TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction.

Autor: Guo X; Department of Biomedical Engineering, Yale University, New Haven, CT, USA. Electronic address: xueqi.guo@yale.edu., Shi L; IBM Research, San Jose, CA, USA., Chen X; Department of Biomedical Engineering, Yale University, New Haven, CT, USA., Liu Q; Department of Biomedical Engineering, Yale University, New Haven, CT, USA., Zhou B; Department of Biomedical Engineering, Yale University, New Haven, CT, USA., Xie H; Department of Biomedical Engineering, Yale University, New Haven, CT, USA., Liu YH; Department of Internal Medicine, Yale University, New Haven, CT, USA., Palyo R; Yale New Haven Hospital, New Haven, CT, USA., Miller EJ; Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Internal Medicine, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA., Sinusas AJ; Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Internal Medicine, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA., Staib L; Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA., Spottiswoode B; Siemens Medical Solutions USA, Inc., Knoxville, TN, USA., Liu C; Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA. Electronic address: chi.liu@yale.edu., Dvornek NC; Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA. Electronic address: nicha.dvornek@yale.edu.
Jazyk: angličtina
Zdroj: Medical image analysis [Med Image Anal] 2024 Aug; Vol. 96, pp. 103190. Date of Electronic Publication: 2024 May 07.
DOI: 10.1016/j.media.2024.103190
Abstrakt: Inter-frame motion in dynamic cardiac positron emission tomography (PET) using rubidium-82 ( 82 Rb) myocardial perfusion imaging impacts myocardial blood flow (MBF) quantification and the diagnosis accuracy of coronary artery diseases. However, the high cross-frame distribution variation due to rapid tracer kinetics poses a considerable challenge for inter-frame motion correction, especially for early frames where intensity-based image registration techniques often fail. To address this issue, we propose a novel method called Temporally and Anatomically Informed Generative Adversarial Network (TAI-GAN) that utilizes an all-to-one mapping to convert early frames into those with tracer distribution similar to the last reference frame. The TAI-GAN consists of a feature-wise linear modulation layer that encodes channel-wise parameters generated from temporal information and rough cardiac segmentation masks with local shifts that serve as anatomical information. Our proposed method was evaluated on a clinical 82 Rb PET dataset, and the results show that our TAI-GAN can produce converted early frames with high image quality, comparable to the real reference frames. After TAI-GAN conversion, the motion estimation accuracy and subsequent myocardial blood flow (MBF) quantification with both conventional and deep learning-based motion correction methods were improved compared to using the original frames. The code is available at https://github.com/gxq1998/TAI-GAN.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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