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Purpose To develop a method of biologically guided deep learning for post-radiation 18 FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information. Methods Based on the classic reaction–diffusion mechanism, a novel biological model was proposed using a partial differential equation that incorporates spatial radiation dose distribution as a patient-specific treatment information variable. A 7-layer encoder–decoder-based convolutional neural network (CNN) was designed and trained to learn the proposed biological model. As such, the model could generate post-radiation 18 FDG-PET image outcome predictions with breakdown biological components for enhanced explainability. The proposed method was developed using 64 oropharyngeal patients with paired 18 FDG-PET studies before and after 20-Gy delivery (2 Gy/day fraction) by intensity-modulated radiotherapy (IMRT). In a two-branch deep learning execution, the proposed CNN learns specific terms in the biological model from paired 18 FDG-PET images and spatial dose distribution in one branch, and the biological model generates post-20-Gy 18 FDG-PET image prediction in the other branch. As in 2D execution, 718/233/230 axial slices from 38/13/13 patients were used for training/validation/independent test. The prediction image results in test cases were compared with the ground-truth results quantitatively. Results The proposed method successfully generated post-20-Gy 18 FDG-PET image outcome prediction with breakdown illustrations of biological model components. Standardized uptake value (SUV) mean values in 18 FDG high-uptake regions of predicted images (2.45 ± 0.25) were similar to ground-truth results (2.51 ± 0.33). In 2D-based Gamma analysis, the median/mean Gamma Index (<1) passing rate of test images was 96.5%/92.8% using the 5%/5 mm criterion; such result was improved to 99.9%/99.6% when 10%/10 mm was adopted. Conclusion The developed biologically guided deep learning method achieved post-20-Gy 18 FDG-PET image outcome predictions in good agreement with ground-truth results. With the breakdown biological modeling components, the outcome image predictions could be used in adaptive radiotherapy decision-making to optimize personalized plans for the best outcome in the future.
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Computational Analysis of a Light-Weight SUVr Processing Technique for Neuroimaging Alzheimer’s Disease
The Standard Uptake Value (SUV) is conventionally calculated using the ratio of the injected PET radiotracer dose and subject body weight (Binj) . SUVs are used to obtain SUV ratios (SUVr), an important metric in many Alzheimer's Disease (AD) neuroimaging studies. However, SUVr can be obtained using only neuroimaging data, bypassing the need for Binj . This paper proposes the SUVr-LightWeight (SUVr-LW) algorithm which is not reliant on clinical data and instead focuses on PET intensity values. The SUVr-LW was evaluated using the Centiloid Project Florebetaben (FBB) subject cohort and reached a linear regression slope of 0.98, while the healthy control subjects produced a slope of 0.87.
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- PAR ID:
- 10458805
- Date Published:
- Journal Name:
- 2022 International Conference on Computational Science and Computational Intelligence (CSCI)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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