Causal MRI reconstruction via Kalman prediction and compressed sensing correction.
This technical note addresses the problem of causal online reconstruction of dynamic MRI, i.e. given the reconstructed frames till the previous time instant, we reconstruct the frame at the current instant. Our work follows a prediction-correction framework. Given the previous frames, the current frame is predicted based on a Kalman estimate. The difference between the estimate and the current frame is then corrected based on the k-space samples of the current frame; this reconstruction assumes that the difference is sparse. The method is compared against prior Kalman filtering based techniques and Compressed Sensing based techniques. Experimental results show that the proposed method is more accurate than these and considerably faster.
Indraprastha Institute of Information Technology, Delhi, India. Electronic address: angshul@iiitd.ac.in.
Angshul
A
Majumdar
D000465:Algorithms; D044962:Data Compression; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*
Compressed sensing; Kalman filter; Reconstruction