ArticleNPJ digital medicine2026
Quantification of PET activation in adipose tissue from non-contrast CT scans.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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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.
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