Physics-Infused Data-Driven Pipeline for Accurate Transcranial Ultrasound Brain Image Reconstruction

Physics-Infused Data-Driven Pipeline for Accurate Transcranial Ultrasound Brain Image Reconstruction | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 17 November 2025 V1 Latest version Share on Physics-Infused Data-Driven Pipeline for Accurate Transcranial Ultrasound Brain Image Reconstruction Authors : Asadollah Norouzi 0009-0000-3012-3376 [email protected] and Roohma Afifa Authors Info & Affiliations https://doi.org/10.22541/au.176340976.66435304/v1 267 views 118 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Obtaining high-fidelity, quantitative brain images via transcranial ultrasound remains a major challenge, largely stemming from the stark acoustic impedance difference between the skull and underlying brain tissues, as well as difficulties in achieving stable coupling between large ultrasound probes and the skull's curved surface. Existing solutions fall short in addressing these issues: Physics-only techniques like fullwaveform inversion (FWI) encounter obstacles such as weak signals-caused by skull-related attenuation, wave mode conversion, and phase distortion-and incomplete imaging coverage, since full-aperture transducer arrays are not feasible for clinical use or manufacturing. Conversely, end-to-end learning approaches that process raw ultrasound channel data struggle to capture the complex, nonlocal nonlinear wave physics involved in ultrasound propagation through bone, often producing SoS maps that look anatomically reasonable but lack quantitative accuracy, especially under low SNR and sparse aperture conditions. To tackle these problems, we introduce SkullAdaptUltrasound, a two-stage hybrid framework. The first stage employs time-reversal acoustics (TRA), also referred to as reverse time migration (RTM), on multi-angle ultrasound data. This step transforms low-SNR, limited-aperture waveforms into migration fragments that retain reliable structural information, even when imaging conditions are suboptimal. The second stage utilizes a transformer-based super-resolution encoder-decoder, augmented with a graph-based attention unit (GAU) to model inter-fragment connections, for data-driven mapping of these fragments into a coherent, quantitative SoS map of the brain. To enhance practicality, we adopt a partial-array acquisition strategy using a movable, low-count transducer set-this improves acoustic coupling and reduces hardware costs, while the hybrid framework offsets the missing aperture. Experiments on two separate synthetic datasets show that SkullAdaptUltrasound outperforms both traditional physicsbased methods and state-of-the-art learning-based approaches in reconstructing complete, detailed brain images from fragmented ultrasound data, underscoring its potential to push forward the field of transcranial ultrasound tomography. Supplementary Material File (manuscript5 (1).pdf) Download 6.65 MB Information & Authors Information Version history V1 Version 1 17 November 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords full-waveform inversion graph convolutional network machine learning time reversal acoustics transcranial ultrasound tomography Authors Affiliations Asadollah Norouzi 0009-0000-3012-3376 [email protected] View all articles by this author Roohma Afifa View all articles by this author Metrics & Citations Metrics Article Usage 267 views 118 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Asadollah Norouzi, Roohma Afifa. Physics-Infused Data-Driven Pipeline for Accurate Transcranial Ultrasound Brain Image Reconstruction. Authorea . 17 November 2025. DOI: https://doi.org/10.22541/au.176340976.66435304/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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