High-Intensity Focused Ultrasound (HIFU) is a non-invasive therapy, yet its safety is often degraded by severe acoustic interference during continuous ultrasound guidance. Conventional HIFU interference suppression methods heavily rely on proprietary raw Radio-Frequency (RF) data or complex hardware synchronization, limiting their clinical utility and preventing real-time implementation. To address this limitation, we propose Manifold-Constrained Hyper-Connections Diffusion (mHC-Diff), an image-domain diffusion framework for real-time interference suppression without specialized hardware synchronization, disentangling complex interference from anatomical structures while ensuring high reconstruction fidelity. To achieve clinical real-time application, our approach employs a two-stage strategy: (i) anatomy-aware prior acquisition, where a diffusion model is trained with multi-step UNet as a highfidelity Teacher; and (ii) efficiency distillation, where this prior is distilled into a one-step Student via knowledge distillation to achieve real-time throughput. Extensive validation on a clinically representative dataset across diverse therapeutic scenarios shows that mHC-Diff achieves superior restoration (26.65 dB PSNR), while enabling real-time inference (~20 FPS) on a single NVIDIA RTX 4090, providing a ~6.8x speedup over iterative diffusion baselines (e.g., HIFU-Diff). By eliminating the requirement for specialized hardware synchronization and proprietary RF access, this image-domain framework ensures compatibility and facilitates real-time interference suppression during ultrasound-guided HIFU interventions.
@article{arxiv.2509.01557,
title = {Real-Time Hardware-Free HIFU Interference Suppression via Teacher-Student Diffusion Framework},
author = {Dejia Cai and Ali Abdollahi and Xi Wang and Kun Yang and Zhaohui Guo and Xiaowei Zhou and Hao Chen},
journal= {arXiv preprint arXiv:2509.01557},
year = {2026}
}