Comprehensive quantitative image quality evaluation of compressed sensing MRI reconstructions using a weighted perceptual difference model (Case-PDM): selective evaluation, disturbance calibration, and aggregative evaluation of noise, blur, aliasing, and oil-painting artifacts

Abstract

The perceptual difference model (Case-PDM) is being used to quantify image quality of fast MR acquisitions and sparse 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 and assume that the observer has no bias towards any of them. Here, we create metrics weighted to different types of artifacts, calibrated to a human observer's preference, and then aggregate them to produce a comprehensive evaluation. The selective PDM is tuned using test images from an input reference image degraded by noise, blur, aliasing, or oil-painting. To each artifact, responses of cortex channels in the PDM are normalized to be weights used for selective evaluation. A pair comparison experiment based on functional measurement theory was used to calibrate selective PDM score of each artifact to its measured disturbance. Test images of varying quality were from identical reference image degraded by one type of artifact. We found that human observers rated aliasing > blur > oil-painting > noise. In order to validate the new evaluation approach, PDM scores were compared to human ratings across a large set of compressed sensing MR reconstruction test images of varying quality. Human ratings (i.e. overall, noise, blur, aliasing, and oil-painting ratings) were obtained from a modified Double Stimulus Continuous Quality Scale experiment. For 3 brain images (transverse, sagittal, and coronal planes), averaged r values [comprehensive-PDM, noise-PDM, blur-PDM, aliasing-PDM, oilpainting- PDM] were [0.947±0.010, 0.827±0.028, 0.913±0.005, 0.941±0.016, 0.884±0.025]. We conclude the weighted Case-PDM is useful for selectively evaluating MR reconstruction artifacts and the proposed comprehensive PDM score can faithfully represent human evaluation, especially when demonstrating artifact bias, of compressed sensing reconstructed MR images.