title: Accelerating phase-encoded proton MR spectroscopic imaging by compressed sensing.
abstract: OBJECTIVE
  To develop a phase encoding reduction scheme based on compressed sensing (CS) for phase-encoded (1) H MR spectroscopic imaging (MRSI).
  
  
  METHODS
  Phantom and in vivo rat brain MRSI experiments were performed at 7 Tesla to examine the performance of CS approach and compare it with the full k-space acquisition. The CS undersampling was performed by acquiring a pseudorandom and density-varying subset of phase encodings. Residual water resonance was first removed from the undersampled k-space dataset before CS reconstruction. The CS reconstruction was performed by a linearized Bregman iteration procedure for efficiency in computing large scale L1 minimization. The spectral and spatial fidelity was evaluated by comparing spectral linewidths and metabolite maps and voxel-wise Bland-Altman analysis.
  
  
  RESULTS
  CS preserved the spectral resolution and metabolite content levels. No spectral broadening was observed, and the estimation biases of the metabolite content levels were no more than 4%. CS clearly preserved the boundaries of metabolite maps but led to slight loss of details in metabolite maps.
  
  
  CONCLUSIONS
  This study demonstrated the feasibility of the proposed CS approach to accelerate phase-encoded (1) H MRSI.
authors.affiliation: Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Pokfulam, Hong Kong SAR, China; Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Cao
authors.forename: Ed X
authors.initials: EX
authors.lastname: Wu
mesh_terms: D000818:Animals; D001923:Brain Chemistry; D044962:Data Compression / Q000379:methods*; D005240:Feasibility Studies; D019047:Phantoms, Imaging; D066244:Proton Magnetic Resonance Spectroscopy / Q000379:methods*; D051381:Rats; D017207:Rats, Sprague-Dawley
keywords: MR spectroscopic imaging; compressed sensing; phase encoding reduction; sparsity