title: A new perceptual difference model for diagnostically relevant quantitative image quality evaluation: a preliminary study.
abstract: OBJECTIVE
  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 preference of human observers toward different artifacts.
  
  
  METHODS
  We measured artifact disturbance to observers and calibrated the novel perceptual difference model (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.
authors.affiliation: Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA.
authors.forename: Jun
authors.initials: J
authors.lastname: Miao
authors.forename: Feng
authors.initials: F
authors.lastname: Huang
authors.forename: Sreenath
authors.initials: S
authors.lastname: Narayan
authors.forename: David L
authors.initials: DL
authors.lastname: Wilson
mesh_terms: D016477:Artifacts; D001185:Artificial Intelligence; D032701:Biomimetics / Q000379:methods*; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods; D010865:Pilot Projects; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014796:Visual Perception / Q000502:physiology*