ENHANCING TRANSCRANIAL FOCUSED ULTRASOUND TREATMENT PLANNING WITH SYNTHETIC CT FROM ULTRA-SHORT ECHO TIME (UTE) MRI: A MULTI-TASK DEEP LEARNING APPROACH.

Abstract

Utilizing a multi-task deep learning framework, this study generated synthetic CT (sCT) images from a limited dataset of Ultrashort echo time (UTE) MRI for transcranial focused ultrasound (tFUS) planning. A 3D Transformer U-Net was employed to produce sCT images that closely replicated actual CT scans, demonstrated by an average Dice coefficient of 0.868 for morphological accuracy. The acoustic simulation with sCT images showed mean focus absolute pressure differences of 8.85±7.29 % for the anterior cingulate cortex, 11.81±8.63 % for the precuneus, and 7.27±3.64 % for the supplemental motor cortex, with focus position discrepancies within 0.9±0.5 mm. These results underscore the efficacy of UTE-MRI as a non-radiative, cost-effective alternative for tFUS planning, with significant potential for clinical application.