Model-Based Free-Breathing Cardiac MRI Reconstruction Using Deep Learned & Storm Priors: MODL-STORM

Sampurna Biswas; Hemant K Aggarwal; Sunrita Poddar; Mathews Jacob

doi:10.1109/ICASSP.2018.8462637

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Model-Based Free-Breathing Cardiac MRI Reconstruction Using Deep Learned & Storm Priors: MODL-STORM

Sampurna Biswas, Hemant K Aggarwal, Sunrita Poddar and Mathews Jacob

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol.2018-, pp.6533-6537

04/2018

DOI: 10.1109/ICASSP.2018.8462637

PMID: 33716574

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Abstract

We introduce a model-based reconstruction framework with deep learned (DL) and smoothness regularization on manifolds (STORM) priors to recover free breathing and ungated (FBU) cardiac MRI from highly undersampled measurements. The DL priors enable us to exploit the local correlations, while the STORM prior enables us to make use of the extensive non-local similarities that are subject dependent. We introduce a novel model-based formulation that allows the seamless integration of deep learning methods with available prior information, which current deep learning algorithms are not capable of. The experimental results demonstrate the preliminary potential of this work in accelerating FBU cardiac MRI.

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Magnetic Resonance Imaging

Machine Learning

Training

Storms

Computational modeling

model-based

inverse problems

Free breathing cardiac MRI

deep CNNs

Image reconstruction

Details

Title: Subtitle: Model-Based Free-Breathing Cardiac MRI Reconstruction Using Deep Learned & Storm Priors: MODL-STORM
Creators: Sampurna Biswas - Department of Electrical and Computer Engineering, The University of Iowa, IA, USA
Hemant K Aggarwal - Department of Electrical and Computer Engineering, The University of Iowa, IA, USA
Sunrita Poddar - Department of Electrical and Computer Engineering, The University of Iowa, IA, USA
Mathews Jacob - Department of Electrical and Computer Engineering, The University of Iowa, IA, USA
Resource Type: Conference proceeding
Publication Details: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol.2018-, pp.6533-6537
DOI: 10.1109/ICASSP.2018.8462637
PMID: 33716574
NLM abbreviation: Proc IEEE Int Conf Acoust Speech Signal Process
ISSN: 1520-6149
eISSN: 2379-190X
Publisher: IEEE
Language: English
Date published: 04/2018
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute; Radiation Oncology
Record Identifier: 9984070503202771

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