Self-adaptive transcranial ultrasound imaging using an effective skull model: experimental evaluation on a human skull at 3.15 MHz
Abstract
Introduction. Transcranial ultrasound imaging is hindered by skull-induced aberrations that degrade focusing and image contrast. Existing correction methods typically rely on external imaging or detailed acoustic models, limiting their applicability in emergency settings. Methods. We propose an adaptive approach based on an effective skull model comprising two interfaces and an effective velocity, estimated directly from Full Matrix Capture data without external information. The estimated parameters are integrated into a Total Focusing Method reconstruction with refraction correction. The method is evaluated experimentally at 3.15 MHz by imaging wire targets located beyond a human skull fragment in four configurations: free-field reference, uncorrected, skull corrected with automatically estimated parameters, and skull corrected with optically scanned surfaces. Results. The effective model reduces the mean Euclidean localization error by 67% (2.16 to 0.71 mm). Comparison with the optical reference isolates interface estimation uncertainty as the dominant residual error source, while confirming that the effective velocity model itself produces comparable performance when provided with accurate interface positions. Discussion. These results demonstrate that a self-estimated model enables reliable localization in transcranial conditions. Improving surface estimation robustness is identified as a key factor for precise target reconstruction.
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BibTeX
@article{kuntz2026selfadaptive,
title = {{Self-adaptive transcranial ultrasound imaging using an effective skull model: experimental evaluation on a human skull at 3.15 MHz}},
author = {Damien Kuntz and Anthony Novell and Quentin Grimal and Léonard Le Jeune and Sylvain Chatillon},
journal = {Acta Acustica},
year = {2026},
month = jul,
doi = {10.1051/aacus/2026062},
url = {https://hal.science/hal-05760945v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Self-adaptive transcranial ultrasound imaging using an effective skull model: experimental evaluation on a human skull at 3.15 MHz
AU - Damien Kuntz
AU - Anthony Novell
AU - Quentin Grimal
AU - Léonard Le Jeune
AU - Sylvain Chatillon
PY - 2026
DA - 2026/07/27
JO - Acta Acustica
DO - 10.1051/aacus/2026062
UR - https://hal.science/hal-05760945v1
AB - Introduction. Transcranial ultrasound imaging is hindered by skull-induced aberrations that degrade focusing and image contrast. Existing correction methods typically rely on external imaging or detailed acoustic models, limiting their applicability in emergency settings. Methods. We propose an adaptive approach based on an effective skull model comprising two interfaces and an effective velocity, estimated directly from Full Matrix Capture data without external information. The estimated parameters are integrated into a Total Focusing Method reconstruction with refraction correction. The method is evaluated experimentally at 3.15 MHz by imaging wire targets located beyond a human skull fragment in four configurations: free-field reference, uncorrected, skull corrected with automatically estimated parameters, and skull corrected with optically scanned surfaces. Results. The effective model reduces the mean Euclidean localization error by 67% (2.16 to 0.71 mm). Comparison with the optical reference isolates interface estimation uncertainty as the dominant residual error source, while confirming that the effective velocity model itself produces comparable performance when provided with accurate interface positions. Discussion. These results demonstrate that a self-estimated model enables reliable localization in transcranial conditions. Improving surface estimation robustness is identified as a key factor for precise target reconstruction.
SN - 2681-4617
ER -