Model-based image reconstruction for dynamic cardiac perfusion MRI from sparse data.
The paper presents a novel approach for dynamic magnetic resonance imaging (MRI) cardiac perfusion image reconstruction from sparse k-space data. It formulates the reconstruction problem in an inverse-methods setting. Relevant prior information is incorporated via a parametric model for the perfusion process. This wealth of prior information empowers the proposed method to give high-quality reconstructions from very sparse k-space data. The paper presents reconstruction results using both Cartesian and radial sampling strategies using data simulated from a real acquisition. The proposed method produces high-quality reconstructions using 14% of the k-space data. The model-based approach can potentially greatly benefit cardiac myocardial perfusion studies as well as other dynamic contrast-enhanced MRI applications including tumor imaging.
Awate
Suyash P
SP
School of Computing, University of Utah, UT 84112, USA. suyash@cs.utah.edu
DiBella
Edward V  R
EV
Tasdizen
Tolga
T
Whitaker
Ross T
RT
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003287:Contrast Media / Q000493:pharmacokinetics*; D006321:Heart / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008955:Models, Cardiovascular; D015233:Models, Statistical; D009206:Myocardium / Q000378:metabolism*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010477:Perfusion / Q000379:methods; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity
D003287:Contrast Media