Few-shot Deep Learning for Phase-Amplitude Aberration Correction in Transcranial Focused Ultrasound

• URL: http://arxiv.org/abs/2607.29182v1
• Date: Fri, 31 Jul 2026 09:02:47 GMT
• Title: Few-shot Deep Learning for Phase-Amplitude Aberration Correction in Transcranial Focused Ultrasound
• Abstract summary: We propose a few-shot framework that predicts per-element phase and amplitude corrections for a 96-element 3D phased-array transducer from patient CT images.<n>The framework is pretrained on diverse skull geometries and fine-tuned with only ten target points, enabling rapid adaptation to unseen patients.
• Score: 2.1352542609902447
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Transcranial focused ultrasound (tFUS) is a non-invasive technique that delivers focused acoustic energy through the skull for neuromodulation and therapeutic applications. However, the heterogeneous structure of the skull induces complex, patient-specific phase and amplitude aberrations that distort the acoustic focus and deviate it from the intended target, compromising therapeutic efficacy and safety. Conventional time-reversal (TR) simulations can correct these aberrations but rely on computationally expensive full-wave solvers, making them impractical for real-time use and iterative treatment planning. We propose a few-shot deep surrogate framework that predicts per-element phase and amplitude corrections for a 96-element 3D phased-array transducer from patient CT images. A geometry-aware encoder extracts skull-path features shared across dedicated phase classification and amplitude regression branches, where phase periodicity is handled via circular expectation decoding. The framework is pretrained on diverse skull geometries and fine-tuned with only ten target points, enabling rapid adaptation to unseen patients without full patient-specific simulation. Evaluated via leave-one-out cross-validation across 12 skulls, it achieves a mean phase CMAE of 0.155 rad and amplitude rMAE of 9.089%, a focal centroid error of 0.467 mm, Dice score of 94.422%, and peak pressure ratio of 92.332%, with an approximately 2,535 times speedup over TR simulation. The code is available at https://github.com/Minju-Seol/fewshot-tfus-correction.

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