ArticleRadiological physics and technology2022
Deep-learning-based fast TOF-PET image reconstruction using direction information.
Article in Radiological physics and technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Improved quantification in end-to-end deep learning FastPET reconstruction using multi-view histo-images of attenuation correction factors.IEEE transactions on radiation and plasma medical sciences · 2026Article
- Dual-domain transformer-based learned primal-dual reconstruction for PET imaging.Frontiers in nuclear medicine · 2026Article
- Deep learning-based time-of-flight (ToF) enhancement of non-ToF PET scans for different radiotracers.European journal of nuclear medicine and molecular imaging · 2025Article
- Two-step optimization for accelerating deep image prior-based PET image reconstruction.Radiological physics and technology · 2024Article
- The effects of back-projection variants in BPF-like TOF PET reconstruction using CNN filtration - Based on simulated and clinical brain data.Medical physics · 2024Article
- Deep learning-based PET image denoising and reconstruction: a review.Radiological physics and technology · 2024Review
- ParaPET: non-invasive deep learning method for direct parametric brain PET reconstruction using histoimages.EJNMMI research · 2024Article
- Prior information-guided reconstruction network for positron emission tomography images.Quantitative imaging in medicine and surgery · 2023Article
- AI for PET image reconstruction.The British journal of radiology · 2023Review
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2 authors.
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Abstract
Although deep learning for application in positron emission tomography (PET) image reconstruction has attracted the attention of researchers, the image quality must be further improved. In this study, we propose a novel convolutional neural network (CNN)-based fast time-of-flight PET (TOF-PET) image reconstruction method to fully utilize the direction information of coincidence events. The proposed method inputs view-grouped histo-images into a 3D CNN as a multi-channel image to use the direction information of such events. We evaluated the proposed method using Monte Carlo simulation data obtained from a digital brain phantom. Compared with a case without direction information, the peak signal-to-noise ratio and structural similarity were improved by 1.2 dB and 0.02, respectively, at a coincidence time resolution of 300 ps. The calculation times of the proposed method were significantly lower than those of a conventional iterative reconstruction. These results indicate that the proposed method improves both the speed and image quality of a TOF-PET image reconstruction.
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