Quantifying Brain [18F]FDG Uptake Noninvasively by Combining Medical Health Records and Dynamic PET Imaging Data

SummaryNon-specific accumulation of 18F-FDG by both tumour and inflammatory lesions can make diagnostic analysis difficult. Our aim was to explore the difference in 18F-FDG uptake kinetics between tumour and inflammatory cells. To this end, we investigated VX2 tumour lesions and inflammatory lesions in rabbits. Methods: Six rabbits with VX2 tumour cells transplanted into one forelimb muscle and inflammatory lesions induced by turpentine oil in the contralateral forelimb were scanned for 60 minutes post 18F-FDG injection. Imaging data was analyzed with the standard 2-tissue-compartment model. Parameters, VB, Ki, K1, k2, k3, k4, were compared between tumour and inflammatory lesions. SUV and dual time scan methods were also compared in the experiment. Results: Time activity curves of VX2 tumour lesions showed a characteristic pattern of gradually increasing 18F-FDG uptake up to 60 min, whereas, 18F-FDG uptake in inflammatory lesions increased more slowly than in tumours. Parameters estimated from the uptake process showed that forward transport constant, K1, and influx constant, Ki, values in VX2 tumour lesions (0.186 ± 0.053 and 0.048 ± 0.014, respectively) was significantly higher than that in inflammatory lesions (0.129 ± 0.024 and 0.022 ± 0.007, respectively) (p < 0.05). In contrast, mean values of VB, k2, k3 and k4 derived from VX2 tumours were not significantly different from that of inflammatory lesions. SUVs at 60 minutes post 18F-FDG injection were also significantly higher in the VX2 tumor lesions than in the inflammatory lesions. Retention index (RI) was not significantly different between VX2 tumours and inflammatory lesions (1.134 ± 0.076 vs. 1.060 ± 0.058, p > 0.05). Conclusion: Different kinetic parameters (Ki, K1, k3) exist between inflammatory and tumour lesions.

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An iterative image-based inter-frame motion compensation method for dynamic brain PET imaging

Physics in Medicine and Biology ◽

10.1088/1361-6560/ac4a8f ◽

2022 ◽

Author(s):

Tao Sun ◽

Yaping Wu ◽

Yan Bai ◽

Zhenguo Wang ◽

Chushu Shen ◽

...

Keyword(s):

Image Quality ◽

Pet Imaging ◽

Motion Compensation ◽

Involuntary Movement ◽

Motion Artifacts ◽

Dynamic Pet ◽

Brain Pet ◽

18F Fdg ◽

Frame Motion ◽

Inter Frame

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 18F-FDG, 18F-Fallypride and 18F-AV45. Fifteen dynamic 18F-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 facilitates the applications of dynamic PET in clinics and research.

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