A 2D autocorrelation-based frequency estimator reflecting spatial tissue distribution to improve Ultrasound H-scan tissue characterization
Abstract
H-scan is a promising quantitative ultrasound technique that estimates the frequency content of backscattered signals and maps the estimated frequencies onto a red/blue color scale to reflect underlying tissue properties. Although it relies on matched filters tuned to different frequencies, the broad spectral bandwidth of ultrasound produces noisy, granular displays. Here, we introduce an adaptive frequency estimator designed to suppress the noise within homogeneous regions while preserving sharpness across tissue boundaries. The method combines 2D autocorrelation with a matched filter. In the first stage, a matched-filter-based estimation yields an a priori map of the spatial distribution of frequencies. The local heterogeneity of these estimates then defines a 2D weighting function that guides a second estimation stage. Drawing on the concept of Loupas's blood velocity estimator, we apply autocorrelation over a 2D spatial kernel to recover the axial frequency components, employing a weighted summation that accounts for the spatial frequency distribution within the kernel. We benchmarked the proposed estimator against conventional approaches, including the short-time Fourier transform, the H-scan matched filter, and standard autocorrelation, using both Field II simulations and in vivo data from human subjects with hepatic steatosis. In simulation, our adaptive estimator reduced the noisy texture in homogeneous regions while retaining clear boundary delineation, whereas the other estimators could achieve only one of these objectives. Applied to the in vivo human liver, the estimator improved H-scan image quality by lowering noise and enhancing the discrimination of steatotic liver from adjacent gallbladder and skin layers.
Related
- High-frequency ultrasound point-of-care device to quantify myopia-induced microstructural changes in the anterior sclera
- Optomechanical transducers for gigahertz frequency ultrasound imaging
- Towards 3D fully randomized frequency-domain reconstruction of the speed of sound in breast ultrasound computed tomography
- Methodological approach to define Low Intensity Pulsed Ultrasound parameters characterization for cell stimulation in near and intermediate fields
- Complex-gate all-optical frequency-resolved optical gating for ultrabroadband isolated attosecond pulse characterization
BibTeX
@misc{baek2026autocorrelationbased,
title = {{A 2D autocorrelation-based frequency estimator reflecting spatial tissue distribution to improve Ultrasound H-scan tissue characterization}},
author = {Jihye Baek and Thurston Brevett and Dongwoon Hyun and Ahmed El Kaffas and Aya Kamaya and Kevin J. Parker and Jeremy J. Dahl},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2609.30686},
eprint = {2609.30686},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.30686},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - A 2D autocorrelation-based frequency estimator reflecting spatial tissue distribution to improve Ultrasound H-scan tissue characterization
AU - Jihye Baek
AU - Thurston Brevett
AU - Dongwoon Hyun
AU - Ahmed El Kaffas
AU - Aya Kamaya
AU - Kevin J. Parker
AU - Jeremy J. Dahl
PY - 2026
DA - 2026/10/23
PB - arXiv
DO - 10.48550/arxiv.2609.30686
UR - https://arxiv.org/abs/2609.30686
AB - H-scan is a promising quantitative ultrasound technique that estimates the frequency content of backscattered signals and maps the estimated frequencies onto a red/blue color scale to reflect underlying tissue properties. Although it relies on matched filters tuned to different frequencies, the broad spectral bandwidth of ultrasound produces noisy, granular displays. Here, we introduce an adaptive frequency estimator designed to suppress the noise within homogeneous regions while preserving sharpness across tissue boundaries. The method combines 2D autocorrelation with a matched filter. In the first stage, a matched-filter-based estimation yields an a priori map of the spatial distribution of frequencies. The local heterogeneity of these estimates then defines a 2D weighting function that guides a second estimation stage. Drawing on the concept of Loupas's blood velocity estimator, we apply autocorrelation over a 2D spatial kernel to recover the axial frequency components, employing a weighted summation that accounts for the spatial frequency distribution within the kernel. We benchmarked the proposed estimator against conventional approaches, including the short-time Fourier transform, the H-scan matched filter, and standard autocorrelation, using both Field II simulations and in vivo data from human subjects with hepatic steatosis. In simulation, our adaptive estimator reduced the noisy texture in homogeneous regions while retaining clear boundary delineation, whereas the other estimators could achieve only one of these objectives. Applied to the in vivo human liver, the estimator improved H-scan image quality by lowering noise and enhancing the discrimination of steatotic liver from adjacent gallbladder and skin layers.
ER -