Conference proceeding
Multi-Shot Sensitivity-Encoded Diffusion MRI Using Model-Based Deep Learning (Modl-Mussels)
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Vol.2019, pp.1541-1544
04/2019
DOI: 10.1109/ISBI.2019.8759514
PMCID: PMC7879460
PMID: 33584974
Abstract
We propose a model-based deep learning architecture for the correction of phase errors in multishot diffusion-weighted echo-planar MRI images. This work is a generalization of MUSSELS, which is a structured low-rank algorithm. We show that an iterative reweighted least-squares implementation of MUSSELS resembles the model-based deep learning (MoDL) framework. We propose to replace the self-learned linear filter bank in MUSSELS with a convolutional neural network, whose parameters are learned from exemplary data. The proposed algorithm reduces the computational complexity of MUSSELS by several orders of magnitude, while providing comparable image quality.
Details
- Title: Subtitle
- Multi-Shot Sensitivity-Encoded Diffusion MRI Using Model-Based Deep Learning (Modl-Mussels)
- Creators
- Hemant K Aggarwal - University of Iowa, Iowa, USAMerry P Mani - University of Iowa, Iowa, USAMathews Jacob - University of Iowa, Iowa, USA
- Resource Type
- Conference proceeding
- Publication Details
- 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Vol.2019, pp.1541-1544
- DOI
- 10.1109/ISBI.2019.8759514
- PMID
- 33584974
- PMCID
- PMC7879460
- NLM abbreviation
- Proc IEEE Int Symp Biomed Imaging
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Publisher
- IEEE
- Language
- English
- Date published
- 04/2019
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute; Radiation Oncology
- Record Identifier
- 9984070564002771
Metrics
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