Unsupervised generation of pseudo normal PET from MRI with diffusion model for epileptic focus localization

Wentao Chen; Jiwei Li; Xichen Xu; Hui Huang; Siyu Yuan; Miao Zhang; Tianming Xu; Jie Luo; Weimin Zhou

doi:10.1117/12.3008266

2 April 2024 Unsupervised generation of pseudo normal PET from MRI with diffusion model for epileptic focus localization

Wentao Chen, Jiwei Li, Xichen Xu, Hui Huang, Siyu Yuan, Miao Zhang, Tianming Xu, Jie Luo, Weimin Zhou

Author Affiliations +

Proceedings Volume 12930, Medical Imaging 2024: Clinical and Biomedical Imaging; 1293027 (2024) https://doi.org/10.1117/12.3008266
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

[¹⁸F]fluorodeoxyglucose (FDG) Positron Emission Tomography (PET) has emerged as a crucial tool in identifying the epileptic focus, especially in cases where Magnetic Resonance Imaging (MRI) diagnosis yields indeterminate results. FDG PET can provide the metabolic information of glucose and help identify abnormal areas that are not easily found through MRI. However, the effectiveness of FDG PET-based assessment and diagnosis depends on the selection of a healthy control group. The healthy control group typically consists of healthy individuals similar to epilepsy patients in terms of age, gender, and other aspects for providing normal FDG PET data, which will be used as a reference for enhancing the accuracy and reliability of the epilepsy diagnosis. However, significant challenges arise when a healthy PET control group is unattainable. Yaakub et al. have previously introduced a Pix2PixGAN-based method for MRI to PET translation. This method used paired MRI and FDG PET scans from healthy individuals for training, and produced pseudo normal FDG PET images from patient MRIs that are subsequently used for lesion detection. However, this approach requires a large amount of high-quality, paired MRI and PET images from healthy control subjects, which may not always be available. In this study, we investigated unsupervised learning methods for unpaired MRI to PET translation for generating pseudo normal FDG PET for epileptic focus localization. Two deep learning methods, CycleGAN and SynDiff, were employed, and we found that diffusion-based method achieved improved performance in accurately localizing the epileptic focus.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wentao Chen, Jiwei Li, Xichen Xu, Hui Huang, Siyu Yuan, Miao Zhang, Tianming Xu, Jie Luo, and Weimin Zhou "Unsupervised generation of pseudo normal PET from MRI with diffusion model for epileptic focus localization", Proc. SPIE 12930, Medical Imaging 2024: Clinical and Biomedical Imaging, 1293027 (2 April 2024); https://doi.org/10.1117/12.3008266

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