An iterative image-based inter-frame motion compensation method for dynamic brain PET imaging. (2nd February 2022)

• Record Type: Journal Article
• Authors: Sun, Tao; Wu, Yaping; Bai, Yan; Wang, Zhenguo; Shen, Chushu; Wang, Wei; Li, Chenwei; Hu, Zhanli; Liang, Dong; Liu, Xin; Zheng, Hairong; Yang, Yongfeng; Wang, Meiyun
• Is Part Of: Physics in medicine & biology. Volume 67:Number 3(2022)
• Publication Date: 2022-02-02
• Subjects: motion correction -- kinetic modelling -- dynamic PET; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ac4a8f
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: As a non-invasive imaging tool, positron emission tomography (PET) plays an important role in brain science and disease research. Dynamic acquisition is one way of brain PET imaging. Its wide application in clinical research has often been hindered by practical challenges, such as patient involuntary movement, which could degrade both image quality and the accuracy of the quantification. This is even more obvious in scans of patients with neurodegeneration or mental disorders. Conventional motion compensation methods were either based on images or raw measured data, were shown to be able to reduce the effect of motion on the image quality. As for a dynamic PET scan, motion compensation can be challenging as tracer kinetics and relatively high noise can be present in dynamic frames. In this work, we propose an image-based inter-frame motion compensation approach specifically designed for dynamic brain PET imaging. Our method has an iterative implementation that only requires reconstructed images, based on which the inter-frame subject movement can be estimated and compensated. The method utilized tracer-specific kinetic modelling and can deal with simple and complex movement patterns. The synthesized phantom study showed that the proposed method can compensate for the simulated motion in scans with 18 F-FDG, 18 F-Fallypride and 18 F-AV45. Fifteen dynamic 18 F-FDG patient scans with motion artifacts were also processed. The quality of the recovered image was superior to the one of the non-corrected images and the corrected images with other image-based methods. The proposed method enables retrospective image quality control for dynamic brain PET imaging, hence facilitating the applications of dynamic PET in clinics and research.