Dynamic muscle damage monitoring in pig crush injury: T2-weighted Dixon and 2D ultrasound applications

This study confirmed the viability of the CI model through biochemical verification and demonstrated that T2-Dixon and ultrasound synergistically assessed muscle damage, providing a more thorough evaluation that surpasses the constraints of biochemical markers alone.

Authors

Institutions

Publication Details

Journal
Frontiers in Veterinary Science
Published
2026-02-25
DOI
https://doi.org/10.3389/fvets.2026.1692050
Primary Topic
Muscle and Compartmental Disorders
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dynamic muscle damage monitoring in pig crush injury: T2-weighted Dixon and 2D ultrasound applications

Qi Lv, Qi Wang, Yi-Xiang Wang, Rongbang Chen et al.
Frontiers in Veterinary Science
Muscle and Compartmental Disorders
article

Dynamic muscle damage monitoring in pig crush injury: T2-weighted Dixon and 2D ultrasound applications

Qi Lv, Qi Wang, Yi-Xiang Wang, Rongbang Chen, Guangda Wang, Haojun Fan, Dou Li, Zikuo Zhao
article en

Abstract

This study confirmed the viability of the CI model through biochemical verification and demonstrated that T2-Dixon and ultrasound synergistically assessed muscle damage, providing a more thorough evaluation that surpasses the constraints of biochemical markers alone.

Frontiers in Veterinary ScienceVol. 13
Hebei Medical University (CN), Tianjin University (CN), Fourth Hospital of Hebei Medical University (CN)
Openalex Percentile: Top 44%
Muscle and Compartmental Disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.