ArticleEuropean journal of nuclear medicine and molecular imaging2020
A deep neural network for fast and accurate scatter estimation in quantitative SPECT/CT under challenging scatter conditions.
Article in European journal of nuclear medicine and molecular imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 55 citations in OpenAlex.
- Artificial Intelligence Across the Cancer Theranostics Workflow: Critical Appraisal of Current Evidence and Future Clinical Translation.Molecular imaging and biology · 2026Review
- Exploring Anti-Coincidence Technique for Scattering Contamination Rejection in a Simultaneous PET/SPECT Imaging System.IEEE transactions on radiation and plasma medical sciences · 2026Article
- Deep learning-based attenuation and scatter correction in myocardial SPECT without using X-ray CT images.Radiological physics and technology · 2026Article
- Deep learning-guided attenuation and scatter correction ofAnnals of nuclear medicine · 2026Article
- Systematic parameter mapping for Wavelet-Based scatter correction in SPECT: clinical perception versus quantitative metrics.EJNMMI physics · 2026Article
- Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks.Zeitschrift fur medizinische Physik · 2025Article
- AI-enhanced patient-specific dosimetry in I-131 planar imaging with a single oblique view.Scientific reports · 2025Article
- CT-free attenuation and Monte-Carlo based scatter correction-guided quantitativeEuropean journal of nuclear medicine and molecular imaging · 2025Article
- Optimization of Image Quality in Pelvis Lymphoscintigraphy SPECT/CT Using Discovery NM/CT 670.World journal of nuclear medicine · 2025Article
- Targeted radioligand therapy: physics and biology, internal dosimetry and other practical aspects duringTheranostics · 2025Review
- ImprovingJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens.Diagnostics (Basel, Switzerland) · 2024Review
- Activity quantification and dosimetry in radiopharmaceutical therapy with reference toFrontiers in nuclear medicine · 2024Review
- 90Y SPECT scatter estimation and voxel dosimetry in radioembolization using a unified deep learning framework.EJNMMI physics · 2023Article
- Reducing scan time inJournal of applied clinical medical physics · 2023Article
- Physics-Guided Deep Scatter Estimation by Weak Supervision for Quantitative SPECT.IEEE transactions on medical imaging · 2023Article
- Training End-to-End Unrolled Iterative Neural Networks for SPECT Image Reconstruction.IEEE transactions on radiation and plasma medical sciences · 2023Article
- Mesh modeling of system geometry and anatomy phantoms for realistic GATE simulations and their inclusion in SPECT reconstruction.Physics in medicine and biology · 2023Article
- Deep-learning convolutional neural network-based scatter correction for contrast enhanced digital breast tomosynthesis in both cranio-caudal and mediolateral-oblique views.Journal of medical imaging (Bellingham, Wash.) · 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
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
purposeA major challenge for accurate quantitative SPECT imaging of some radionuclides is the inadequacy of simple energy window-based scatter estimation methods, widely available on clinic systems. A deep learning approach for SPECT/CT scatter estimation is investigated as an alternative to computationally expensive Monte Carlo (MC) methods for challenging SPECT radionuclides, such as
methodsA deep convolutional neural network (DCNN) was trained to separately estimate each scatter projection from the measured
resultsThe contrast recovery (CR) vs. noise and lung insert residual error vs. noise curves for images reconstructed with DCNN-SC and MC-SC estimates were similar. At the same noise level of 10% (across multiple realizations), the average sphere CR was 24%, 52%, 55%, and 67% for NO-SC, MC-SC, DCNN-SC, and TRUE-SC, respectively. For the liver phantom, the average CR for liver inserts were 32%, 73%, and 65% for NO-SC, MC-SC, and DCNN-SC, respectively while the corresponding values for average contrast-to-noise ratio (visibility index) in low-concentration extra-hepatic inserts were 2, 19, and 61, respectively. In patients, there was high concordance between lesion-to-liver uptake ratios for SPECT reconstruction with DCNN-SC (median 4.8, range 0.02-13.8) compared with MC-SC (median 4.0, range 0.13-12.1; CCC = 0.98) and with
conclusionsFor diverse
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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.