Predicting standardized uptake value of brown adipose tissue from CT scans using convolutional neural networks.

ABSTRACT: The standard method for identifying active Brown Adipose Tissue (BAT) is [¹⁸F]-Fluorodeoxyglucose ([¹⁸F]-FDG) PET/CT imaging, which is costly and exposes patients to radiation, making it impractical for population studies. These issues can be addressed with computational methods that predict [¹⁸F]-FDG uptake by BAT from CT; earlier population studies pave the way for developing such methods by showing some correlation between the Hounsfield Unit (HU) of BAT in CT and the corresponding [¹⁸F]-FDG uptake in PET. In this study, we propose training convolutional neural networks (CNNs) to predict [¹⁸F]-FDG uptake by BAT from unenhanced CT scans in the restricted regions that are likely to contain BAT. Using the Attention U-Net architecture,

SUBMITTER: Erdil E 

PROVIDER: S-EPMC11436835 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature