Deep-Learning Generated Synthetic Material Decomposition Images Based on Single-Energy CT to Differentiate Intracranial Hemorrhage and Contrast Staining Within 24 Hours After Endovascular Thrombectomy.

ABSTRACT:

Aims

To develop a transformer-based generative adversarial network (trans-GAN) that can generate synthetic material decomposition images from single-energy CT (SECT) for real-time detection of intracranial hemorrhage (ICH) after endovascular thrombectomy.

Materials

We retrospectively collected data from two hospitals, consisting of 237 dual-energy CT (DECT) scans, including matched iodine overlay maps, virtual noncontrast, and simulated SECT images. These scans were randomly divided into a training set (n = 190) and an internal validation set (n = 47) in a 4:1 ratio based on the proportion of ICH. Additionally, 26 SECT scans were included as an external validation set. We compared our trans-GAN with state-of-the-art generation methods using several physical metrics of the ge

SUBMITTER: Wang T 

PROVIDER: S-EPMC11758448 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature