The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.
Positron Emission Tomography: Current Challenges and Opportunities for Technological Advances in Clinical and Preclinical Imaging Systems
Open accessAbstract
Positron emission tomography (PET) imaging is based on detecting two time-coincident high-energy photons from the emission of a positron-emitting radioisotope. The physics of the emission, and the detection of the coincident photons, give PET imaging unique capabilities for both very high sensitivity and accurate estimation of the in vivo concentration of the radiotracer. PET imaging has been widely adopted as an important clinical modality for oncological, cardiovascular, and neurological applications. PET imaging has also become an important tool in preclinical studies, particularly for investigating murine models of disease and other small-animal models. However, there are several challenges to using PET imaging systems. These include the fundamental trade-offs between resolution and noise, the quantitative accuracy of the measurements, and integration with X-ray computed tomography and magnetic resonance imaging. In this article, we review how researchers and industry are addressing these challenges.
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Cites
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Maximum Likelihood Reconstruction for Emission Tomography
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Self-citationOllinger, J. M., & Fessler, J. A. (1997) · IEEE Signal Processing Magazine · doi:10.1109/79.560323 · Cited by 1,167
Performance Measurements of the Siemens mMR Integrated Whole-Body PET/MR Scanner
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Cited by
Performance Evaluation of the Vereos PET/CT System According to the NEMA NU2-2012 Standard
Rausch, I., Ruiz, A., Valverde-Pascual, I., Cal-González, J., Beyer, T., & Carrio, I. (2019) · Journal of Nuclear Medicine · doi:10.2967/jnumed.118.215541 · Cited by 168
Metadata
- Authors
- Vaquero, J. J., & Kinahan, P.
- Venue
- Annual Review of Biomedical Engineering
- Publisher
- Annual Reviews
- Published
- 2015-12-07
- Volume
- 17
- Issue
- 1
- Pages
- 385-414
- ISSN
- 1523-9829 (ISSN-L)
- 1545-4274
- OpenAlex
- W1924463094
- Cited by
- 383
- References
- 71
- FWCI
- 1.85
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
