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
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Abstract	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.
AbstractList	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. 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. 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. 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). 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). 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. 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.PURPOSEThe 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.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).METHODSWe 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).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).RESULTSFor 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).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.CONCLUSIONSWe 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.
Author	Sun, Changyu Wang, Yu Altes, Talissa A. Kumar, Senthil Thornburgh, Cody
AuthorAffiliation	1 Department of Chemical and Biomedical Engineering University of Missouri Columbia Missouri USA 3 Department of Medicine University of Missouri Columbia Missouri USA 2 Department of Radiology University of Missouri Columbia Missouri USA
AuthorAffiliation_xml	– name: 2 Department of Radiology University of Missouri Columbia Missouri USA – name: 3 Department of Medicine University of Missouri Columbia Missouri USA – name: 1 Department of Chemical and Biomedical Engineering University of Missouri Columbia Missouri USA
Author_xml	– sequence: 1 givenname: Changyu orcidid: 0000-0001-8102-7130 surname: Sun fullname: Sun, Changyu email: csyfc@missouri.edu organization: University of Missouri – sequence: 2 givenname: Cody surname: Thornburgh fullname: Thornburgh, Cody organization: University of Missouri – sequence: 3 givenname: Yu surname: Wang fullname: Wang, Yu organization: University of Missouri – sequence: 4 givenname: Senthil surname: Kumar fullname: Kumar, Senthil organization: University of Missouri – sequence: 5 givenname: Talissa A. surname: Altes fullname: Altes, Talissa A. organization: University of Missouri
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Snippet	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... 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... 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...
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SubjectTerms	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
Title	TagGen: Diffusion‐based generative model for cardiac MR tagging super resolution
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Volume	94
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