A new perceptual difference model for diagnostically relevant quantitative image quality evaluation: A preliminary study

Abstract

Purpose Most objective image quality metrics average over a wide range of image degradations. However, human clinicians demonstrate bias toward different types of artifacts. Here, we aim to create a perceptual difference model based on Case-PDM that mimics the bias of human observers towards different artifacts. Method We measured artifact disturbance to observers and calibrated the novel PDM. To tune the new model, which we call Artifact-PDM, degradations were synthetically added to three healthy brain MR data sets. Four types of artifacts (noise, blur, aliasing, or “oil-painting” which shows up as flattened, over-smoothened regions) of standard Compressed Sensing (CS) reconstruction, within a reasonable range of artifact severity, as measured by both PDM and visual inspection, were considered. After the model parameters were tuned by each synthetic image, we used a Functional Measurement Theory pair-comparison experiment to measure the disturbance of each artifact to human observers and determine the weights of each artifact’s PDM score. To validate Artifact-PDM, human ratings obtained from a Double Stimulus Continuous Quality Scale experiment were compared to the model for noise, blur, aliasing, oil-painting and overall qualities using a large set of CS reconstructed MR images of varying quality. Finally, we used this new approach to compare CS to GRAPPA, a parallel MRI reconstruction algorithm. Results We found that for the same Artifact-PDM score, the human observer found incoherent aliasing to be the most disturbing and noise the least. Artifact-PDM results were highly correlated to human observers in both experiments. Optimized CS reconstruction quality compared favorably to GRAPPA’s for the same sampling ratio. Conclusions We conclude our novel metric can faithfully represent human observer artifact evaluation and can be useful in evaluating CS and GRAPPA reconstruction algorithms, especially in studying artifact trade-offs.

Compressed sensing MRI: a review of the clinical literature

“…39 Sharma et al 23 (brain MRI), Worters et al 26 (spine MSI) and Zhang et al 29 (paediatric abdominal MRI) were the only studies we reviewed that systematically described artefacts observed in CS. In the study of brain MRI, artefacts became prominent and disruptive to diagnosis at acceleration factors .23.…”

Super‐resolution reconstruction in frequency, image, and wavelet domains to reduce through‐plane partial voluming in MRI

“…The rankings are then averaged and correlated with the similarity metrics. We should note that various quantitative measures, such as perceptual evaluation models, 34,35 have been used to evaluate MR image artifacts, such as blur, aliasing, and noise; but the focus here was on resolution, therefore we design the phantom study to compare images with a high-resolution scan. Expert evaluation is similarly performed to compare reconstructed images to the high-resolution reference scan.…”

The spatial statistics of structural magnetic resonance images: application to post-acquisition quality assessment of brain MRI images

“…They can be categorized into reference methods [12], [13], [14], [15], [16] and no-reference methods [17], [18], [19], [20], [21]. Detailed review of image quality evaluation for a general class of images and MRI images can be found in [22], [23], [24], [25], [26], [27], [28], [29].…”