Dose-aware Diffusion Model for 3D PET Image Denoising: Multi-institutional Validation with Reader Study and Real Low-dose Data
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arXiv
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| Natura: | Preprint |
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2024
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| author | Xie, Huidong Gan, Weijie Bayerlein, Reimund Zhou, Bo Chen, Ming-Kai Kulon, Michal Boustani, Annemarie Ko, Kuan-Yin Wang, Der-Shiun Spencer, Benjamin A. Ji, Wei Chen, Xiongchao Liu, Qiong Guo, Xueqi Xia, Menghua Zhou, Yinchi Liu, Hui Guo, Liang An, Hongyu Kamilov, Ulugbek S. Wang, Hanzhong Li, Biao Rominger, Axel Shi, Kuangyu Wang, Ge Badawi, Ramsey D. Liu, Chi |
| author_facet | Xie, Huidong Gan, Weijie Bayerlein, Reimund Zhou, Bo Chen, Ming-Kai Kulon, Michal Boustani, Annemarie Ko, Kuan-Yin Wang, Der-Shiun Spencer, Benjamin A. Ji, Wei Chen, Xiongchao Liu, Qiong Guo, Xueqi Xia, Menghua Zhou, Yinchi Liu, Hui Guo, Liang An, Hongyu Kamilov, Ulugbek S. Wang, Hanzhong Li, Biao Rominger, Axel Shi, Kuangyu Wang, Ge Badawi, Ramsey D. Liu, Chi |
| contents | Reducing scan times, radiation dose, and enhancing image quality for lower-performance scanners, are critical in low-dose PET imaging. Deep learning techniques have been investigated for PET image denoising. However, existing models have often resulted in compromised image quality when achieving low-count/low-dose PET and have limited generalizability to different image noise-levels, acquisition protocols, and patient populations. Recently, diffusion models have emerged as the new state-of-the-art generative model to generate high-quality samples and have demonstrated strong potential for medical imaging tasks. However, for low-dose PET imaging, existing diffusion models failed to generate consistent 3D reconstructions, unable to generalize across varying noise-levels, often produced visually-appealing but distorted image details, and produced images with biased tracer uptake. Here, we develop DDPET-3D, a dose-aware diffusion model for 3D low-dose PET imaging to address these challenges. Collected from 4 medical centers globally with different scanners and clinical protocols, we evaluated the proposed model using a total of 9,783 18F-FDG studies with low-dose levels ranging from 1% to 50%. With a cross-center, cross-scanner validation, the proposed DDPET-3D demonstrated its potential to generalize to different low-dose levels, different scanners, and different clinical protocols. As confirmed with reader studies performed by board-certified nuclear medicine physicians, experienced readers judged the images to be similar or superior to the full-dose images and previous DL baselines based on qualitative visual impression. Lesion-level quantitative accuracy was evaluated using a Monte Carlo simulation study and a lesion segmentation network. The presented results show the potential to achieve low-dose PET while maintaining image quality. Real low-dose scans was also included for evaluation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_12996 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dose-aware Diffusion Model for 3D PET Image Denoising: Multi-institutional Validation with Reader Study and Real Low-dose Data Xie, Huidong Gan, Weijie Bayerlein, Reimund Zhou, Bo Chen, Ming-Kai Kulon, Michal Boustani, Annemarie Ko, Kuan-Yin Wang, Der-Shiun Spencer, Benjamin A. Ji, Wei Chen, Xiongchao Liu, Qiong Guo, Xueqi Xia, Menghua Zhou, Yinchi Liu, Hui Guo, Liang An, Hongyu Kamilov, Ulugbek S. Wang, Hanzhong Li, Biao Rominger, Axel Shi, Kuangyu Wang, Ge Badawi, Ramsey D. Liu, Chi Image and Video Processing Reducing scan times, radiation dose, and enhancing image quality for lower-performance scanners, are critical in low-dose PET imaging. Deep learning techniques have been investigated for PET image denoising. However, existing models have often resulted in compromised image quality when achieving low-count/low-dose PET and have limited generalizability to different image noise-levels, acquisition protocols, and patient populations. Recently, diffusion models have emerged as the new state-of-the-art generative model to generate high-quality samples and have demonstrated strong potential for medical imaging tasks. However, for low-dose PET imaging, existing diffusion models failed to generate consistent 3D reconstructions, unable to generalize across varying noise-levels, often produced visually-appealing but distorted image details, and produced images with biased tracer uptake. Here, we develop DDPET-3D, a dose-aware diffusion model for 3D low-dose PET imaging to address these challenges. Collected from 4 medical centers globally with different scanners and clinical protocols, we evaluated the proposed model using a total of 9,783 18F-FDG studies with low-dose levels ranging from 1% to 50%. With a cross-center, cross-scanner validation, the proposed DDPET-3D demonstrated its potential to generalize to different low-dose levels, different scanners, and different clinical protocols. As confirmed with reader studies performed by board-certified nuclear medicine physicians, experienced readers judged the images to be similar or superior to the full-dose images and previous DL baselines based on qualitative visual impression. Lesion-level quantitative accuracy was evaluated using a Monte Carlo simulation study and a lesion segmentation network. The presented results show the potential to achieve low-dose PET while maintaining image quality. Real low-dose scans was also included for evaluation. |
| title | Dose-aware Diffusion Model for 3D PET Image Denoising: Multi-institutional Validation with Reader Study and Real Low-dose Data |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2405.12996 |