Preliminary Data of Neck Muscle Morphology With Head-Supported Mass in Male and Female Volunteers

MILITARY MEDICINE(2023)

引用 0|浏览4
暂无评分
摘要
Introduction This study quantified parameters related to muscle morphology using a group of upright seated female and male volunteers with a head-supported mass. Materials and Methods Upright magnetic resonance images (MRIs) were obtained from 23 healthy volunteers after approval from the U.S. DoD. They were asymptomatic for neck pain, with no history of injury. The volunteers were scanned using an upright MRI scanner with a head-supported mass (army combat helmet). T1 and T2 sagittal and axial images were obtained. Measurements were performed by an engineer and a neurosurgeon. The cross-sectional areas of the sternocleidomastoid and multifidus muscles were measured at the inferior endplate in the sub-axial column, and the centroid angle and centroid radius were quantified. Differences in the morphology by gender and spinal level were analyzed using a repeated measures analysis of variance model, adjusted for multiple corrections. Results For females and males, the cross-sectional area of the sternocleidomastoid muscle ranged from 2.3 to 3.6cm(2) and from 3.4 to 5.4cm(2), the centroid radius ranged from 4.1 to 5.1cm and from 4.7 to 5.7cm, and the centroid angle ranged from 75 degrees to 131 degrees and from 4.8 degrees to 131.2 degrees, respectively. For the multifidus muscle, the area ranged from 1.7 to 3.9cm(2) and from 2.4 to 4.2cm(2), the radius ranged from 3.1 to 3.4cm and from 3.3 to 3.8cm, the angle ranged from 15 degrees to 24.4 degrees and 16.2 degrees to 24.4 degrees, respectively. Results from all levels for both muscles and male and female spines are given. Conclusions The cross-sectional area, angulation, and centroid radii data for flexor and extensor muscles of the cervical spine serve as a dataset that may be used to better define morphologies in computational models and obtain segmental motions and loads under external mechanical forces. These data can be used in computational models for injury prevention, mitigation, and readiness.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要