Causal MRI reconstruction via Kalman prediction and compressed sensing correction |
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| Institution: | 1. Department of Radiation Sciences, Umeå University, Umeå, Sweden;2. Department of Radiology, Uppsala University Hospital, Uppsala, Sweden;3. Applied Science Laboratory, GE Healthcare, Uppsala, Sweden;1. Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584CX Utrecht, The Netherlands;2. Department of Orthopedics, University Medical Center Utrecht, Heidelberglaan 100, 3584CX Utrecht, The Netherlands;1. Department of Medicine, Section of Cardiology, The University of Chicago, Chicago, IL, United States;2. Department of Surgery, Section of Cardiothoracic Surgery, The University of Chicago, Chicago, IL, United States;3. Department of Radiology, The University of Chicago, Chicago, IL, United States;4. Philips, Gainesville, FL, United States.;1. Department of Surgical Sciences, Uppsala University, Sweden.;2. Department of Immunology, Genetics and Pathology, Uppsala University, Sweden |
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| Abstract: | 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. |
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