title: Accelerated 3D MERGE carotid imaging using compressed sensing with a hidden Markov tree model.
abstract: OBJECTIVE
  To determine the potential for accelerated 3D carotid magnetic resonance imaging (MRI) using wavelet based compressed sensing (CS) with a hidden Markov tree (HMT) model.
  
  
  METHODS
  We retrospectively applied HMT model-based CS and conventional CS to 3D carotid MRI data with 0.7 mm isotropic resolution from six subjects with known carotid stenosis (12 carotids). We applied a wavelet-tree model learned from a training database of carotid images to improve CS reconstruction. Quantitative endpoints such as lumen area, wall area, mean and maximum wall thickness, plaque calcification, and necrotic core area were measured and compared using Bland-Altman analysis along with image quality.
  
  
  RESULTS
  Rate-4.5 acceleration with HMT model-based CS provided image quality comparable to that of rate-3 acceleration with conventional CS and fully sampled reference reconstructions. Morphological measurements made on rate-4.5 HMT model-based CS reconstructions were in good agreement with measurements made on fully sampled reference images. There was no significant bias or correlation between mean and difference of measurements when comparing rate 4.5 HMT model-based CS with fully sampled reference images.
  
  
  CONCLUSIONS
  HMT model-based CS can potentially be used to accelerate clinical carotid MRI by a factor of 4.5 without impacting diagnostic quality or quantitative endpoints.
authors: Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, California 90089-2564, USA. makhijan@usc.edu
authors: Mahender K
authors: MK
authors: Makhijani
authors: Niranjan
authors: N
authors: Balu
authors: Kiyofumi
authors: K
authors: Yamada
authors: Chun
authors: C
authors: Yuan
authors: Krishna S
authors: KS
authors: Nayak
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D002339:Carotid Arteries / Q000473:pathology*; D016893:Carotid Stenosis / Q000473:pathology*; D044962:Data Compression / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*