ArticleInverse problems2020
Fisher information analysis of list-mode SPECT emission data for joint estimation of activity and attenuation distribution.
Article in Inverse problems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
18 citing papers in PubMed, 6 citations in OpenAlex.
- Development and in silico imaging trial evaluation of a deep-learning-based transmission-less attenuation compensation method for DaT SPECT.Medical physics · 2025Article
- On the Effect of the Patient Table on Attenuation in Myocardial Perfusion Imaging SPECT.EJNMMI physics · 2025Article
- ISIT-GEN: An in silico imaging trial to assess the inter-scanner generalizability of CTLESS for myocardial perfusion SPECT on defect-detection task.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- CTLESS: A scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT.IEEE transactions on medical imaging · 2024Article
- Deep-learning-based methods of attenuation correction for SPECT and PET.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Deep learning approximation of attenuation maps for myocardial perfusion SPECT with an IQEJNMMI physics · 2023Article
- A list-mode multi-energy window low-count SPECT reconstruction method for isotopes with multiple emission peaks.EJNMMI physics · 2023Article
- Article
- Attenuation Correction for Dedicated Cardiac SPECT Imaging Without Using Transmission Data.Molecular imaging and radionuclide therapy · 2023Article
- Development and task-based evaluation of a scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2023Article
- Cross-vender, cross-tracer, and cross-protocol deep transfer learning for attenuation map generation of cardiac SPECT.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022Article
- Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT.European journal of nuclear medicine and molecular imaging · 2022Article
- A physics and learning-based transmission-less attenuation compensation method for SPECT.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Task-based assessment of binned and list-mode SPECT systems.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Direct Image-Based Attenuation Correction using Conditional Generative Adversarial Network for SPECT Myocardial Perfusion Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Deep learning-based attenuation map generation for myocardial perfusion SPECT.European journal of nuclear medicine and molecular imaging · 2020Article
- A LIST-MODE OSEM-BASED ATTENUATION AND SCATTER COMPENSATION METHOD FOR SPECT.Proceedings. IEEE International Symposium on Biomedical Imaging · 2020Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
The potential to perform attenuation and scatter compensation (ASC) in single-photon emission computed tomography (SPECT) imaging without a separate transmission scan is highly significant. In this context, attenuation in SPECT is primarily due to Compton scattering, where the probability of Compton scatter is proportional to the attenuation coefficient of the tissue and the energy of the scattered photon and the scattering angle are related. Based on this premise, we investigated whether the SPECT scattered-photon data acquired in list-mode (LM) format and including the energy information can be used to estimate the attenuation map. For this purpose, we propose a Fisher-information-based method that yields the Cramer-Rao bound (CRB) for the task of jointly estimating the activity and attenuation distribution using only the SPECT emission data. In the process, a path-based formalism to process the LM SPECT emission data, including the scattered-photon data, is proposed. The Fisher information method was implemented on NVIDIA graphics processing units (GPU) for acceleration. The method was applied to analyze the information content of SPECT LM emission data, which contains up to first-order scattered events, in a simulated SPECT system with parameters modeling a clinical system using realistic computational studies with 2-D digital synthetic and anthropomorphic phantoms. The method was also applied to LM data containing up to second-order scatter for a synthetic phantom. Experiments with anthropomorphic phantoms simulated myocardial perfusion and dopamine transporter (DaT)-Scan SPECT studies. The results show that the CRB obtained for the attenuation and activity coefficients was typically much lower than the true value of these coefficients. An increase in the number of detected photons yielded lower CRB for both the attenuation and activity coefficients. Further, we observed that systems with better energy resolution yielded a lower CRB for the attenuation coefficient. Overall, the results provide evidence that LM SPECT emission data, including the scattered photons, contains information to jointly estimate the activity and attenuation coefficients.
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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.