Learning-Based Rapid Phase-Aberration Correction and Control for Robot-Assisted MRI-Guided Low-/High-Intensity Focused Ultrasound Treatments

Abstract

Magnetic resonance imaging (MRI)-guided focused ultrasound (MRg-FUS) is an effective and noninvasive procedure for treating diseases such as neurological disorders. Phase adjustment on ultrasound transducers can only achieve a limited focal-spot steering range. When treating large abdominopelvic targets, mechanical adjustment on the transducers' position and orientation is the prerequisite for enlarging the steering range. Therefore, we previously designed an MRI-guided robot to manipulate the transducers to offer sufficient focal-spot movement range. This could provide more modulation solutions to constructive ultrasound interference. However, full-wave ultrasound propagation inside a patient's heterogeneous abdominal media is complex and nonlinear, posing significant challenges in ultrasound modulation and beam motion control. Here, we propose a novel learning-based phase-aberration correction and model-free control framework for robot-assisted MRg-FUS treatments. The correction policy guarantees rapid aberration compensation within 5.0 ms. Submillimeter refocusing accuracy is achieved in both the liver (0.32 mm) and pancreas (0.51 mm), meeting clinical requirements for focal targeting. Our controller can accommodate nonlinear phase actuation with fast convergence (< 5.7 ms) and ensure accurate positional tracking with a mean error of 0.26 mm, without prior knowledge of inhomogeneous media. Compared with the conventional model-based method, it contributes to 61.77%–70.39% mean error reduction without requiring model parameter tuning.