Version 1
: Received: 1 November 2024 / Approved: 1 November 2024 / Online: 1 November 2024 (11:10:41 CET)
How to cite:
Shah, J.; Che, Y.; Sohankar, J.; Luo, J.; Li, B.; Su, Y.; Wu, T. Enhancing PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion Models. Preprints2024, 2024110051. https://doi.org/10.20944/preprints202411.0051.v1
Shah, J.; Che, Y.; Sohankar, J.; Luo, J.; Li, B.; Su, Y.; Wu, T. Enhancing PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion Models. Preprints 2024, 2024110051. https://doi.org/10.20944/preprints202411.0051.v1
Shah, J.; Che, Y.; Sohankar, J.; Luo, J.; Li, B.; Su, Y.; Wu, T. Enhancing PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion Models. Preprints2024, 2024110051. https://doi.org/10.20944/preprints202411.0051.v1
APA Style
Shah, J., Che, Y., Sohankar, J., Luo, J., Li, B., Su, Y., & Wu, T. (2024). Enhancing PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion Models. Preprints. https://doi.org/10.20944/preprints202411.0051.v1
Chicago/Turabian Style
Shah, J., Yi Su and Teresa Wu. 2024 "Enhancing PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion Models" Preprints. https://doi.org/10.20944/preprints202411.0051.v1
Abstract
Amyloid PET imaging plays a crucial role in the diagnosis and research of Alzheimer's disease (AD), allowing non-invasive detection of amyloid-β plaques in the brain. However, the low spatial resolution of PET scans limits accurate quantification of amyloid deposition due to partial volume effects (PVE). In this study, we propose a novel approach to addressing PVE using a latent diffusion model for resolution recovery (LDM-RR) of PET imaging. We leverage a synthetic data generation pipeline to create high-resolution PET digital phantoms for model training. The proposed LDM-RR model incorporates a weighted combination of L1, L2, and MS-SSIM losses at both noise and image scales to enhance MRI-guided reconstruction. We evaluated the model's performance in improving statistical power for detecting longitudinal changes and enhancing agreement between amyloid PET measurements from different tracers. Results demonstrate that the LDM-RR approach significantly improves PET quantification accuracy, reduces inter-tracer variability, and enhances the detection of subtle changes in amyloid deposition over time. We show that deep learning has the potential to improve PET quantification in AD, effectively contributing to early detection and monitoring of disease progression.
Keywords
partial volume correction (PVC); positron emission tomography; amyloid; deep learning; diffusion models; medical image super-resolution
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
