Evaluates a new ultrasound system to assess muscle morphology, indicating a radiation-free alternative to MRI or CT.
Personalization of musculoskeletal spine models requires subject-specific paravertebral muscle morphology, which is commonly obtained using magnetic resonance imaging (MRI) or computed tomography (CT). However, these modalities are costly, time-intensive, and, in the case of CT, involve ionizing radiation. The purpose of this study was to develop a custom-designed rail-guided ultrasound system and evaluate its intra- and inter-rater reliability for standardized assessment of thoracolumbar paravertebral muscle morphology. The developed height-adjustable system guides a handheld ultrasound probe along three orthogonal axes and allows controlled rotation and application of a constant inward force to minimize operator-dependent variability. Twelve healthy adults participated in two measurement sessions within one week. Cross-sectional area (CSA) and muscle thickness (MT) were measured bilaterally at spinal levels L4, L1, and T8 using a curved-array transducer. Intra-rater reliability was assessed across sessions (n=12), and inter-rater reliability was evaluated in a subset of five participants in the first session. All measurements were analyzed using intraclass correlation coefficients (ICC), Bland-Altman plots, and root mean square error (RMSE). Intra-rater reliability was excellent for CSA (ICC[1,1]=0.994) and good for MT (ICC[1,1]=0.882). Inter-rater reliability was excellent for CSA (ICC[3,1]=0.952) and good for MT (ICC[3,1]=0.877). Bland-Altman analysis showed minimal bias and narrow limits of agreement for both parameters. The rail-guided ultrasound system enabled reproducible bilateral measurements across lumbar and thoracic levels. With further validation, this radiation-free, cost-effective, and laboratory-based approach may provide a feasible alternative to MRI or CT for obtaining subject-specific paravertebral muscle morphology and facilitate scalable personalization of musculoskeletal spine models.
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Strähl et al. (2026) studied this question.
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