Non-Invasive Quantification of the Brain [¹⁸F]FDG-PET Using Inferred Blood Input Function Learned From Total-Body Data With Physical Constraint

Wang, Zhenguo; Wu, Yaping; Xia, Zeheng; Chen, Xinyi; Li, Xiaochen; Bai, Yan; Zhou, Yun; Liang, Dong; Zheng, Hairong; Yang, Yongfeng; Wang, Shanshan; Wang, Meiyun; Sun, Tao

doi:10.1109/tmi.2024.3368431

Cited by 2 publications

(1 citation statement)

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“…Wang et al and Lv and Xi [139,140] attempted to construct a deep progressive learning method for shortaxis PET image reconstruction in which the regularization process improved the image quality by running the data through a pre-trained neural network at each iteration. Another potentially useful application is one-toone mapping of TB-PET data with dynamic brain imaging and IDIF sampled at the aorta [141]. The IDIF at the aorta can be extracted non-invasively from the data acquired on a short-axial FOV scanner.…”

Section: Future Directionsmentioning

confidence: 99%

Current progress and future perspectives in total‐body PET imaging, part I: Data processing and analysis

Sun,

Chen,

Liu

et al. 2024

iRADIOLOGY

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Total‐body positron emission tomography (TB‐PET) has ultra‐high sensitivity and the unique ability to conduct dynamic imaging of the entire body. Both the hardware configuration and the data acquired from a TB‐PET scanner differ from those of the conventional short axial field‐of‐view scanners. Therefore, various aspects concerning data processing need careful consideration when implementing TB‐PET in clinical settings. Additionally, advances in data analysis are needed to fully uncover the potential of these systems. Although some progress has been achieved, further research and innovation in scan data management are necessary. In this report, we provide a comprehensive overview of the current progress, challenges, and possible future directions for TB‐PET data processing and analysis. For a review of clinical applications, please find the other review accompanying this paper.

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Section: Future Directionsmentioning

confidence: 99%