ArticleEJNMMI physics2019
Accelerated SPECT image reconstruction with FBP and an image enhancement convolutional neural network.
Article in EJNMMI physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- 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
- Respiratory signal estimation for cardiac perfusion SPECT using deep learning.Medical physics · 2024Article
- Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies.EJNMMI physics · 2023Review
- A role for artificial intelligence in molecular imaging of infection and inflammation.European journal of hybrid imaging · 2022Review
- Analysis of a deep learning-based method for generation of SPECT projections based on a large Monte Carlo simulated dataset.EJNMMI physics · 2022Article
- Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept.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
- Deep learning in structural and functional lung image analysis.The British journal of radiology · 2022Review
- Artificial intelligence with deep learning in nuclear medicine and radiology.EJNMMI physics · 2021Review
- Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2021Article
- Artificial Intelligence-Based Data Corrections for Attenuation and Scatter in Position Emission Tomography and Single-Photon Emission Computed Tomography.PET clinics · 2021Review
- Applications of artificial intelligence in nuclear medicine image generation.Quantitative imaging in medicine and surgery · 2021Review
- Artificial intelligence in single photon emission computed tomography (SPECT) imaging: a narrative review.Annals of translational medicine · 2021Review
- SPECTnet: a deep learning neural network for SPECT image reconstruction.Annals of translational medicine · 2021Article
- Deep learning-based image quality improvement ofEJNMMI physics · 2021Article
- A Learned Reconstruction Network for SPECT Imaging.IEEE transactions on radiation and plasma medical sciences · 2021Article
- A deep neural network for fast and accurate scatter estimation in quantitative SPECT/CT under challenging scatter conditions.European journal of nuclear medicine and molecular imaging · 2020Article
- Adaptive scan duration in SPECT: Evaluation for radioembolization.Medical physics · 2020Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundMonte Carlo-based iterative reconstruction to correct for photon scatter and collimator effects has been proven to be superior over analytical correction schemes in single-photon emission computed tomography (SPECT/CT), but it is currently not commonly used in daily clinical practice due to the long associated reconstruction times. We propose to use a convolutional neural network (CNN) to upgrade fast filtered back projection (FBP) image quality so that reconstructions comparable in quality to the Monte Carlo-based reconstruction can be obtained within seconds.
resultsA total of 128 technetium-99m macroaggregated albumin pre-treatment SPECT/CT scans used to guide hepatic radioembolization were available. Four reconstruction methods were compared: FBP, clinical reconstruction, Monte Carlo-based reconstruction, and the neural network approach. The CNN generated reconstructions in 5 sec, whereas clinical reconstruction took 5 min and the Monte Carlo-based reconstruction took 19 min. The mean squared error of the neural network approach in the validation set was between that of the Monte Carlo-based and clinical reconstruction, and the lung shunting fraction difference was lower than 2 percent point. A phantom experiment showed that quantitative measures required in radioembolization were accurately retrieved from the CNN-generated reconstructions.
conclusionsFBP with an image enhancement neural network provides SPECT reconstructions with quality close to that obtained with Monte Carlo-based reconstruction within seconds.
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