated simulations were obtained using the Mojette Transform. By digitally editing the CT volume, the simulations were separated into trabecular and cortical components into a region of interest. Different images were compared and correlated, some bone micro-architecture parameters calculated. A high correlation was found between computed radiographs and calculated simulations from micro-CT. The Mojette transform was successful to obtain high quality images. Cortical bone did not contribute to change in a major way simulated images. These first results imply that intrabony trabecular pattern observed on radiographs can not only be a representation of the cortical bone endosteal surface and that trabecular bone is highly visible in intraoral radiographs.

Micro-CT/PET imaging scanner provides a powerful tool to study tumor in small rodents in response to therapy. Accurate image registration is a necessary step to quantify the characteristics of images acquired in longitudinal studies. Small animal registration is challenging because of the very deformable body of the animal often resulting in different postures despite physical restraints. In this paper, we propose a non-rigid registration approach for the automatic registration of mouse whole body skeletons, which is based on our improved 3D shape context non-rigid registration method. The whole body skeleton registration approach has been tested on 21 pairs of mouse CT images with variations of individuals and time-instances. The experimental results demonstrated the stability and accuracy of the proposed method for automatic mouse whole body skeleton registration.

Trabecular bone parameters extracted from magnetic resonance (MR) images are compared in their ability to predict biomechanical properties determined through mechanical testing. Trabecular bone density and structural changes throughout the proximal tibia are indicative of several musculoskeletal disorders of the knee joint involving changes in the bone quality and the surrounding soft tissue. Recent studies have shown that MR imaging, most frequently applied in soft tissue imaging, also allows non-invasive 3-dimensional characterization of bone microstructure. Sophisticated MR image features that estimate local structural and geometric properties of the trabecular bone may improve the ability of MR imaging to determine local bone quality in vivo. The purpose of the current study is to use whole joint MR images to compare the performance of trabecular bone features extracted from the images in predicting biomechanical strength properties measured on the corresponding ex vivo specimens. The regional apparent bone volume fraction (appBVF) and scaling index method (SIM) derived features were calculated; a Multilayer Radial Basis Functions Network was then optimized to calculate the prediction accuracy as measured by the root mean square error (RSME) for each bone feature. The best prediction result was obtained with a SIM feature with the lowest prediction error (RSME=0.246) and the highest coefficient of determination (R² = 0.769). The current study demonstrates that the combination of sophisticated bone structure features and supervised learning techniques can improve MR imaging as an in vivo imaging tool in determining local trabecular bone quality.

Fixed partial prostheses as integral ceramic, polymers, metal-ceramic or metal-polymers bridges are mainly used in the frontal part of the dental arch (especially the integral bridges). They have to satisfy high stress as well as esthetic requirements. The masticatory stress may induce fractures of the bridges. These may be triggered by initial materials defects or by alterations of the technological process. The fractures of these bridges lead to functional, esthetic and phonetic disturbances which finally render the prosthetic treatment inefficient. Dental interfaces represent one of the most significant aspects in the strength of the dental prostheses under the masticatory load. The purpose of this study is to evaluate the capability of optical coherence tomography (OCT) to characterize the dental prostheses interfaces. The materials used were several fixed partial prostheses integral ceramic, polymers, metal-ceramic and metal-polymers bridges. It is important to produce both C-scans and B-scans of the defects in order to differentiate morphological aspects of the bridge infrastructures. The material defects observed with OCT were investigated with micro-CT in order to prove their existence and positions. In conclusion, it is important to have a non invasive method to investigate dental prostheses interfaces before the insertion of prostheses in the oral cavity.

A microfabricated phantom with application in diffusion tensor imaging (DTI) is presented. Using lithography technique, we have the capability of creating microchannels in the same scale as actual neural fibers (few to tens of microns in diameter). The method is flexible in generating different geometrical patterns. Neural bundles were simulated by designing a large number of microchannels, running parallel to each other. PDMS, the casting material, is not diffusible to water. This applies the restriction to the diffusion of water molecules in different directions. The dimensions of the channels can be calculated based on the desired fractional anisotropy (FA) ratios. Many unresolved issues in studying and improving diffusion tensor imaging can be carefully investigated by implementing an artificial model of neural bundles with well-known geometrical parameters. Problems affiliated with the crossing fibers in 'tractography' are among these issues. Since the topology of the tracts is known, whenever the full characterization of water motion is desired, the elements of the diffusion tensor can be calculated to be compared to the measured values. Optimization of the pulse sequences and calibration of the gradients in DTI are among other application of such phantom. The phantom is made of PDMS, a silicon based, MRI compatible material. Once the mold is generated, creating new phantoms is easy, quick and inexpensive and requires no special equipment.

General linear model (GLM) and independent component analysis (ICA) are widely used methods in the community of functional magnetic resonance imaging (fMRI) data analysis. GLM and ICA are all assuming that fMRI components are location locked. Here we extend the Differentially variable component analysis (dVCA) and introduce it into fMRI data to analyze the transient changes during fMRI experiments which are ignored in GLM and ICA. We apply the extended dVCA to model fMRI images as the linear combination of ongoing activity and multiple fMRI components. We test our extended dVCA method on simulated images that mimicked the fMRI slice images containing two components, and employ the iterative maximum a posteriori (MAP) solution succeed to estimate each component's time-invariant spatial patterns, and its time-variant amplitude scaling factors and location shifts. The extended dVCA algorithm also identify two fMRI components that reflect the fact of hemispheric asymmetry for motor area in another test with fMRI data acquired with the block design task of right/left hand finger tapping alternately. This work demonstrates that our extended dVCA method is robustness to detect the variability of the fMRI components that maybe existent during the fMRI experiments.

One of the recent themes to study the brain dynamics is studying the effective connectivity between brain regions in the target area. We propose and apply algorithm model, dynamic causal modeling (DCM), Psychophysiological interaction (PPI) and first order kernels and also SVD applied directly to singular intrinsic connectivity matrix end up to integrate and describe the interaction of several Brain Regions based on functional magnetic resonance imaging time series to make inferences about functional integration and segregation within the human brain. The method is to demonstrate using real data to show how such models are able to characterize interregional dependence. We extend estimating and reviewing designed model to characterize the interactions between regions and then to estimate the effective connectivity between these regions. All designs, estimates, reviews are implemented using SPM, one of the free best software packages used for design models and analysis for inferring about FMRI functional magnetic resonance imaging time series.

Functional magnetic resonance imaging (fMRI) is used to model brain activation patterns associated with various perceptual and cognitive processes as reflected by the hemodynamic (BOLD) response. While many sensory and motor tasks are associated with relatively simple activation patterns in localized regions, higher-order cognitive tasks may produce activity in many different brain areas involving complex neural circuitry. We applied a recently proposed probabilistic independent component analysis technique (PICA) to determine the true dimensionality of the fMRI data and used EEG localization to identify the common activated patterns (mapped as Brodmann areas) associated with a complex cognitive task like analytic reasoning. Our preliminary study suggests that a hybrid GLM/PICA analysis may reveal additional regions of activation (beyond simple GLM) that are consistent with electroencephalography (EEG) source localization patterns.

The objective of this study was to identify on healthy subjects the correlation between motor performances and brain activation maps, by the simultaneous use of functional magnetic resonance imaging (fMRI) and optoelectronic motion analysis system. The specific goal was to individuate how amplitude affects the related cerebral flow maps in active, passive and electrical stimulated (FES) movements. Ankle DorsiFlexion (ADF) was chosen as analyzed task because of its importance in the gait cycle. Firstly FES compatibility with fMRI images acquisition was assessed, both for the safety of the subject and of the device, and for mutual disturbances evaluation. We identified the experimental protocol so as to optimize the measured cerebral maps and the repeatability of the results. Intra-subject analysis of movement parameters along with brain activation mapping was performed. First level analysis to compare different execution modalities have been studied and preliminary qualitative results are reported. The long term application is the exploitation of the combined system in the evaluation of neurological patients where the definition of the motor tasks could be only partially accomplished depending on the patient residual functionality.

Resting-state functional magnetic resonance imaging (fMRI) is a technique that measures the intrinsic function of brain and has some advantages over task-induced fMRI. Regional homogeneity (ReHo) assesses the similarity of the time series of a given voxel with its nearest neighbors on a voxel-by-voxel basis, which reflects the temporal homogeneity of the regional BOLD signal. In the present study, we used the resting state fMRI data to investigate the ReHo changes of the whole brain in the prelingually deafened patients relative to normal controls. 18 deaf patients and 22 healthy subjects were scanned. Kendall's coefficient of concordance (KCC) was calculated to measure the degree of regional coherence of fMRI time courses. We found that regional coherence significantly decreased in the left frontal lobe, bilateral temporal lobes and right thalamus, and increased in the postcentral gyrus, cingulate gyrus, left temporal lobe, left thalamus and cerebellum in deaf patients compared with controls. These results show that the prelingually deafened patients have higher degree of regional coherence in the paleocortex, and lower degree in neocortex. Since neocortex plays an important role in the development of auditory, these evidences may suggest that the deaf persons reorganize the paleocortex to offset the loss of auditory.

This paper describes the development of novel computer-aided analysis algorithms to identify the language activation patterns at a certain Region of Interest (ROI) in Functional Magnetic Resonance Imaging (fMRI). Previous analysis techniques have been used to compare typical and pathologic activation patterns in fMRI images resulting from identical tasks but none of them analyzed activation topographically in a quantitative manner. This paper presents new analysis techniques and algorithms capable of identifying a pattern of language activation associated with localization related epilepsy. fMRI images of 64 healthy individuals and 31 patients with localization related epilepsy have been studied and analyzed on an ROI basis. All subjects are right handed with normal MRI scans and have been classified into three age groups (4-6, 7-9, 10-12 years). Our initial efforts have focused on investigating activation in the Left Inferior Frontal Gyrus (LIFG). A number of volumetric features have been extracted from the data. The LIFG has been cut into slices and the activation has been investigated topographically on a slice by slice basis. Overall, a total of 809 features have been extracted, and correlation analysis was applied to eliminate highly correlated features. Principal Component analysis was then applied to account only for major components in the data and One-Way Analysis of Variance (ANOVA) has been applied to test for significantly different features between normal and patient groups. Twenty Nine features have were found to be significantly different (p<0.05) between patient and control groups

Twenty-three Taiwanese infants with unilateral cleft lip and palate (UCLP) were CT-scanned before lip repair at the age of 3 months, and again after lip repair at the age of 12 months. In order to evaluate the surgical result, detailed point correspondence between pre- and post-surgical images was needed. We have previously demonstrated that non-rigid registration using B-splines is able to provide automated determination of point correspondences in populations of infants without cleft lip. However, this type of registration fails when applied to the task of determining the complex deformation from before to after lip closure in infants with UCLP. The purpose of the present work was to show that use of prior information about typical deformations due to lip closure, through the construction of a knowledge-based atlas of deformations, could overcome the problem. Initially, mean volumes (atlases) for the pre- and post-surgical populations, respectively, were automatically constructed by non-rigid registration. An expert placed corresponding landmarks in the cleft area in the two atlases; this provided prior information used to build a knowledge-based deformation atlas. We model the change from pre- to post-surgery using thin-plate spline warping. The registration results are convincing and represent a first move towards an automatic registration method for dealing with difficult deformations due to this type of surgery.

Contrast imaging is a compelling enhancement for the portable, flat panel-based brain CT scanner currently under development at Xoran. Due to the relative low temporal resolution of flat panel detectors, enabling tomographic imaging on such platform requires optimizing the imaging and injection protocols. A dynamic CT head phantom was designed to facilitate this task. The Dynamic Perfusion and Angiography Model (PAM), mimics tissue attenuation in CT images, provides physiological timing for angiography and perfusion studies, and moves fluid with properties similar to those of blood. The design consists of an arterial system, which contains bifurcating vessels that feed into perfusion chambers, mimicking blood flow through capillaries and smaller vessels, and a venous system, which is symmetrical to the arterial side and drains the perfusion chambers. The variation of geometry and flow rate in the phantom provides the physiological total time that fluid spends in the head, and the difference in material densities correlates to CT numbers for biological tissues. This paper discusses the design of Dynamic PAM and shows experimental results demonstrating its ability to realistically simulate blood flow. Results of dynamic imaging studies of the phantom are also presented.

The threat of a virulent strain of influenza, severe acute respiratory syndrome (SARS), tuberculosis, H1N1/A virus (swine flu) and possible mutations are a constant threat to global health. Implementation of pandemic infrared thermographic screening is based on the detection of febrile temperatures (inner canthus of the eyes) that are correlated with an infectious disease. Previous attempts at pandemic thermal screening have experienced problems (e.g. SARS outbreak, Singapore 2003) associated with the deployment plan, implementation and operation of the screening thermograph. Since this outbreak, the International Electrotechnical Commission has developed international standards that set minimum requirements for thermographic system fever screening and procedures that insure reliable and reproducible measurements. These requirements are published in IEC 80601-2-59:2008, Medical electrical equipment - Part 2-59: Particular requirements for the basic safety and essential performance of screening thermographs for human febrile temperature screening. The International Organization for Standardization has developed ISO/TR 13154:2009, Medical Electrical Equipment - which provides deployment, implementation and operational guidelines for identifying febrile humans using a screening thermograph. These new standards includes recommendations for camera calibrations, use of black body radiators, view field, focus, pixels within measurement site, image positioning, and deployment locations. Many current uses of thermographic screening at airports do not take into account critical issues addressed in the new standard, and are operating below the necessary effectiveness and efficiency. These documents, related thermal research, implications for epidemiology screening, and the future impact on medical thermography are discussed.

Analysis of 3D images of vascular trees presents a major logistic and multi-scale imaging challenge. One approach that greatly reduces the image analysis difficulty is to apply an 'erode/dilate' approach to a binarized, segmented, image so as to progressively eliminate branches of increasing diameter. Although this provides useful data for detecting some changes in branching geometry, it eliminates information about the hierarchical structure of the vascular tree. To quantify the impact of this loss of branching hierarchy information we analyzed 3D micro-CT images (4μm and 20μm isotropic voxels) of porcine myocardial "biopsies" obtained in control animals and in animals after 100μm diameter microspheres were injected into the coronary artery perfusing the site of subsequent biopsy. After the in vivo embolization, the vascular tree was injected with radiopaque Microfil and "biopsies" of the myocardium harvested. The analysis of the micro-CT images of the biopsies involved erode/dilate analysis of the opacified vessels in the entire biopsy and also of isolated vascular trees (isolated via a 'connect' function) within the biopsy. The isolated trees were also analyzed by dimensional measurement of the individual interbranch segment lengths and volumes, results that were then put into the same form as those produced by the erode/dilate method. In the embolized specimens the volume-loss of vessels below 60μm diameter closely matched for (i) erode/dilate of entire biopsy, (ii) erode/dilate of isolated tree, and (iii) direct measurement of isolated tree. The erode/dilate method quantifies the effects of a microsphere embolization, indicating what diameter interbranch segments trap a microsphere of a given size.

Carotid atherosclerosis is the major cause of ischemic stroke, a leading cause of death and disability. This is driving the development of image analysis methods to quantitatively evaluate local arterial effects of potential treatments of carotid disease. Here we investigate the use of novel texture analysis tools to detect potential changes in the carotid arteries after statin therapy. Three-dimensional (3D) carotid ultrasound images were acquired from the left and right carotid arteries of 35 subjects (16 treated with 80 mg atorvastatin and 19 treated with placebo) at baseline and after 3 months of treatment. Two-hundred and seventy texture features were extracted from 3D ultrasound carotid artery images. These images previously had their vessel walls (VW) manually segmented. Highly ranked individual texture features were selected and compared to the VW volume (VWV) change using 3 measures: distance between classes, Wilcoxon rank sum test, and accuracy of the classifiers. Six classifiers were used. Using texture feature (L7R7) increases the average accuracy and area under the ROC curve to 74.4% and 0.72 respectively compared to 57.2% and 0.61 using VWV change. Thus, the results demonstrate that texture features are more sensitive in detecting drug effects on the carotid vessel wall than VWV change.

A fully automatic ICA based data driven technique which incorporates additional a priori information from physiological modeling of the cerebral microcirculation (gamma variate model) is developed for the separation of arteries and veins in contrast-enhanced studies of the cerebral microvasculature. A dynamic data set of 50 images taken by a two-photon laser scanning microscopy technique that monitors the passage of a bolus of dye through artery and vein is used here. A temporally constrained ICA (TCICA) technique is developed to extract the vessel specific dynamics of artery and vein by adding two constraints to classical ICA algorithm. One of the constraints guarantees that the extracted curves follow the gamma variate model of blood passage through vessels. Positivity as the second constraint indicates that none of the extracted component images that correspond to the artery, vein or other capillaries in the imaging field of view, has negative impact on the acquired images. Experimental results show improved performance of the proposed temporally constrained ICA (TCICA) over the most commonly used classical ICA technique (fast-ICA) in generating physiologically meaningful curves; they are also closer to that of pixel by pixel model fitting algorithms and perform better in handling noise. This technique is also fully automatic and does not require specifying regions of interest which is critical in model based techniques.

For a common set of 4D CT lung images, we report results from the application of a number of optical flow techniques including global, local, and combined local-global methods for tracking planar lung motion. Our comparisons are primarily empirical, and concentrate on the accuracy, that is magnitude and direction of error at a discrete set of landmark points with known motions, and reliability, that is, objective lung boundary deformation tracking. Our results indicate that performance varies significantly among the techniques tested.

In cardiovascular diagnostics, the knowledge of blood pressure is essential for the physician. Nowadays, blood pressures are usually obtained by catheter measurements or sphygmomanometric methods. These techniques suffer from different drawbacks in terms of invasiveness, observable vessels and the resolution of the pressure values, respectively. Magnetic resonance imaging (MRI) offers a promising approach to establish a method for blood pressure measurements that is able to overcome these difficulties. Phase-contrast MRI is used to acquire velocity-encoded data. Fluid pressure gradients can be derived from the measured velocities using the Navier-Stokes equations. Unfortunately, this technique is known to suffer from a strong sensitivity to imaging quality. Especially the low signal-to-noise ratios (SNR) of phase contrast MRI data combined with the limited spatial and temporal resolution could severely reduce the reliability of computations. In this paper, we analyze computations of blood pressure gradients based on phase contrast MRI measurements of steady and pulsatile flow in a phantom. The influence of image quality of the velocity-encoded data as well as of different segmentation techniques is evaluated. In case of steady flow, the pressure gradient values computed via Navier-Stokes equations show good agreement with theoretical values if physical a-priori knowledge is incorporated. If a pulsatile aortic flow profile is applied, the computed pressure gradients generally match catheter measurements well. Nevertheless, an underestimation of pressure gradient peaks is observed. Different segmentation techniques influence the size of root mean squared errors between computation and measurement as well as their reduction by the use of higher SNRs.

A new hardware device called Microemulsion Analyzer (MEA), which facilitates the preparation and evaluation of microemulsions, was developed. Microemulsions, consisting of three phases (oil, surfactant and water) and prepared on deep well plates according to the PDMPD method can be automatically evaluated by means of the optical properties. The ratio of ingredients to form a microemulsion strongly depends on the properties and the amounts of the used ingredients. A microemulsion assay is set up on deep well plates to determine these ratios. The optical properties of the ingredients change from turbid to transparent as soon as a microemulsion is formed. The MEA contains a frame and an imageprocessing and analysis algorithm. The frame itself consists of aluminum, an electro luminescent foil (ELF) and a camera. As the frame keeps the well plate at the correct position and angle, the ELF provides constant illumination of the plate from below. The camera provides an image that is processed by the algorithm to automatically evaluate the turbidity in the wells. Using the determined parameters, a phase diagram is created that visualizes the information. This build-up can be used to analyze microemulsion assays and to get results in a standardized way. In addition, it is possible to perform stability tests of the assay by creating special differential stability diagrams after a period of time.

The temperature of magnetic nanoparticles can be estimated with excellent accuracy using the ratio of the harmonics of the magnetization induced by a harmonic magnetic field. The harmonics can be measured in vivo with high sensitivity. The method has been generalized to measure any physical property influencing the nanoparticle Brownian motion, including temperature, viscosity and bound state. We present theory and preliminary data suggesting that the phase of the harmonics can also be used to estimate the viscosity. The use of the phase should allow more sensitive measurements and alternative ways of separating dynamic from static effects.

The assessment of trabecular bone microarchitecture by numerical analysis of high resolution magnetic resonance (HRMR) images provides global and local structural characteristics, which improve the understanding of the progression of osteoporosis and its diagnosis. In the present work we apply the finite elements method (FEM), which models the biomechanical behaviour of the bone, the scaling index method (SIM), which describes the topology of the structure on a local level, and Minkowski Functionals (MF), which are global topological characteristics, for analysing 3D HRMR images of 48 distal radius specimens in vitro. Diagnostic performance of texture measures derived from the numerical methods is compared with regard to the prevalence of vertebral fractures. Both topological methods show significantly better results than those obtained using bone mineral density (BMD) measurement and the failure load estimated by FEM. The receiver operating characteristic analysis for differentiating subjects with and without fractures reveals area under the curve of 0.63 for BMD, 0.66 for maximum compressive strength as determined in a biomechanical test, 0.72 for critical load estimated by FEM, 0.79 for MF₄ and 0.86 for SIM, i.e. local topological characteristics derived by SIM suit best for diagnosing osteoporosis. The combination of FEM and SIM on tissue level shows that in both weak and strong bones the plate-like substructure of the trabecular network are the main load bearing part of the inner bone and that the relative amount of plates to rods is the most important characteristic for the prediction of bone strength.

Histopathologic correlation is an essential component for validation of the radiological findings. There has been significant advancement in medical imaging technologies, including molecular imaging, such that, it is essential to establish the system beyond histopathologic correlation, to protein profiling that can be correlated with imaging at anatomically identical manner for accurate examination. Recently, a novel technology for proteomic profiling has been established, called "multiplex tissue immunoblotting (MTIB)" which can offer studying multiple protein expression from a single histology slide. Therefore, we attempted to establish the system to obtain an identical plane between high resolution imaging and histopathology at microscopic level so that proteomic profiling can be readily performed using MTIB. A variety of tissues were obtained from autopsy materials and initially scanned with high field MRI (14T) ex vivo along with the marker for tissue orientation. The histology slides were prepared from post-scanned tissue under the marker-guidance in order to obtain an identical plane with high resolution imaging. Subsequently, MTIB was carried out to study expression of proteins of interest and point by point correlation with high resolution imaging was performed at histogeographically identical manner.