Filling of incomplete sinograms from sparse PET detector configurations using a residual U-Net

• URL: http://arxiv.org/abs/2506.19600v1
• Date: Tue, 24 Jun 2025 13:10:44 GMT
• Title: Filling of incomplete sinograms from sparse PET detector configurations using a residual U-Net
• Authors: Klara Leffler, Luigi Tommaso Luppino, Samuel Kuttner, Karin Söderkvist, Jan Axelsson,
• Abstract summary: Long axial field-of-view PET scanners offer increased field-of-view and sensitivity compared to traditional PET scanners.<n>A significant cost is associated with the densely packed photodetectors required for the extended-coverage systems.<n>Alternative sparse system configurations have been proposed, allowing an extended field-of-view PET design with detector costs similar to a standard PET system.
• Score: 0.6640968473398456
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long axial field-of-view PET scanners offer increased field-of-view and sensitivity compared to traditional PET scanners. However, a significant cost is associated with the densely packed photodetectors required for the extended-coverage systems, limiting clinical utilisation. To mitigate the cost limitations, alternative sparse system configurations have been proposed, allowing an extended field-of-view PET design with detector costs similar to a standard PET system, albeit at the expense of image quality. In this work, we propose a deep sinogram restoration network to fill in the missing sinogram data. Our method utilises a modified Residual U-Net, trained on clinical PET scans from a GE Signa PET/MR, simulating the removal of 50% of the detectors in a chessboard pattern (retaining only 25% of all lines of response). The model successfully recovers missing counts, with a mean absolute error below two events per pixel, outperforming 2D interpolation in both sinogram and reconstructed image domain. Notably, the predicted sinograms exhibit a smoothing effect, leading to reconstructed images lacking sharpness in finer details. Despite these limitations, the model demonstrates a substantial capacity for compensating for the undersampling caused by the sparse detector configuration. This proof-of-concept study suggests that sparse detector configurations, combined with deep learning techniques, offer a viable alternative to conventional PET scanner designs. This approach supports the development of cost-effective, total body PET scanners, allowing a significant step forward in medical imaging technology.

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