title: Sparse reconstruction of compressive sensing MRI using cross-domain stochastically fully connected conditional random fields.
abstract: Magnetic Resonance Imaging (MRI) is a crucial medical imaging technology for the screening and diagnosis of frequently occurring cancers. However, image quality may suffer from long acquisition times for MRIs due to patient motion, which also leads to patient discomfort. Reducing MRI acquisition times can reduce patient discomfort leading to reduced motion artifacts from the acquisition process. Compressive sensing strategies applied to MRI have been demonstrated to be effective in decreasing acquisition times significantly by sparsely sampling the k-space during the acquisition process. However, such a strategy requires advanced reconstruction algorithms to produce high quality and reliable images from compressive sensing MRI.
  
  
  
  This paper proposes a new reconstruction approach based on cross-domain stochastically fully connected conditional random fields (CD-SFCRF) for compressive sensing MRI. The CD-SFCRF introduces constraints in both k-space and spatial domains within a stochastically fully connected graphical model to produce improved MRI reconstruction.
  
  
  
  Experimental results using T2-weighted (T2w) imaging and diffusion-weighted imaging (DWI) of the prostate show strong performance in preserving fine details and tissue structures in the reconstructed images when compared to other tested methods even at low sampling rates.
  
  
  
  The ability to better utilize a limited amount of information to reconstruct T2w and DWI images in a short amount of time while preserving the important details in the images demonstrates the potential of the proposed CD-SFCRF framework as a viable reconstruction algorithm for compressive sensing MRI.
authors: Department of Systems Design Engineering, University of Waterloo, Ontario, Waterloo, Canada.
authors: Edward
authors: E
authors: Li
authors: Department of Medical Imaging, University of Toronto and Sunnybrook Research Institute, Toronto, Ontario, Canada.
authors: Farzad
authors: F
authors: Khalvati
authors: Department of Systems Design Engineering, University of Waterloo, Ontario, Waterloo, Canada.
authors: Mohammad Javad
authors: MJ
authors: Shafiee
authors: Department of Medical Imaging, University of Toronto and Sunnybrook Research Institute, Toronto, Ontario, Canada.
authors: Masoom A
authors: MA
authors: Haider
authors: Department of Systems Design Engineering, University of Waterloo, Ontario, Waterloo, Canada. a28wong@uwaterloo.ca.
authors: Alexander
authors: A
authors: Wong
mesh_terms: D000465:Algorithms; D016477:Artifacts; D044962:Data Compression / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement
keywords: Compressive sensing; Conditional random fields; Magnetic resonance imaging