title: Patch based reconstruction of undersampled data (PROUD) for high signal-to-noise ratio and high frame rate contrast enhanced liver imaging.
abstract: OBJECTIVE
  High spatial-temporal four-dimensional imaging with large volume coverage is necessary to accurately capture and characterize liver lesions. Traditionally, parallel imaging and adapted sampling are used toward this goal, but they typically result in a loss of signal to noise. Furthermore, residual under-sampling artifacts can be temporally varying and complicate the quantitative analysis of contrast enhancement curves needed for pharmacokinetic modeling. We propose to overcome these problems using a novel patch-based regularization approach called Patch-based Reconstruction Of Under-sampled Data (PROUD).
  
  
  METHODS
  PROUD produces high frame rate image reconstructions by exploiting the strong similarities in spatial patches between successive time frames to overcome the severe k-space under-sampling. To validate PROUD, a numerical liver perfusion phantom was developed to characterize contrast-to-noise ratio (CNR) performance compared with a previously proposed method, TRACER. A second numerical phantom was constructed to evaluate the temporal footprint and lag of PROUD and TRACER reconstructions. Finally, PROUD and TRACER were evaluated in a cohort of five liver donors.
  
  
  RESULTS
  In the CNR phantom, PROUD, compared with TRACER, improved peak CNR by 3.66 times while maintaining or improving temporal fidelity. In vivo, PROUD demonstrated an average increase in CNR of 60% compared with TRACER.
  
  
  CONCLUSIONS
  The results presented in this work demonstrate the feasibility of using a combination of patch based image constraints with temporal regularization to provide high SNR, high temporal frame rate and spatial resolution four dimensional imaging.
authors.affiliation: Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA.|Department of Radiology, Weill Cornell Medical College, New York, New York, USA.
authors.forename: Mitchell A
authors.initials: MA
authors.lastname: Cooper
authors.affiliation: Department of Radiology, Weill Cornell Medical College, New York, New York, USA.
authors.forename: Thanh D
authors.initials: TD
authors.lastname: Nguyen
authors.affiliation: Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA.|Department of Radiology, Weill Cornell Medical College, New York, New York, USA.
authors.forename: Bo
authors.initials: B
authors.lastname: Xu
authors.affiliation: Department of Radiology, Weill Cornell Medical College, New York, New York, USA.
authors.forename: Martin R
authors.initials: MR
authors.lastname: Prince
authors.affiliation: Division of Computer Science, Technion - Israel Institute of Technology, Haifa, Israel.
authors.forename: Michael
authors.initials: M
authors.lastname: Elad
authors.affiliation: Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA.|Department of Radiology, Weill Cornell Medical College, New York, New York, USA.
authors.forename: Yi
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Radiology, Weill Cornell Medical College, New York, New York, USA.
authors.forename: Pascal
authors.initials: P
authors.lastname: Spincemaille
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003287:Contrast Media; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000033:anatomy & histology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio
chemical_list: D003287:Contrast Media
keywords: PROUD; TRACER; dictionary learning; liver; patch; perfusion; time-resolved