Multi-Task Deep Learning and Uncertainty Estimation for Pet Head Motion Correction
- Resource Type
- Conference
- Authors
- Lieffrig, Eleonore V.; Zeng, Tianyi; Zhang, Jiazhen; Fontaine, Kathryn; Fang, Xi; Revilla, Enette; Lu, Yihuan; Onofrey, John A.
- Source
- 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) Biomedical Imaging (ISBI), 2023 IEEE 20th International Symposium on. :1-5 Apr, 2023
- Subject
- Bioengineering
Computing and Processing
Photonics and Electrooptics
Signal Processing and Analysis
Deep learning
Performance evaluation
Head
Uncertainty
Tracking
Supervised learning
Predictive models
Multi-task Learning
Deep Learning
Motion Correction
Uncertainty Evaluation
PET
Brain
- Language
- ISSN
- 1945-8452
Head motion occurring during brain positron emission tomography images acquisition leads to a decrease in image quality and induces quantification errors. We have previously introduced a Deep Learning Head Motion Correction (DL-HMC) method based on supervised learning of gold-standard Polaris Vicra motion tracking device and showed the potential of this method. In this study, we upgrade our network to a multi-task architecture in order to include image appearance prediction in the learning process. This multi-task Deep Learning Head Motion Correction (mtDL-HMC) model was trained on 21 subjects and showed enhanced motion prediction performance compared to our previous DL-HMC method on both quantitative and qualitative results for 5 testing subjects. We also evaluate the trustworthiness of network predictions by performing Monte Carlo Dropout at inference on testing subjects. We discard the data associated with a great motion prediction uncertainty and show that this does not harm the quality of reconstructed images, and can even improve it.