Machine Learning-Enhanced Skull-Universal Acoustic Hologram for Efficient Transcranial Ultrasound Neuromodulation Across Varied Rodent Skulls

초록

Ultrasound neuromodulation (UNM) has gained significant interest in brain science due to its non-invasive nature, precision, and deep brain stimulation capabilities. However, the skull poses challenges along the acoustic path, leading to beam distortion and necessitating effective acoustic aberration correction. Acoustic holograms used with single-element ultrasound transducers offer a promising solution by enabling both aberration correction and multi-focal stimulation. A major limitation, however, is that hologram lenses designed for specific skulls may not perform well on other skulls, requiring multiple custom lenses for scaled studies. To address this, we introduce the Skull-Universal Acoustic Hologram (SUAH), which enables efficient transcranial UNM across various skull types. Our hologram generation framework integrates a physics-based acoustic hologram, differentiable acoustic simulation in heterogeneous media, and a gradient accumulation technique. SUAH, trained on a range of rodent skull shapes, demonstrated remarkable generalizability and robustness, even outperforming the Skull-Specific Acoustic Hologram (SSAH). Through comprehensive analyses, we showed that SUAH performs exceptionally well—even when trained on smaller datasets—significantly outperforming training based on individual skulls. In conclusion, SUAH shows promise as a scalable, versatile, and accurate tool for ultrasound neuromodulation, representing a significant advancement over conventional single-skull hologram lenses. Its ability to adapt to different skull types without the need for multiple custom lenses has the potential to greatly facilitate research in ultrasound neuromodulation.

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연합학습 및 표현 학습을 사용한 medical 도메인에서의 딥 러닝 기법, 도메인 적응 및 테스트 타임 학습과 같은 딥 러닝 기반 컴퓨터 비전 응용, 이미지 처리 및 이미지-텍스트 캡셔닝을 포함한 의료 응용의 딥 러닝 기반 진단 등을 포함합니다.