Objective: Identify neck muscle boundaries using real-time ultrasound in cervical dystonia and relate imaging data to neck muscle structure and function.

Background: Identification of individual neck muscles is a crucial unmet need in assessment and treatment of cervical dystonia. We have shown a method for segmentation of posterior neck muscles using real-time ultrasound in healthy control participants [1]. This study is part of the development of a tool to assess neck muscle structure and function in patients with cervical dystonia.

Methods: We recruited 35 patients with idiopathic cervical dystonia (mean age 61±10 years) and 25 healthy controls (mean age 59±14 years). Dystonia severity was quantified using the TWSTRS score. Magnetic resonance (MR) imaging and real-time ultrasound of the neck muscles, and motion capture analysis were performed in all participants. Neck muscle boundaries were defined using annotation of the MR and ultrasound data, and also by machine learning. Analysis of the shape and cross-sectional area of cervical muscles was performed.

Results: The mean TWSTRS motor score in dystonia patients was 15.4±2.9, indicating moderately severe dystonia. Neck muscle boundaries could be identified using muscle annotation, but machine learning offers additional benefits in terms of accuracy.

Conclusions: Real-time ultrasound is able to define neck muscle anatomy in patients with cervical dystonia. Further work is required to determine the relationship between changes in ultrasound shape and texture and neck muscle function. Our final goal is a tool for identification of dystonic muscle groups which can be applied in real time in the clinical management of cervical dystonia.

References: [1] Cunningham RJ, Harding PJ, Loram ID. Real-time ultrasound segmentation, analysis and visualization of deep cervical muscle structure. IEEE Trans Med Imaging 2017;36:653-665.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/segmentation-of-neck-muscles-using-ultrasound-in-cervical-dystonia/