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Proceedings Paper

Weighted perceptual difference model (case-PDM) for MR image quality evaluation
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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 by comparing to slower, full k-space, high quality reference images. To date, most perceptual difference models average a single scalar image quality metric over a large region of interest. In this paper, we create an alternative metric weighted to image processing features. Spatial filters were applied to the reference image to create edge and flat region images, then weighted and aggregated to create "structural" images which in turn spatially weighted the perceptual difference maps. We optimized the scale of the spatial filters and weighting scheme with an exhaustive search so as to improve the linear correlation coefficient between human ratings and weighted Case-PDM, across a large set of MR reconstruction test images of varying quality. Human ratings were obtained from a modified Double Stimulus Continuous Quality Scale experiment. For 5 different images (3 different brain, 1 cardiac, and 1 phantom images), r values [weighted PDM, average PDM] were improved ([0.96, 0.94], [0.93, 0.91], [0.97, 0.95], [0.97, 0.91], [0.96, 0.95]) in all cases. The method is robust across subjects and anatomy; that is, scores maintain a high correlation with human ratings even if the test dataset is different from the training dataset.

Paper Details

Date Published: 6 March 2008
PDF: 9 pages
Proc. SPIE 6917, Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment, 69170L (6 March 2008); doi: 10.1117/12.772945
Show Author Affiliations
Jun Miao, Case Western Reserve Univ. (United States)
Wilbur C. K. Wong, Case Western Reserve Univ. (United States)
David L. Wilson, Case Western Reserve Univ. (United States)
Univ. Hospitals of Cleveland (United States)


Published in SPIE Proceedings Vol. 6917:
Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment
Berkman Sahiner; David J. Manning, Editor(s)

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