This PDF file contains the front matter associated with SPIE Proceedings Volume 11632 including the Title Page, Copyright information, and Table of Contents.

Diffuser-based sensing has shown potentials in inexpensive and compact optical systems. Here we demonstrate a low-cost diffuser-based computational funduscope that can recover pathological features of the model eye fundus. Our system implements an infinite-conjugate design by relaying the ocular lens onto the diffuser which provides shift-invariance across a wide field-of-view (FOV). Our experiments show that fundus images can be reconstructed over 33 degree FOV and our device is robust to 4D refractive error using a single point-spread-function.

Functional diffuse optical tomography (fDOT) system for breast imaging consists of an optical fiber-based light delivery subsystem to sequentially inject multi-wavelength NIR light at multiple locations on the tissue surface and a diffuse transmitted light measurement subsystem. In the low-cost fDOT, few NIR light sources and detectors are electromechanically multiplexed. Though it reduces cost substantially, it adds measurement uncertainty and this increases with the measurement cycle. Traditionally homogeneous phantom measurement data is used for the high-level system calibration. In this paper, an embedded system based digital calibration technique in the hardware level for the electromechanical optical fiber switch-based 3D fDOT system is proposed that is suitable for the low-resource settings. The system has four LED sources of four wavelengths (660, 735, 810 and 850 nm) and 24 SiPD detectors. An algorithm was developed and programmed a microcontroller-based circuitry to digitally control the electromechanics with a spatial resolution of 12.5 micro meter to couple the four-wavelength NIR sources to sixteen source fibers. A calibration scheme was adopted for source illuminations that takes feedback from a power meter to the controller and digitally calibrates to ensure that light entering the imaging domain is near identical. Measurement data from a homogeneous and a heterogeneous phantom was used to study measurement uncertainty and noise performance, and to compare with the traditional method. Experimental results statistically showed that active hardware-level digital calibration improved the measurement accuracy and convergence of the image reconstruction that can open up to a fast, reliable, and cost-effective fDOT system.

We propose a simple smartphone attachment module to realize a portable wide-field high-resolution microscope based on Fourier Ptychographic Microscopy (FPM). Using the smartphone's screen as the illumination and the front camera module for image acquisition, we can construct a stand-alone portable FPM, a microscopy technique that can achieve high resolution by computationally combining a number of variably illuminated low-resolution bright-field and dark-field images through an iterative phase retrieval algorithm. With the custom-built android application that performs in situ calculation for acquisition, reconstruction, and display of the images, we can achieve a true stand-alone portable imaging device for field applications.

Major challenges in diabetic foot ulcer (DFU) treatment include compliance and routine clinical visits to facilitate healing. Virtual Medicine (VM) can greatly impact DFU wound care management with tools for remote patient monitoring (RPM). Herein, a novel low-cost smartphone-based imaging device was developed to provide physiological (in terms of tissue oxygenation) and visual measurements of DFUs. Quantitative changes in tissue oxygenation between the wound and peri-wound in DFUs are obtained using SPOT device in an IRB approved pilot study. On a long-term, SPOT has potential to offer a low-cost alternative for VM and RPM in DFU wound care management.

Current fingerprint anti-spoofing and liveness detection techniques heavily relied on image processing algorithms and did not fully explore other hardware of a smartphone. In this study, we utilize the phone’s front-facing teardrop notch camera and LED display for multi-spectral imaging and liveness detection. We programmed the phone to illuminate different RGB colors patterns while the camera captures images of the illuminated finger. A custom-designed software allows for the distinct detection between spoof and live fingers based on resulting images and video. The method was implemented and tested in a Samsung Galaxy A50 with a teardrop notch camera. Utilizing a MATLAB program, we were able to distinguish a real finger from eight different-colored spoofs based on the images captured from the front-facing camera.

We present a deep-learning based device to perform automated screening of sickle cell disease (SCD) using images of blood smears captured by a smartphone-based microscope. We experimentally validated the system using 96 blood smears (including 32 positive samples for SCD), each coming from a unique patient. Tested on these blood smears, our framework achieved a 98% accuracy and had an area-under-the-curve (AUC) of 0.998. Since this technique is both low-cost and accurate, it has the potential to improve access to cost-effective screening and monitoring of patients in low resource settings – particularly in areas where existing diagnostic methods are unsuitable.

Lighting gel filters have been used for over 100 years to modify the color of traditional light sources. Very few applications of gel filters have been recorded in scientific literature despite their potential in low-cost optical design . We propose that key factors that prevent wider adoption of gel filters in scientific applications are a lack of extensive optical characterization and comparison to commonly used dielectric and color glass filters. Here, we perform optical characterization of lighting gel filters and compare their transmission, auto-fluorescence, and photobleaching performance to dielectric and color glass filters. The results indicate that gel filters are a viable low-cost alternative with unique advantages that make them suitable for scientific applications including low-cost smartphone imaging systems and disposable lab-on-chip fluorescence tests.

COVID-19-induced personal protective equipment shortages necessitate effective, accessible N95 decontamination, especially in low-resource settings. UV-C irradiation inactivates SARS-CoV-2 analogs on N95 respirators when ≥1.0 J/cm^2 is delivered[1]. However, lack of robust and scalable UV-C dose validation tools impedes effective, widespread adoption. Tailored for field-deployment in settings lacking specialized UV-C radiometers, we design workflows to quantify photochromic UV-C dose indicators and extend dynamic range using low-cost filters. We demonstrate that photochromic quantification meets specifications required for robust UV-C measurements. Previously infeasible on-N95 measurements show ~20X dose variation within a UV-C system, highlighting how photochromics can underpin new validation strategies. Reference: 1. Brian Heimbuch and Del Harnish. Research to Mitigate a Shortage of Respiratory Protection Devices During Public Health Emergencies. (2019).

The oral cancer is the 6th most incident cancer type in the world and is most prevalent among men than in women. Usually screening procedures are performed only in advanced stages which leads to an increase of mortality and morbidity. Analyzing images from 20 patients we conclude that the observed optical image (conventional microscope or smartphone) of the formed pattern enables discrimination of oral cancer in few hours. It represents a non-invasive diagnostic method which minimizes hospital medical procedures, hospitalizations and patient discomfort.

We report a label-free, field-portable, holographic imaging flow cytometer that can automatically detect and count Giardia lamblia cysts in water samples with a throughput of 100 mL/h. Our cytometer has the dimensions of 19×19×16 cm and a laptop computer-connected to it reconstructs the phase and intensity images of the flowing microparticles in the sample at three different wavelengths and classifies them by a trained convolutional neural network, thereby detecting the Giardia cysts in real time. We experimentally demonstrated that our system can detect Giardia contamination in fresh and seawater samples containing as low as <10 cysts/50 mL.

Lens-free imaging (LFI) has become an important microscopy tool in many life science and industrial applications. Due to the absence of optical lenses (such as objectives) and accompanying lens aberrations (such as chromatic aberrations), the LFI modality is well suited for optical inspection of microscopic objects in a wide spectral range. However, the relatively restricted spectral sensitivity of CMOS imagers, i.e. from visible (~400 nm) up to near-infrared range (~900 nm), limits the wide spectral use of the technique. Many microscopic samples contain valuable information both in the visible and in the short wave infra-red (SWIR), sometimes in addition to visible (VIS) and near-infrared (NIR). With the recent emergence of cost-effective image sensor technologies such as quantum-dot and graphene-based image sensors with high quantum efficiency in SWIR, new lens-free imaging opportunities are emerging for wideband and high throughput microscopy. We demonstrate for the first time an LFI system based on a quantum-dot image sensor, capable of operating in both the visible and short-wave infrared range. The holograms of the samples are obtained through multiple partially coherent illumination sources in both visible and short-wave infrared (ranging from 405 nm to 1550 nm). The captured holograms are reconstructed to obtain images of the sample in focus. We demonstrate an optical resolution of 3.48 micron in a field of view of 9.6 mm² over the whole spectral range. Our technique mitigates the need for bulky and expensive achromatic imaging optics and offers significant improvements in cost, field-of-view, scalability, and optical resolution to achieve microscopic imaging in both the visible and short-wave infrared spectral range with a simple imaging system. We present in this paper a performance analysis of the system and several potential applications and use cases.

We present a deep learning-based high-throughput cytometer to detect rare cells in whole blood using a cost-effective and light-weight design. This system uses magnetic-particles to label and enrich the target cells. Then, a periodically-alternating magnetic-field creates time-modulated diffraction patterns of the target cells that are recorded using a lensless microscope. Finally, a custom-designed convolutional network is used to detect and classify the target cells based on their modulated spatio-temporal patterns. This cytometer was tested with cancer cells spiked in whole blood to achieve a limit-of-detection of 10 cells/mL. This compact, cost-effective and high-throughput cytometer might serve diagnostics needs in resource-limited-settings.

The development of smartphones over the last decade has led to a growth in interest of their potential to tackle numerous point-of-care diagnostic and treatment assessment applications. Despite this interest, there has been lackluster transition of smartphones within clinical care, where the reproducibility of measurements across devices and inability to perform image analysis within the device has hampered development. Here, we present an open-source platform for performing quantitative imaging and analysis within the iOS smartphone environment. We explore the need for RAW pixel data within quantitative applications and characterize the iPhone 11 to lay the foundation for its use in scientific and point-of-care imaging applications.

There have been numerous attempts to use mobile phone images to interpret results of lateral flow assays (LFA’s). Many initial efforts created attachments to position test strips by the camera or added external light sources. To see widespread use, especially in low-resource settings, for aiding test interpretation or performing some level of quantification, a mobile phone LFA reader should not require materials beyond the test and would be phone-agnostic. To assess the feasibility of this approach, twelve CareStart malaria LFA cassettes were run using spiked whole blood. About 880 images of these cassettes were acquired using three brands of phones and under various lighting conditions, imaging distances, and viewing angles. Test strip regions were converted to 1-dimensional (1D) intensity profiles along the direction of flow. Corrections for color accuracy, gamma, and white balance were implemented, and features such as peak height and area under the curve of control and test lines were used in linear regression. Both a fully connected and a 1D convolutional neural network were trained on the 1D profiles of test strips without feature extraction as well. The best regression models achieved R² of 0.77 and prediction error of 102 ng/ml. A multi-class support vector machine provided 84% accuracy for a semi-quantitative approach of negative or weak, medium, and high positives. For all analyses, corrections to color, white balance, etc. did not provide meaningful improvements, and limiting analysis to a single phone was not substantially better. Thus, there is promise for a device-agnostic mobile phone LFA reader.

In developing countries, anemia is a major public health problem; anemia affects 24.8% of the global population, corresponding to 1.62 billion people. As anemia is defined as a low hemoglobin level in the blood, it is important to measure exact hemoglobin content in grams per deciliter of the blood. Recent advances in mobile health (mHealth) technologies for blood hemoglobin levels are promising, but often rely on additional complex components to the smartphone and require blood sampling. As a result, noninvasive quantification of hemoglobin content in the blood is still limited. We have recently developed intravital mHealth spectroscopy to extract spectrally encoded microvascular and blood information from peripheral tissue. Spectral learning virtually transforms the built-in camera of a smartphone into a hyperspectral imager for spectroscopic analyses. Owing to the easy accessibility and relatively uniform microvasculature, the inner eyelid is used as a sensing site. Spectroscopic analyses of spectra acquired from the inner eyelid further result in key parameters about the blood and the microvasculature that are used for predicting blood hemoglobin levels in a noninvasive and real-time manner. Our clinical study conducted in sub-Saharan Africa supports reliable performance of blood hemoglobin quantification and anemia prediction. As our mHealth technology requires no additional attachment (our data-centric approach minimizes hardware complexity), the key features include mobility, simplicity, and affordability for rapid and scalable adaptation. Successful implementation with local governments and community healthcare workers can potentially provide unreached and underserved remote populations with accessible and affordable healthcare services in low-resource settings.

Point-of-care (PoC) devices are gaining more attention due to their rapid readout times, low cost and reduced system complexity. The use of these devices to aid in health outcomes, particularly of populations that lack access to centralized healthcare, is essential to improving quality of life. Broad fluorescence background presents a major issue in Raman detection with suppression techniques being employed to prevent signal crosstalk. The presented spectroscopic platform splits the Raman and fluorescence signal onto separate detectors in order in minimize crosstalk and to extract useful information from each optical signal. Separate illumination sources were utilized to selectively excite either Raman or fluorescence emission. The developed spectroscopic platform was designed using off-the-shelf components with small form factor and ultimately the capacity for low cost being the primary selection criteria. The platform enables multimodal detection of Raman signal over a spectral range of 900 – 2000 cm^-1 with a resolution of 2 nm, coupled with monitoring the average fluorescence emission intensity. The ratio of the two signals is compared in order to quantify the concentration of target molecules present. The optical system was assembled on a portable optical breadboard and calibrated using an Argon emission lamp. The multimodal functionality was validated using Raman reporter (4-MBA) tagged gold nanoparticles in solution with unbound fluorophores (fluorescein). Results showed an increase in both the Raman and fluorescence signals as the concentration of each was increased from 5-55 μM.