期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 35, 期 9, 页码 2119-2129出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2016.2550204
关键词
High-dimensional MR imaging; low-rank tensor; partial separability; sparse regularization; sparse sampling
类别
资金
- Chinese Scholarship Council
- National Natural Science Foundation of China [61362001]
- [NIH-R01-EB013695-01]
- [NIH-R21-EB021013-01]
- [NIH-P41-EB002034-14A1]
High-dimensional MR imaging often requires long data acquisition time, thereby limiting its practical applications. This paper presents a low-rank tensor based method for accelerated high-dimensional MR imaging using sparse sampling. This method represents high-dimensional images as low-rank tensors (or partially separable functions) and uses this mathematical structure for sparse sampling of the data space and for image reconstruction from highly undersampled data. More specifically, the proposed method acquires two datasets with complementary sampling patterns, one for subspace estimation and the other for image reconstruction; image reconstruction from highly undersampled data is accomplished by fitting the measured data with a sparsity constraint on the core tensor and a group sparsity constraint on the spatial coefficients jointly using the alternating direction method of multipliers. The usefulness of the proposed method is demonstrated in MRI applications; it may also have applications beyond MRI.
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