title: PROMISE: parallel-imaging and compressed-sensing reconstruction of multicontrast imaging using SharablE information.
abstract: OBJECTIVE
  A typical clinical MR examination includes multiple scans to acquire images with different contrasts for complementary diagnostic information. The multicontrast scheme requires long scanning time. The combination of partially parallel imaging and compressed sensing (CS-PPI) has been used to reconstruct accelerated scans. However, there are several unsolved problems in existing methods. The target of this work is to improve existing CS-PPI methods for multicontrast imaging, especially for two-dimensional imaging.
  
  
  METHODS
  If the same field of view is scanned in multicontrast imaging, there is significant amount of sharable information. It is proposed in this study to use manifold sharable information among multicontrast images to enhance CS-PPI in a sequential way. Coil sensitivity information and structure based adaptive regularization, which were extracted from previously reconstructed images, were applied to enhance the following reconstructions. The proposed method is called Parallel-imaging and compressed-sensing Reconstruction Of Multicontrast Imaging using SharablE information (PROMISE).
  
  
  RESULTS
  Using L1 -SPIRiT as a CS-PPI example, results on multicontrast brain and carotid scans demonstrated that lower error level and better detail preservation can be achieved by exploiting manifold sharable information. Besides, the privilege of PROMISE still exists while there is interscan motion.
  
  
  CONCLUSIONS
  Using the sharable information among multicontrast images can enhance CS-PPI with tolerance to motions.
authors.affiliation: Magnetic Resonance System Research Lab, Department of Electrical Engineering, Stanford University, Stanford, California, USA; Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China.
authors.forename: Enhao
authors.initials: E
authors.lastname: Gong
authors.forename: Feng
authors.initials: F
authors.lastname: Huang
authors.forename: Kui
authors.initials: K
authors.lastname: Ying
authors.forename: Wenchuan
authors.initials: W
authors.lastname: Wu
authors.forename: Shi
authors.initials: S
authors.lastname: Wang
authors.forename: Chun
authors.initials: C
authors.lastname: Yuan
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D002339:Carotid Arteries / Q000033:anatomy & histology*; D044962:Data Compression / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: compressed sensing; multichannel; multicontrast; parallel imaging; sharable information