title: Direct estimation of tracer-kinetic parameter maps from highly undersampled brain dynamic contrast enhanced MRI.
abstract: The purpose of this work was to develop and evaluate a T1-weighted dynamic contrast enhanced (DCE) MRI methodology where tracer-kinetic (TK) parameter maps are directly estimated from undersampled (k,t)-space data.
  
  
  
  The proposed reconstruction involves solving a nonlinear least squares optimization problem that includes explicit use of a full forward model to convert parameter maps to (k,t)-space, utilizing the Patlak TK model. The proposed scheme is compared against an indirect method that creates intermediate images by parallel imaging and compressed sensing before to TK modeling. Thirteen fully sampled brain tumor DCE-MRI scans with 5-second temporal resolution are retrospectively undersampled at rates R = 20, 40, 60, 80, and 100 for each dynamic frame. TK maps are quantitatively compared based on root mean-squared-error (rMSE) and Bland-Altman analysis. The approach is also applied to four prospectively R = 30 undersampled whole-brain DCE-MRI data sets.
  
  
  
  In the retrospective study, the proposed method performed statistically better than indirect method at R ≥ 80 for all 13 cases. This approach provided restoration of TK parameter values with less errors in tumor regions of interest, an improvement compared to a state-of-the-art indirect method. Applied prospectively, the proposed method provided whole-brain, high-resolution TK maps with good image quality.
  
  
  
  Model-based direct estimation of TK maps from k,t-space DCE-MRI data is feasible and is compatible up to 100-fold undersampling. Magn Reson Med 78:1566-1578, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
authors.affiliation: Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA.
authors.forename: Yi
authors.initials: Y
authors.lastname: Guo
authors.affiliation: Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA.
authors.forename: Sajan Goud
authors.initials: SG
authors.lastname: Lingala
authors.affiliation: Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA.
authors.forename: Yinghua
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: GE Healthcare, Calgary, Alberta, Canada.
authors.forename: R Marc
authors.initials: RM
authors.lastname: Lebel
authors.affiliation: Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA.
authors.forename: Krishna S
authors.initials: KS
authors.lastname: Nayak
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging*; D001932:Brain Neoplasms / Q000000981:diagnostic imaging; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D019047:Phantoms, Imaging; D012189:Retrospective Studies
keywords: constrained reconstruction; dynamic contrast enhanced (DCE) MRI; kinetic parameter mapping; model-based reconstruction