Multi-parametric quantitative ultrasound imaging evaluation of rat sciatic nerve regeneration
Abstract
Peripheral nerve injury is a prevalent condition that significantly impairs patients' sensorimotor function. Noninvasive monitoring of peripheral nerve regeneration could serve as a valuable clinical tool for improving the timing and effectiveness of surgical interventions. We present a multiparametric high-frequency quantitative ultrasound evaluation to noninvasively track temporal and spatial peripheral nerve regeneration following early (n = 16) and delayed (n = 15) graft-based repair of rat sciatic nerve gaps, compared with a sham control group (n = 8). Multiple ultrasound parameters—including backscatter coefficient (BSC) spectra-based measurements (integrated BSC and midband fitted slope), homodyned K envelope statistics (μ and κ ), gray-level co-occurrence matrix (GLCM) texture features, and B-mode morphological measurements were acquired using a Vevo MD UHF 48 transducer at proximal, intra-graft, and distal nerve locations at 1, 2, and 3 months post-repair. Four sciatic nerve samples were extracted after each time point from each experimental group. A histological grading scheme was applied to classify nerve sections into mild, moderate, and severe pathological grades. Principal component analysis (PCA) combined with support vector machine (SVM) classification was employed to establish decision boundaries among pathological groups. Four parameters (μ, cross-sectional area, normalized integrated BSC, midband fitted slope) significantly differentiated groups with varying pathological severity. PCA-SVM analysis achieved excellent classification performance with an area under the ROC curve of 0.93 and accuracy of 0.85 for distinguishing minimal versus severe pathological injured nerves. These findings demonstrate that multiparametric quantitative ultrasound (qUS) effectively characterizes nerve pathologies by integrating complementary information about microstructural changes across different structural scales, potentially offering a comprehensive, noninvasive assessment of the regenerative microenvironment.
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BibTeX
@article{wu2026multiparametric,
title = {{Multi-parametric quantitative ultrasound imaging evaluation of rat sciatic nerve regeneration}},
author = {Yuanshan Wu and Kaixin Pan and Xin Cheng and Elisabeth Orozco and Aiguo Han and Michael P. Andre and Eric Y. Chang and Sameer B. Shah},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-72243-1},
url = {https://www.nature.com/articles/s41598-026-72243-1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Multi-parametric quantitative ultrasound imaging evaluation of rat sciatic nerve regeneration
AU - Yuanshan Wu
AU - Kaixin Pan
AU - Xin Cheng
AU - Elisabeth Orozco
AU - Aiguo Han
AU - Michael P. Andre
AU - Eric Y. Chang
AU - Sameer B. Shah
PY - 2026
DA - 2026/10/01
JO - Scientific Reports
DO - 10.1038/s41598-026-72243-1
UR - https://www.nature.com/articles/s41598-026-72243-1
AB - Peripheral nerve injury is a prevalent condition that significantly impairs patients' sensorimotor function. Noninvasive monitoring of peripheral nerve regeneration could serve as a valuable clinical tool for improving the timing and effectiveness of surgical interventions. We present a multiparametric high-frequency quantitative ultrasound evaluation to noninvasively track temporal and spatial peripheral nerve regeneration following early (n = 16) and delayed (n = 15) graft-based repair of rat sciatic nerve gaps, compared with a sham control group (n = 8). Multiple ultrasound parameters—including backscatter coefficient (BSC) spectra-based measurements (integrated BSC and midband fitted slope), homodyned K envelope statistics (μ and κ ), gray-level co-occurrence matrix (GLCM) texture features, and B-mode morphological measurements were acquired using a Vevo MD UHF 48 transducer at proximal, intra-graft, and distal nerve locations at 1, 2, and 3 months post-repair. Four sciatic nerve samples were extracted after each time point from each experimental group. A histological grading scheme was applied to classify nerve sections into mild, moderate, and severe pathological grades. Principal component analysis (PCA) combined with support vector machine (SVM) classification was employed to establish decision boundaries among pathological groups. Four parameters (μ, cross-sectional area, normalized integrated BSC, midband fitted slope) significantly differentiated groups with varying pathological severity. PCA-SVM analysis achieved excellent classification performance with an area under the ROC curve of 0.93 and accuracy of 0.85 for distinguishing minimal versus severe pathological injured nerves. These findings demonstrate that multiparametric quantitative ultrasound (qUS) effectively characterizes nerve pathologies by integrating complementary information about microstructural changes across different structural scales, potentially offering a comprehensive, noninvasive assessment of the regenerative microenvironment.
SN - 2045-2322
ER -