Quantification of PET activation in adipose tissue from non-contrast CT scans.
Authors
Affiliations (6)
Affiliations (6)
- Applied Chest Imaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Winchester Hospital, Department of Radiology, Winchester, MA, USA.
- Department of Radiology, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA.
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA.
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
- Applied Chest Imaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. [email protected].
Abstract
Brown adipose tissue (BAT) plays a key role in energy metabolism and cardiometabolic health. Its detection typically relies on 18F-FDG PET, which is costly, radiation-intensive, and impractical for large-scale screening. We propose a deep learning model to estimate regional metabolic activity in adipose tissue from standard non-contrast CT, enabling PET-like insights without radiotracers. Using paired PET/CT data from two independent cohorts, we trained a conditional Generative Adversarial Network (cGAN) to predict standardized uptake values (SUV) within adipose regions identified on CT. The network included a fat-focused loss function to enhance metabolic signal estimation. Predicted activations showed strong agreement with PET-derived values and were reproducible across anatomical regions and datasets. This method provides a radiation-sparing alternative for assessing adipose metabolic activity in clinical and research settings and it could support population-based studies of BAT, metabolic health, and disease progression using routine chest CT scans without additional imaging burden.