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Magnetic Resonance Imaging (MRI) is a crucial medical imaging technology forthe screening and diagnosis of frequently occurring cancers. However imagequality may suffer by long acquisition times for MRIs due to patient motion, aswell as result in great patient discomfort. Reducing MRI acquisition time canreduce patient discomfort and as a result reduces motion artifacts from theacquisition process. Compressive sensing strategies, when applied to MRI, havebeen demonstrated to be effective at decreasing acquisition times significantlyby sparsely sampling the \emph{k}-space during the acquisition process.However, such a strategy requires advanced reconstruction algorithms to producehigh quality and reliable images from compressive sensing MRI. This paperproposes a new reconstruction approach based on cross-domain stochasticallyfully connected conditional random fields (CD-SFCRF) for compressive sensingMRI. The CD-SFCRF introduces constraints in both \emph{k}-space and spatialdomains within a stochastically fully connected graphical model to produceimproved MRI reconstruction. Experimental results using T2-weighted (T2w)imaging and diffusion-weighted imaging (DWI) of the prostate show strongperformance in preserving fine details and tissue structures in thereconstructed images when compared to other tested methods even at low samplingrates.

Sparse Reconstruction of Compressive Sensing MRI using Cross-Domain Stochastically Fully Connected Conditional Random Fields