Journal article
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)
IEEE transactions on medical imaging, Vol.39(12), pp.4186-4197
12/2020
DOI: 10.1109/TMI.2020.3014581
PMCID: PMC7731895
PMID: 32755854
Abstract
Structured low-rank (SLR) algorithms, which exploit annihilation relations between the Fourier samples of a signal resulting from different properties, is a powerful image reconstruction framework in several applications. This scheme relies on low-rank matrix completion to estimate the annihilation relations from the measurements. The main challenge with this strategy is the high computational complexity of matrix completion. We introduce a deep learning (DL) approach to significantly reduce the computational complexity. Specifically, we use a convolutional neural network (CNN)-based filterbank that is trained to estimate the annihilation relations from imperfect (under-sampled and noisy) k-space measurements of Magnetic Resonance Imaging (MRI). The main reason for the computational efficiency is the pre-learning of the parameters of the non-linear CNN from exemplar data, compared to SLR schemes that learn the linear filterbank parameters from the dataset itself. Experimental comparisons show that the proposed scheme can enable calibration-less parallel MRI; it can offer performance similar to SLR schemes while reducing the runtime by around three orders of magnitude. Unlike pre-calibrated and self-calibrated approaches, the proposed uncalibrated approach is insensitive to motion errors and affords higher acceleration. The proposed scheme also incorporates image domain priors that are complementary, thus significantly improving the performance over that of SLR schemes.
Details
- Title: Subtitle
- Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)
- Creators
- Aniket Pramanik - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USAHemant Kumar Aggarwal - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USAMathews Jacob - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on medical imaging, Vol.39(12), pp.4186-4197
- DOI
- 10.1109/TMI.2020.3014581
- PMID
- 32755854
- PMCID
- PMC7731895
- NLM abbreviation
- IEEE Trans Med Imaging
- ISSN
- 0278-0062
- eISSN
- 1558-254X
- Publisher
- Institute of Electrical and Electronics Engineers
- Grant note
- 1R01EB019961-01A1 / NIH (10.13039/100000002)
- Language
- English
- Date published
- 12/2020
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute; Radiation Oncology
- Record Identifier
- 9984070545102771
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