Proceedings Article | 21 March 2007
KEYWORDS: Image quality, Magnetic resonance imaging, Human subjects, Reconstruction algorithms, Data modeling, Image restoration, Image processing, Data analysis, Brain, Calibration
There is an extraordinary number of fast MR imaging techniques, especially for parallel imaging. When one considers
multiple reconstruction algorithms, reconstruction parameters, coil configurations, acceleration factors, noise levels, and
multiple test images, one can easily create 1000's of test images for image quality evaluation. We have found the
perceptual difference model (Case-PDM) to be quite useful as a means of rapid quantitative image quality evaluation in
such experiments, and have applied it to keyhole, spiral, SENSE, and GRAPPA applications. In this study, we have
compared human evaluation of MR images from multiple organs and from multiple image reconstruction algorithms to
Case-PDM. We compared human DSCQS (Double Stimulus Continuous Quality Scale) scoring against Case-PDM
measurements for 3 different image types and 3 different image reconstruction algorithms. We found that Case-PDM
linearly correlated (r > 0.9) with human subject ratings over a very large range of image quality. We also compared
Case-PDM to other image quality evaluation methods. Case-PDM generally performed better than NASA's DCTune,
MITRE's IQM, Zhou Wang's NR models and mean square error (MSE) method, by showing a higher Pearson
correlation coefficient, higher Spearman rank-order correlation and lower root-mean-squared error. All three models
(Case-PDM, Sarnoff's IDM, and Zhou Wang's SSIM) performed very similarly in this experiment. To focus on high
quality reconstructions, we performed a 2-AFC (Alternate Forced Choice) experiment to determine the "just perceptible
difference" between two images. We found that threshold Case-PDM scores changed little (0.6-1.8) with 2 different
image types and 3 degradation patterns, and results with Case-PDM were much tighter than the other methods (IDM and
MSE) by showing a lower ratio of mean to standard deviation value. We conclude that Case-PDM can correctly predict
the ordering of image quality over a large range of image quality. Case-PDM can also be used to screen the images
which are "perceptually equal" to the original image. Although Case-PDM is a very useful tool for comparing "similar
raw images with similar processing," one should be careful when interpreting Case-PDM scores across MR images.
