title: Accelerated noncontrast-enhanced pulmonary vein MRA with distributed compressed sensing.
abstract: OBJECTIVE
  To investigate the efficacy of distributed compressed sensing (CS) to accelerate free-breathing, electrocardiogram (ECG)-triggered noncontrast pulmonary vein (PV) magnetic resonance angiography (MRA).
  
  
  METHODS
  Fully sampled ECG-triggered noncontrast PV MRA, using a spatially selective slab inversion preparation sequence, was acquired on seven healthy adult subjects (27 ± 17 years, range: 19-65 years, 4 women). The k-space data were retrospectively randomly undersampled by factors of 2, 4, 6, 8, and 10 and then reconstructed using distributed CS and coil-by-coil CS methods. The reconstructed images were evaluated by two blinded readers in consensus for assessment of major PV branches as well as the presence of artifacts in left atrium (LA) and elsewhere. Diameters of right inferior and right superior PV branches were measured. Additionally, mean square errors (MSE) of the reconstructions were calculated.
  
  
  RESULTS
  Both CS methods resulted in image quality scores similar to the fully sampled reference images at undersampling factors up to 6-fold for distributed CS and 4-fold for coil-by-coil CS reconstructions. There was no difference in the presence of artifacts in LA and freedom from important artifacts elsewhere between the two techniques up to undersampling factors of 10 compared to the fully sampled reconstruction. For the PV diameters, no systematic variation between the reference and the reconstructions were observed for either technique. There were no significant differences in MSE between the two methods when compared at a given rate, but the difference was significant when compared across all rates.
  
  
  CONCLUSIONS
  The sparsity of noncontrast PV MRA and the joint sparsity of different coil images allow imaging at high undersampling factors (up to 6-fold) when distributed CS is used.
authors.affiliation: Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts 02215, USA.
authors.forename: Mehmet
authors.initials: M
authors.lastname: Akçakaya
authors.forename: Peng
authors.initials: P
authors.lastname: Hu
authors.forename: Michael L
authors.initials: ML
authors.lastname: Chuang
authors.forename: Thomas H
authors.initials: TH
authors.lastname: Hauser
authors.forename: Long H
authors.initials: LH
authors.lastname: Ngo
authors.forename: Warren J
authors.initials: WJ
authors.lastname: Manning
authors.forename: Vahid
authors.initials: V
authors.lastname: Tarokh
authors.forename: Reza
authors.initials: R
authors.lastname: Nezafat
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D004562:Electrocardiography / Q000379:methods; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional / Q000379:methods; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011667:Pulmonary Veins / Q000473:pathology*