ListmodeCNN: deep learning-based PET list-mode image reconstruction

Rui Hu; Yingying Li; Huafeng Liu

doi:10.1117/12.2647340

7 April 2023 ListmodeCNN: deep learning-based PET list-mode image reconstruction

Rui Hu, Yingying Li, Huafeng Liu

Author Affiliations +

Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 124631T (2023) https://doi.org/10.1117/12.2647340
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

PET image reconstruction direct from list-mode data can eliminate the storage of empty sinogram bins, preserve all the precision and accuracy of the large amount data of PET scanners. However, the traditional list-mode reconstruction methods, such as list-mode ML-EM algorithm always suffer from high level of noise due to the ill-conditioning of the PET reconstruction problem. In this paper, we proposed a novel deep learning based method for list-mode reconstruction. We first adopted a domain transfer function to convert the sensor domain data to image domain, then the Residual Attention Dense U-net was used to learn the reconstruction.The proposed RADU-net is based on the RDU-net structure, where the Attention Gate module is integrated into.Instead of concatenating the left side feature to the right side directly, the attention gated module takes advantage of the high level feature to guide the low level feature to realize the attention mechanism before doing the concatenation. Realistic PET acquisitions of 25 digital PET brain phantoms were simulated, generating noisy list-mode data, used for evaluation. Quantification results show that the proposed listmodeCNN can outperform the U-net, list-mode ML-EM as well as TV regularized ML-EM in terms of SSIM and PSNR.

Citation Download Citation

Rui Hu, Yingying Li, and Huafeng Liu "ListmodeCNN: deep learning-based PET list-mode image reconstruction", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 124631T (7 April 2023); https://doi.org/10.1117/12.2647340

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