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AI for Improved PET/CT Attenuation Correction in Prostate Cancer Imaging

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Manage episode 428405407 series 1754503
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Positron Emission Tomography (PET) combined with Computed Tomography (CT) is a powerful imaging modality used in oncology for diagnosis, staging, and treatment monitoring. However, one limitation of PET/CT is the need for accurate attenuation correction (AC) to account for tissue density variations. Traditionally, low-dose CT scans are used for AC, but these contribute to patient radiation exposure. In a new study, researchers Kevin C. Ma, Esther Mena, Liza Lindenberg, Nathan S. Lay, Phillip Eclarinal, Deborah E. Citrin, Peter A. Pinto, Bradford J. Wood, William L. Dahut, James L. Gulley, Ravi A. Madan, Peter L. Choyke, Ismail Baris Turkbey, and Stephanie A. Harmon from the National Cancer Institute proposed an artificial intelligence (AI) tool to generate attenuation-corrected PET (AC-PET) images directly from non-attenuation-corrected PET (NAC-PET) images, reducing the reliance on CT scans. Their research paper was published in Oncotarget’s Volume 15 on May 7, 2024, entitled, “Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN.” Full blog - https://www.oncotarget.org/2024/07/11/ai-for-improved-pet-ct-attenuation-correction-in-prostate-cancer-imaging/ Paper DOI - https://doi.org/10.18632/oncotarget.28583 Correspondence to - Stephanie A. Harmon - stephanie.harmon@nih.gov Video short - https://www.youtube.com/watch?v=0mZItCB8AtI Sign up for free Altmetric alerts about this article - https://oncotarget.altmetric.com/details/email_updates?id=10.18632%2Foncotarget.28583 Subscribe for free publication alerts from Oncotarget - https://www.oncotarget.com/subscribe/ Keywords - cancer, deep learning, PSMA PET, attenuation correction About Oncotarget Oncotarget is an open-access, peer-reviewed journal that has published primarily oncology-focused research papers since 2010. These papers are available to readers (at no cost and free of subscription barriers) in a continuous publishing format at Oncotarget.com. Oncotarget is indexed and archived by PubMed/Medline, PubMed Central, Scopus, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science). To learn more about Oncotarget, please visit https://www.oncotarget.com and connect with us: Facebook - https://www.facebook.com/Oncotarget/ X - https://twitter.com/oncotarget Instagram - https://www.instagram.com/oncotargetjrnl/ YouTube - https://www.youtube.com/@OncotargetJournal LinkedIn - https://www.linkedin.com/company/oncotarget Pinterest - https://www.pinterest.com/oncotarget/ Reddit - https://www.reddit.com/user/Oncotarget/ Spotify - https://open.spotify.com/show/0gRwT6BqYWJzxzmjPJwtVh MEDIA@IMPACTJOURNALS.COM
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458 episodes

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Manage episode 428405407 series 1754503
Content provided by Oncotarget Podcast. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Oncotarget Podcast or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Positron Emission Tomography (PET) combined with Computed Tomography (CT) is a powerful imaging modality used in oncology for diagnosis, staging, and treatment monitoring. However, one limitation of PET/CT is the need for accurate attenuation correction (AC) to account for tissue density variations. Traditionally, low-dose CT scans are used for AC, but these contribute to patient radiation exposure. In a new study, researchers Kevin C. Ma, Esther Mena, Liza Lindenberg, Nathan S. Lay, Phillip Eclarinal, Deborah E. Citrin, Peter A. Pinto, Bradford J. Wood, William L. Dahut, James L. Gulley, Ravi A. Madan, Peter L. Choyke, Ismail Baris Turkbey, and Stephanie A. Harmon from the National Cancer Institute proposed an artificial intelligence (AI) tool to generate attenuation-corrected PET (AC-PET) images directly from non-attenuation-corrected PET (NAC-PET) images, reducing the reliance on CT scans. Their research paper was published in Oncotarget’s Volume 15 on May 7, 2024, entitled, “Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN.” Full blog - https://www.oncotarget.org/2024/07/11/ai-for-improved-pet-ct-attenuation-correction-in-prostate-cancer-imaging/ Paper DOI - https://doi.org/10.18632/oncotarget.28583 Correspondence to - Stephanie A. Harmon - stephanie.harmon@nih.gov Video short - https://www.youtube.com/watch?v=0mZItCB8AtI Sign up for free Altmetric alerts about this article - https://oncotarget.altmetric.com/details/email_updates?id=10.18632%2Foncotarget.28583 Subscribe for free publication alerts from Oncotarget - https://www.oncotarget.com/subscribe/ Keywords - cancer, deep learning, PSMA PET, attenuation correction About Oncotarget Oncotarget is an open-access, peer-reviewed journal that has published primarily oncology-focused research papers since 2010. These papers are available to readers (at no cost and free of subscription barriers) in a continuous publishing format at Oncotarget.com. Oncotarget is indexed and archived by PubMed/Medline, PubMed Central, Scopus, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science). To learn more about Oncotarget, please visit https://www.oncotarget.com and connect with us: Facebook - https://www.facebook.com/Oncotarget/ X - https://twitter.com/oncotarget Instagram - https://www.instagram.com/oncotargetjrnl/ YouTube - https://www.youtube.com/@OncotargetJournal LinkedIn - https://www.linkedin.com/company/oncotarget Pinterest - https://www.pinterest.com/oncotarget/ Reddit - https://www.reddit.com/user/Oncotarget/ Spotify - https://open.spotify.com/show/0gRwT6BqYWJzxzmjPJwtVh MEDIA@IMPACTJOURNALS.COM
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