
Proceedings Paper
Selective evaluation of noise, blur, and aliasing artifacts in fast MRI reconstructions using a weighted perceptual difference model: Case-PDMFormat | Member Price | Non-Member Price |
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Paper Abstract
The perceptual difference model (Case-PDM) is being used to quantify image quality of fast, parallel MR acquisitions
and reconstruction algorithms as compared to slower, full k-space, high quality reference images. To date, most
perceptual difference models average image quality over a wide range of image degradations. Here, we create metrics
weighted to different types of artifacts. The selective PDM is tuned using test images from an input reference image
degraded by noise, blur, or aliasing. Using an objective function based on the computation of diffusivity and edges
applied to the output perceptual difference map, cortex channels in the PDM are arranged in a matrix and weighted by a
2D Gaussian function to ensure maximal response to each artifact in turn. PDM scores were compared to human ratings
across a large set of MR reconstruction test images of varying quality. Human ratings (i.e. overall, noise, blur, and
aliasing ratings) were obtained from a modified Double Stimulus Continuous Quality Scale experiment. For 3 different
image types (brain, cardiac, and phantom), averaged r values [PDM, noise-PDM, blur-PDM, aliasing-PDM] were
[0.933±0.018, 0.938±0.015, 0.727±0.106, 0.500±0.193], which with the possible exception of aliasing compared
favorably inter-subject correlation [0.936±0.028, 0.856±0.064, 0.539±0.230, 0.767±0.125], respectively. With continued
fine tuning, we believe that the weighted Case-PDM score will be useful for selectively evaluating artifacts in fast MR
imaging.
Paper Details
Date Published: 12 March 2009
PDF: 7 pages
Proc. SPIE 7263, Medical Imaging 2009: Image Perception, Observer Performance, and Technology Assessment, 72631N (12 March 2009); doi: 10.1117/12.813818
Published in SPIE Proceedings Vol. 7263:
Medical Imaging 2009: Image Perception, Observer Performance, and Technology Assessment
Berkman Sahiner; David J. Manning, Editor(s)
PDF: 7 pages
Proc. SPIE 7263, Medical Imaging 2009: Image Perception, Observer Performance, and Technology Assessment, 72631N (12 March 2009); doi: 10.1117/12.813818
Show Author Affiliations
Jun Miao, Case Western Reserve Univ. (United States)
David L. Wilson, Univ. Hospitals of Cleveland & Case Western Reserve Univ. (United States)
Published in SPIE Proceedings Vol. 7263:
Medical Imaging 2009: Image Perception, Observer Performance, and Technology Assessment
Berkman Sahiner; David J. Manning, Editor(s)
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