ArticleIEEE transactions on radiation and plasma medical sciences2021
A Learned Reconstruction Network for SPECT Imaging.
Article in IEEE transactions on radiation and plasma medical sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 28 citations in OpenAlex.
- Artificial Intelligence Across the Cancer Theranostics Workflow: Critical Appraisal of Current Evidence and Future Clinical Translation.Molecular imaging and biology · 2026Review
- Super-Resolution Reconstruction and Detector Geometric Error Correction for Parallel-Beam Low-Resolution Multi-Detector SPECT: A Proof of Concept.Tomography (Ann Arbor, Mich.) · 2026Article
- A review of state-of-the-art resolution improvement techniques in SPECT imaging.EJNMMI physics · 2025Review
- Investigation of a deep learning-based reconstruction approach utilizing dual-view projection for myocardial perfusion SPECT imaging.American journal of nuclear medicine and molecular imaging · 2025Article
- Training End-to-End Unrolled Iterative Neural Networks for SPECT Image Reconstruction.IEEE transactions on radiation and plasma medical sciences · 2023Article
- Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation.European journal of nuclear medicine and molecular imaging · 2022Review
- Generation of Digital Brain Phantom for Machine Learning Application of Dopamine Transporter Radionuclide Imaging.Diagnostics (Basel, Switzerland) · 2022Article
- Dielectric Breast Phantoms by Generative Adversarial Network.IEEE transactions on antennas and propagation · 2022Article
- Analysis of a deep learning-based method for generation of SPECT projections based on a large Monte Carlo simulated dataset.EJNMMI physics · 2022Article
- Super-resolution reconstruction for parallel-beam SPECT based on deep learning and transfer learning: a preliminary simulation study.Annals of translational medicine · 2022Article
- Artificial intelligence in single photon emission computed tomography (SPECT) imaging: a narrative review.Annals of translational medicine · 2021Review
- Application and Construction of Deep Learning Networks in Medical Imaging.IEEE transactions on radiation and plasma medical sciences · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
A neural network designed specifically for SPECT image reconstruction was developed. The network reconstructed activity images from SPECT projection data directly. Training was performed through a corpus of training data including that derived from digital phantoms generated from custom software and the corresponding projection data obtained from simulation. When using the network to reconstruct images, input projection data were initially fed to two fully connected (FC) layers to perform a basic reconstruction. Then the output of the FC layers and an attenuation map were delivered to five convolutional layers for signal-decay compensation and image optimization. To validate the system, data not used in training, simulated data from the Zubal human brain phantom, and clinical patient data were used to test reconstruction performance. Reconstructed images from the developed network proved closer to the truth with higher resolution and quantitative accuracy than those from conventional OS-EM reconstruction. To understand better the operation of the network for reconstruction, intermediate results from hidden layers were investigated for each step of the processing. The network system was also retrained with noisy projection data and compared with that developed with noise-free data. The retrained network proved even more robust after having learned to filter noise. Finally, we showed that the network still provided sharp images when using reduced view projection data (retrained with reduced view data).
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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.