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
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Keywords - cancer, deep learning, PSMA PET, attenuation correction
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