Rapid MRI-Based Synthetic CT Simulations for Precise tFUS Targeting

TL;DR

This paper introduces a fast, MRI-based synthetic CT simulation framework for precise, radiation-free transcranial focused ultrasound targeting, combining deep learning and accelerated modeling to achieve sub-millimeter accuracy and significant time savings.

Contribution

It presents a novel, fully MRI-based simulation pipeline integrating deep learning generated sCT with rapid modeling techniques for accurate tFUS targeting.

Findings

Sub-millimeter targeting deviation achieved with sCT-based methods

Focal shape consistency with FWHM ~3.3-3.8 mm

Simulation time reduced by over 90% using hybrid algorithms

Abstract

Accurate targeting is critical for the effectiveness of transcranial focused ultrasound (tFUS) neuromodulation. While CT provides accurate skull acoustic properties, its ionizing radiation and poor soft tissue contrast limit clinical applicability. In contrast, MRI offers superior neuroanatomical visualization without radiation exposure but lacks skull property mapping. This study proposes a novel, fully CT free simulation framework that integrates MRI-derived synthetic CT (sCT) with efficient modeling techniques for rapid and precise tFUS targeting. We trained a deep-learning model to generate sCT from T1-weighted MRI and integrated it with both full-wave (k-Wave) and accelerated simulation methods, hybrid angular spectrum (kWASM) and Rayleigh-Sommerfeld ASM (RSASM). Across five skull models, both full-wave and hybrid pipelines using sCT demonstrated sub-millimeter targeting deviation, focal shape consistency (FWHM ~3.3-3.8 mm), and <0.2 normalized pressure error compared to CT-based gold standard. Notably, the kW-ASM and RS-ASM pipelines reduced simulation time from ~3320 s to 187 s and 34 s respectively, achieving ~94% and ~90% time savings. These results confirm that MRI-derived sCT combined with innovative rapid simulation techniques enables fast, accurate, and radiation-free tFUS planning, supporting its feasibility for scalable clinical applications.