# CNN-Based PET Sinogram Repair to Mitigate Defective Block Detectors

arXiv: https://arxiv.org/abs/1908.10252 · HTML: https://mlsanity.com/paper/1908.10252/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2019.

## Built on by (most cited first, 5 of 5)

- [Cross-domain Iterative Network for Simultaneous Denoising, Limited-angle Reconstruction, and Attenuation Correction of Low-dose Cardiac SPECT](https://mlsanity.com/paper/2305.10326/) (2023)
- [Joint Denoising and Few-angle Reconstruction for Low-dose Cardiac SPECT Using a Dual-domain Iterative Network with Adaptive Data Consistency](https://mlsanity.com/paper/2305.10328/) (2023)
- [Dual-Domain Coarse-to-Fine Progressive Estimation Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT](https://mlsanity.com/paper/2401.13140/) (2024)
- [Filling of incomplete sinograms from sparse PET detector configurations using a residual U-Net](https://mlsanity.com/paper/2506.19600/) (2025)
- [UniSino: Physics-Driven Foundational Model for Universal CT Sinogram Standardization](https://mlsanity.com/paper/2508.17816/) (2025)

## Abstract (arXiv)

Positron emission tomography (PET) scanners continue to increase sensitivity and axial coverage by adding an ever expanding array of block detectors. As they age, one or more block detectors may lose sensitivity due to a malfunction or component failure. The sinogram data missing as a result thereof can lead to artifacts and other image degradations. We propose to mitigate the effects of malfunctioning block detectors by carrying out sinogram repair using a deep convolutional neural network. Experiments using whole-body patient studies with varying amounts of raw data removed are used to show that the neural network significantly outperforms previously published methods with respect to normalized mean squared error for raw sinograms, a multi-scale structural similarity measure for reconstructed images and with regard to quantitative accuracy.