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:10.48550/arxiv.2501.06595

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Fast multi-contrast MRI using joint multiscale energy model

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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

ArXiv.org

Cornell University

01/11/2025

DOI: 10.48550/arxiv.2501.06595

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Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

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.

Details

Title: Subtitle: Fast multi-contrast MRI using joint multiscale energy model
Creators: Nima Yaghoobi
Jyothi Rikhab Chand
Yan Chen
Steve R Kecskemeti
James H Holmes
Mathews Jacob
Resource Type: Preprint
Publication Details: ArXiv.org
DOI: 10.48550/arxiv.2501.06595
ISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 01/11/2025
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Technology Institute; Iowa Neuroscience Institute
Record Identifier: 9984773419402771

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