A Flow-based Truncated Denoising Diffusion Model for super-resolution Magnetic Resonance Spectroscopic Imaging.

Diffusion Models MR Spectroscopic Imaging Normalizing Flow Super-resolution

Journal

Medical image analysis
ISSN: 1361-8423
Titre abrégé: Med Image Anal
Pays: Netherlands
ID NLM: 9713490

Informations de publication

Date de publication:
27 Sep 2024
Historique:
received: 09 11 2023
revised: 23 06 2024
accepted: 23 09 2024
medline: 3 10 2024
pubmed: 3 10 2024
entrez: 1 10 2024
Statut: aheadofprint

Résumé

Magnetic Resonance Spectroscopic Imaging (MRSI) is a non-invasive imaging technique for studying metabolism and has become a crucial tool for understanding neurological diseases, cancers and diabetes. High spatial resolution MRSI is needed to characterize lesions, but in practice MRSI is acquired at low resolution due to time and sensitivity restrictions caused by the low metabolite concentrations. Therefore, there is an imperative need for a post-processing approach to generate high-resolution MRSI from low-resolution data that can be acquired fast and with high sensitivity. Deep learning-based super-resolution methods provided promising results for improving the spatial resolution of MRSI, but they still have limited capability to generate accurate and high-quality images. Recently, diffusion models have demonstrated superior learning capability than other generative models in various tasks, but sampling from diffusion models requires iterating through a large number of diffusion steps, which is time-consuming. This work introduces a Flow-based Truncated Denoising Diffusion Model (FTDDM) for super-resolution MRSI, which shortens the diffusion process by truncating the diffusion chain, and the truncated steps are estimated using a normalizing flow-based network. The network is conditioned on upscaling factors to enable multi-scale super-resolution. To train and evaluate the deep learning models, we developed a

Identifiants

DOI: 10.1016/j.media.2024.103358 PMID: 39353335
pubmed: 39353335
pii: S1361-8415(24)00283-4
doi: 10.1016/j.media.2024.103358
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

103358

Informations de copyright

Copyright © 2024. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: James S. Duncan is one of the Editors-in-Chief for Medical Image Analysis. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Siyuan Dong (S)

Department of Electrical Engineering, Yale University, New Haven, CT, USA. Electronic address: s.dong@yale.edu.

Zhuotong Cai (Z)

Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China. Electronic address: zhuotong.cai@yale.edu.

Gilbert Hangel (G)

Department of Biomedical Imaging and Image-Guided Therapy, Highfield MR Center, Medical University of Vienna, Vienna, Austria; Department of Neurosurgery, Medical University of Vienna, Vienna, Austria.

Wolfgang Bogner (W)

Department of Biomedical Imaging and Image-Guided Therapy, Highfield MR Center, Medical University of Vienna, Vienna, Austria.

Georg Widhalm (G)

Department of Neurosurgery, Medical University of Vienna, Vienna, Austria.

Yaqing Huang (Y)

Department of Pathology, Yale University, New Haven, CT, USA.

Qinghao Liang (Q)

Department of Biomedical Engineering, Yale University, New Haven, CT, USA.

Chenyu You (C)

Department of Electrical Engineering, Yale University, New Haven, CT, USA.

Chathura Kumaragamage (C)

Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.

Robert K Fulbright (RK)

Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.

Amit Mahajan (A)

Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.

Amin Karbasi (A)

Department of Electrical Engineering, Yale University, New Haven, CT, USA; Department of Computer Science, Yale University, New Haven, CT, USA; Department of Statistics and Data Science, Yale University, New Haven, CT, USA.

John A Onofrey (JA)

Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.

Robin A de Graaf (RA)

Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.

James S Duncan (JS)

Department of Electrical Engineering, Yale University, New Haven, CT, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA. Electronic address: james.duncan@yale.edu.

Classifications MeSH