Conference proceeding
Calibrationless Parallel MRI Using Model Based Deep Learning (C-MODL)
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Vol.2020, pp.1428-1431
04/2020
DOI: 10.1109/ISBI45749.2020.9098490
PMID: 33584976
Abstract
We introduce a fast model based deep learning approach for calibrationless parallel MRI reconstruction. The proposed scheme is a non-linear generalization of structured low rank (SLR) methods that self learn linear annihilation filters from the same subject. It pre-learns non-linear annihilation relations in the Fourier domain from exemplar data. The pre-learning strategy significantly reduces the computational complexity, making the proposed scheme three orders of magnitude faster than SLR schemes. The proposed framework also allows the use of a complementary spatial domain prior; the hybrid regularization scheme offers improved performance over calibrated image domain MoDL approach. The calibrationless strategy minimizes potential mismatches between calibration data and the main scan, while eliminating the need for a fully sampled calibration region.
Details
- Title: Subtitle
- Calibrationless Parallel MRI Using Model Based Deep Learning (C-MODL)
- Creators
- Aniket Pramanik - The University of Iowa,Iowa City,USAHemant Aggarwal - The University of Iowa,Iowa City,USAMathews Jacob - The University of Iowa,Iowa City,USA
- Resource Type
- Conference proceeding
- Publication Details
- 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Vol.2020, pp.1428-1431
- DOI
- 10.1109/ISBI45749.2020.9098490
- PMID
- 33584976
- NLM abbreviation
- Proc IEEE Int Symp Biomed Imaging
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Publisher
- IEEE
- Language
- English
- Date published
- 04/2020
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute; Radiation Oncology
- Record Identifier
- 9984070739802771
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