Enhancing transcranial focused ultrasound treatment planning with synthetic ct from ultra-short echo time (UTE) MRI: a deep learning approach

Motivation: There’s a clinical interest in exploring an alternative option using ultrashort-time-echo MRI to replace CT imaging for accurate transcranial FUS treatment planning. Goal(s): To employ a deep learning approach to generate synthetic CT images from a limited UTE-MRI dataset. Approach: A deep learning framework based on 3D Transformer U-net is applied to the paired UTE-CT dataset and acoustic simulation is performed to validate the results. Results: Utilizing UTE MRI can offer synthetic CT as an alternative to traditional CT imaging. The simulations showed a minimal maximum acoustic pressure difference of less than8% and a focus shift of less than1.5mm compared to CT-based simulations. Impact: This study introduces a novel multi-task deep learning approach that enables accurate synthetic CT generation from limited UTE-MRI data. This innovation provides a cost-effective and radiation-free alternative to traditional CT imaging, significantly enhancing transcranial focused ultrasound treatment planning.