Sparse MRI reconstruction using multi-contrast image guided graph representation |
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| Affiliation: | 1. Department of Communication Engineering, Xiamen University, Xiamen 361005, China;2. Department of Electronic Science, Xiamen University, Xiamen 361005, China;3. Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China;4. School of Computer and Information Engineering, Fujian Provincial University Key Laboratory of Internet of Things Application Technology, Xiamen University of Technology, Xiamen 361024, China;5. Department of Radiology, Xiamen 2nd Hospital, Xiamen 361021, China;1. Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA;2. Clinical Research Core, Office of the Scientific Director, National Institute on Aging, National Institutes of Health, Baltimore, MD 21225, USA;1. Aix-Marseille Université, Centre de Résonance Magnétique Biologique et Médicale, UMR CNRS 7339, Marseille, France;2. Assistance Publique – Hôpitaux de Marseille, CEMREM, Pôle d''Imagerie Médicale, CHU Timone, Marseille, France;1. GE Healthcare, University of California, San Diego, United States;2. Radiology Department, University of California, San Diego, United States;1. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA;2. Applied Mathematics, Universidad Rey Juan Carlos, Mostoles, Madrid, Spain;3. A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA;4. Harvard Medical School, Boston, MA, USA;5. Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA;6. Institute of Medical Engineering & Science, MIT, Cambridge, MA, USA |
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| Abstract: | Accelerating the imaging speed without sacrificing image structures plays an important role in magnetic resonance imaging. Under-sampling the k-space data and reconstructing the image with sparsity constraint is one efficient way to reduce the data acquisition time. However, achieving high acceleration factor is challenging since image structures may be lost or blurred when the acquired information is not sufficient. Therefore, incorporating extra knowledge to improve image reconstruction is expected for highly accelerated imaging. Fortunately, multi-contrast images in the same region of interest are usually acquired in magnetic resonance imaging protocols. In this work, we propose a new approach to reconstruct magnetic resonance images by learning the prior knowledge from these multi-contrast images with graph-based wavelet representations. We further formulate the reconstruction as a bi-level optimization problem to allow misalignment between these images. Experiments on realistic imaging datasets demonstrate that the proposed approach improves the image reconstruction significantly and is practical for real world application since patients are unnecessarily to stay still during successive reference image scans. |
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