Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Jan:99:103358.
doi: 10.1016/j.media.2024.103358. Epub 2024 Sep 27.

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

Siyuan Dong  1 , Zhuotong Cai  2 , Gilbert Hangel  3 , Wolfgang Bogner  4 , Georg Widhalm  5 , Yaqing Huang  6 , Qinghao Liang  7 , Chenyu You  8 , Chathura Kumaragamage  9 , Robert K Fulbright  9 , Amit Mahajan  9 , Amin Karbasi  10 , John A Onofrey  9 , Robin A de Graaf  11 , James S Duncan  12
Affiliations

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

Siyuan Dong et al. Med Image Anal. 2025 Jan.

Abstract

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 1H-MRSI dataset acquired from 25 high-grade glioma patients. We demonstrate that FTDDM outperforms existing generative models while speeding up the sampling process by over 9-fold compared to the baseline diffusion model. Neuroradiologists' evaluations confirmed the clinical advantages of our method, which also supports uncertainty estimation and sharpness adjustment, extending its potential clinical applications.

Keywords: Diffusion Models; MR Spectroscopic Imaging; Normalizing Flow; Super-resolution.

PubMed Disclaimer

Conflict of interest statement

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.

References

    1. Ardizzone L, Kruse J, Rother C, Köthe U, 2018. Analyzing inverse problems with invertible neural networks, in: International Conference on Learning Representations.
    1. Bogner W, Otazo R, Henning A, 2021. Accelerated mr spectroscopic imaging—a review of current and emerging techniques. NMR in Biomedicine 34, e4314. - PMC - PubMed
    1. Chen Z, Dong S, Sun S, Chen X, Liu Y, Chen T, 2024a. Mri reconstruction based on generative models. US Patent App. 17/891,702.
    1. Chen Z, Dong S, Sun S, Chen X, Liu Y, Chen T, 2024b. Systems and methods for processing medical images with invertible neural networks. US Patent App. 17/891,668.
    1. Chow LS, Rajagopal H, Paramesran R, Initiative ADN, et al., 2016. Correlation between subjective and objective assessment of magnetic resonance (mr) images. Magnetic resonance imaging 34, 820–831. - PubMed

LinkOut - more resources