TagGen: Diffusion‐based generative model for cardiac MR tagging super resolution

Purpose The aim of the work is to develop a cascaded diffusion‐based super‐resolution model for low‐resolution (LR) MR tagging acquisitions, which is integrated with parallel imaging to achieve highly accelerated MR tagging while enhancing the tag grid quality of low‐resolution images. Methods We in...

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Veröffentlicht in:Magnetic resonance in medicine Jg. 94; H. 1; S. 362 - 372
Hauptverfasser: Sun, Changyu, Thornburgh, Cody, Wang, Yu, Kumar, Senthil, Altes, Talissa A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Wiley Subscription Services, Inc 01.07.2025
John Wiley and Sons Inc
Schlagworte:
Adult
Algorithms
Computer Processing and Modeling
Data acquisition
deep learning
Diffusion
diffusion generative model
Diffusion Magnetic Resonance Imaging - methods
Female
Generative adversarial networks
Heart - diagnostic imaging
Humans
Image acquisition
Image Interpretation, Computer-Assisted - methods
Image Processing, Computer-Assisted - methods
Image quality
Magnetic Resonance Imaging
Male
MR tagging
Retrospective Studies
Signal quality
Signal-To-Noise Ratio
super resolution
Synthetic data
Technical Note
deep learning
super resolution
diffusion generative model
MR tagging
ISSN:0740-3194, 1522-2594, 1522-2594
Online-Zugang:Volltext
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Zusammenfassung:Purpose The aim of the work is to develop a cascaded diffusion‐based super‐resolution model for low‐resolution (LR) MR tagging acquisitions, which is integrated with parallel imaging to achieve highly accelerated MR tagging while enhancing the tag grid quality of low‐resolution images. Methods We introduced TagGen, a diffusion‐based conditional generative model that uses low‐resolution MR tagging images as guidance to generate corresponding high‐resolution tagging images. The model was developed on 50 patients with long‐axis‐view, high‐resolution tagging acquisitions. During training, we retrospectively synthesized LR tagging images using an undersampling rate (R) of 3.3 with truncated outer phase‐encoding lines. During inference, we evaluated the performance of TagGen and compared it with REGAIN, a generative adversarial network–based super‐resolution model that was previously applied to MR tagging. In addition, we prospectively acquired data from 6 subjects with three heartbeats per slice using 10‐fold acceleration achieved by combining low‐resolution R = 3.3 with GRAPPA‐3 (generalized autocalibrating partially parallel acquisitions 3). Results For synthetic data (R = 3.3), TagGen outperformed REGAIN in terms of normalized root mean square error, peak signal‐to‐noise ratio, and structural similarity index (p < 0.05 for all). For prospectively 10‐fold accelerated data, TagGen provided better tag grid quality, signal‐to‐noise ratio, and overall image quality than REGAIN, as scored by two (blinded) radiologists (p < 0.05 for all). Conclusions We developed a diffusion‐based generative super‐resolution model for MR tagging images and demonstrated its potential to integrate with parallel imaging to reconstruct highly accelerated cine MR tagging images acquired in three heartbeats with enhanced tag grid quality.
Bibliographie:ObjectType-Article-1
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ISSN:0740-3194
1522-2594
1522-2594
DOI:10.1002/mrm.30422