Fast Multi-Contrast MRI Using Joint Multiscale Energy Model

Nima Yaghoobi; Jyothi Rikhab Chand; Yan Chen; Steve R. Kecskemeti; James H. Holmes; Mathews Jacob

doi:10.1109/ISBI60581.2025.10981204

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Fast Multi-Contrast MRI Using Joint Multiscale Energy Model

Conference proceeding

Open access

Fast Multi-Contrast MRI Using Joint Multiscale Energy Model

Nima Yaghoobi, Jyothi Rikhab Chand, Yan Chen, Steve R. Kecskemeti, James H. Holmes and Mathews Jacob

Proceedings (International Symposium on Biomedical Imaging), pp.1-5

04/14/2025

DOI: 10.1109/ISBI60581.2025.10981204

PMCID: PMC12381937

PMID: 40881624

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https://pmc.ncbi.nlm.nih.gov/articles/PMC12381937/View

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Abstract

The acquisition of 3D multicontrast MRI data with good isotropic spatial resolution is challenged by lengthy scan times. In this work, we introduce a CNN-based multiscale energy model to learn the joint probability distribution of the multi-contrast images. The joint recovery of the contrasts from undersampled data is posed as a maximum a posteriori estimation scheme, where the learned energy serves as the prior. We use a majorize-minimize algorithm to solve the optimization scheme. The proposed model leverages the redundancies across different contrasts to improve image fidelity. The proposed scheme is observed to preserve fine details and contrast, offering sharper reconstructions compared to reconstruction methods that independently recover the contrasts. While we focus on 3D MPNRAGE acquisitions in this work, the proposed approach is generalizable to arbitrary multi-contrast settings.

Magnetic Resonance Imaging

Energy based model

Image reconstruction

Plug-and-play

Probability distribution

Re-construction

Reconstruction algorithms

Redundancy

Solid modeling

Spatial resolution

Three-dimensional displays

Velocity measurement

Volume measurement

Details

Title: Subtitle: Fast Multi-Contrast MRI Using Joint Multiscale Energy Model
Creators: Nima Yaghoobi - University of Virginia
Jyothi Rikhab Chand - University of Virginia
Yan Chen - University of Virginia
Steve R. Kecskemeti - University of Wisconsin–Madison
James H. Holmes - University of Iowa
Mathews Jacob - University of Virginia
Resource Type: Conference proceeding
Publication Details: Proceedings (International Symposium on Biomedical Imaging), pp.1-5
DOI: 10.1109/ISBI60581.2025.10981204
PMID: 40881624
PMCID: PMC12381937
eISSN: 1945-8452
Publisher: IEEE
Grant note: ROI-AG067078,ROI-AG087159,ROI-HD108868,P50 HD103556 / NIH (10.13039/100000002)
Language: English
Date published: 04/14/2025
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering
Record Identifier: 9984824196002771

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