ArticleNeuroImage2021
Approximating anatomically-guided PET reconstruction in image space using a convolutional neural network.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Glycolic Acid-Guided Intelligent Neurovascular Imaging: A Cross-Scale Platform for Real-Time Neuroprotection and Adaptive Stroke Imaging.Journal of clinical medicine · 2026Article
- DG-DiT: Dual-Branch Gating Diffusion Transformer for Multi-Tracer and Multi-Scanner Brain PET Image Denoising.IEEE transactions on radiation and plasma medical sciences · 2025Article
- Improving the Quality of Brain PET Images Using MR-Guided PET Reconstruction Technique With Integrated PET/MR.Human brain mapping · 2025Article
- Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs.Magma (New York, N.Y.) · 2024Review
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Deep learning-based PET image denoising and reconstruction: a review.Radiological physics and technology · 2024Review
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.ArXiv · 2024Article
- SMART-PET: a Self-SiMilARiTy-aware generative adversarial framework for reconstructing low-count [18F]-FDG-PET brain imaging.Frontiers in nuclear medicine · 2024Article
- PET image denoising based on denoising diffusion probabilistic model.European journal of nuclear medicine and molecular imaging · 2024Article
- Dose reduction in dynamic synaptic vesicle glycoprotein 2A PET imaging using artificial neural networks.Physics in medicine and biology · 2023Article
- AI Transformers for Radiation Dose Reduction in Serial Whole-Body PET Scans.Radiology. Artificial intelligence · 2023Article
- Low-count whole-body PET/MRI restoration: an evaluation of dose reduction spectrum and five state-of-the-art artificial intelligence models.European journal of nuclear medicine and molecular imaging · 2023Article
- Current state of pediatric neuro-oncology imaging, challenges and future directions.Neoplasia (New York, N.Y.) · 2023Review
- Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement.European journal of nuclear medicine and molecular imaging · 2022Review
- Poster Presentations.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2022Article
- Super-resolution reconstruction for parallel-beam SPECT based on deep learning and transfer learning: a preliminary simulation study.Annals of translational medicine · 2022Article
- Application of artificial intelligence in brain molecular imaging.Annals of nuclear medicine · 2022Review
- Reconstruction-free positron emission imaging: Fact or fiction?Frontiers in nuclear medicine · 2022Article
- Machine Learning Algorithms in Neuroimaging: An Overview.Acta neurochirurgica. Supplement · 2022Review
- Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement.PET clinics · 2021Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
In the last two decades, it has been shown that anatomically-guided PET reconstruction can lead to improved bias-noise characteristics in brain PET imaging. However, despite promising results in simulations and first studies, anatomically-guided PET reconstructions are not yet available for use in routine clinical because of several reasons. In light of this, we investigate whether the improvements of anatomically-guided PET reconstruction methods can be achieved entirely in the image domain with a convolutional neural network (CNN). An entirely image-based CNN post-reconstruction approach has the advantage that no access to PET raw data is needed and, moreover, that the prediction times of trained CNNs are extremely fast on state of the art GPUs which will substantially facilitate the evaluation, fine-tuning and application of anatomically-guided PET reconstruction in real-world clinical settings. In this work, we demonstrate that anatomically-guided PET reconstruction using the asymmetric Bowsher prior can be well-approximated by a purely shift-invariant convolutional neural network in image space allowing the generation of anatomically-guided PET images in almost real-time. We show that by applying dedicated data augmentation techniques in the training phase, in which 16 [
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.