  Magnetic Resonance Imaging (MRI) is a crucial medical imaging technology for
the screening and diagnosis of frequently occurring cancers. However image
quality may suffer by long acquisition times for MRIs due to patient motion, as
well as result in great patient discomfort. Reducing MRI acquisition time can
reduce patient discomfort and as a result reduces motion artifacts from the
acquisition process. Compressive sensing strategies, when applied to MRI, have
been demonstrated to be effective at decreasing acquisition times significantly
by sparsely sampling the \emph{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 \emph{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.
