Dynamic MRI reconstruction from highly undersampled (k,t)-space data using weighted Schatten p-norm regularizer of tensor |
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| Institution: | 1. Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland;2. Division of Gastroenterology and Hepatology, University Hospital Zurich, Zurich, Switzerland;1. Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA;2. Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232, USA;3. Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA;4. AstraZeneca, Alderley Park, MACCLESFIELD, Cheshire SK10 4TG, United Kingdom;5. Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA;6. Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA;7. Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN 37232, USA;1. Medical Physics Laboratory, Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy;2. Radiology and Diagnostic Imaging Department, Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy;3. Department of Radiotherapy, Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy;4. Department of Pathology, Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy;5. Department of Otolaryngology & Head and Neck Surgery, Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy;1. Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC, USA;2. Department of Medical Radiological Technologists, Technological Education Institute of Athens, Greece;3. Department of Radiology, University Hospital of Larissa, Larissa, Greece;4. Department of Medical Imaging, IASO Thessalias Hospital, Larissa, Greece;5. Department of Medical Instruments Technology, Techological Education Institute of Athens, Greece;6. Department of Radiology, General Hospital of Athens ‘G. Genimatas’, Athens, Greece;1. Departments of Neurology, Leiden University Medical Center, Albinusdreef 2, PO Box 9600, 2300 RC Leiden, The Netherlands;2. Departments of Radiology, Leiden University Medical Center, Albinusdreef 2, PO Box 9600, 2300 RC Leiden, The Netherlands;3. Department of Radiation Sciences, Umeå, Sweden;4. C.J. Gorter Center for High Field MRI of the Leiden University Medical Center, Albinusdreef 2, PO Box 9600, 2300 RC Leiden, The Netherlands |
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| Abstract: | Combination of the low-rankness and sparsity has been successfully used to reconstruct desired dynamic magnetic resonance image (MRI) from highly-undersampled (k, t)-space data. However, nuclear norm, as a convex relaxation of the rank function, can cause the solution deviating from the original solution of low-rank problem. Moreover, equally treating different rank component is not flexible to deal with real applications. In this paper, an efficient reconstruction model is proposed to efficiently reconstruct dynamic MRI. First, we treat dynamic MRI as a 3rd-order tensor, and formulate the low-rankness via non-convex Schatten p-norm of matrices unfolded from the tensor. Secondly, we assign different weight for each rank component in Schatten p-norm. Furthermore, we combine the proposed weighted Schatten p-norm of a tensor as low-rank regularizer, and spatiotemporal total variation as sparse regularizer to formulate the reconstruction model for dynamic MRI. Thirdly, to efficiently solve the formulated reconstruction model, we derive an algorithm based on Bregman iterations with alternating direction multiplier. Over two public data sets of dynamic MRI, experiments demonstrate that the proposed method achieves much better quality. |
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