ArticleJournal of the Optical Society of America. A, Optics, image science, and vision2020
Quantifying the loss of information from binning list-mode data.
Article in Journal of the Optical Society of America. A, Optics, image science, and vision, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- CTLESS: A scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT.IEEE transactions on medical imaging · 2024Article
- A list-mode multi-energy window low-count SPECT reconstruction method for isotopes with multiple emission peaks.EJNMMI physics · 2023Article
- Article
- 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
- Effect on null spaces of list-mode imaging systems due to increasing the size of attribute space.Journal of the Optical Society of America. A, Optics, image science, and vision · 2021Article
- 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
- Fisher information analysis of list-mode SPECT emission data for joint estimation of activity and attenuation distribution.Inverse problems · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
List-mode data are increasingly being used in single photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging, among other imaging modalities. However, there are still many imaging designs that effectively bin list-mode data before image reconstruction or other estimation tasks are performed. Intuitively, the binning operation should result in a loss of information. In this work, we show that this is true for Fisher information and provide a computational method for quantifying the information loss. In the end, we find that the information loss depends on three factors. The first factor is related to the smoothness of the mean data function for the list-mode data. The second factor is the actual object being imaged. Finally, the third factor is the binning scheme in relation to the other two factors.
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Registered trials
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