Category: Dystonia: Pathophysiology, Imaging
Objective: The aim was to assess the capabilities of using fractal analysis of neuromuscular ultrasound images to evaluate muscle health and spasticity.
Background: Ultrasound (US) is a valuable imaging modality for assessment muscle spasticity [10 and can predict muscular fitness. Mathematical algorithms like fractal analysis [2,3] can expand diagnostic value of diagnostic muscle imaging, providing an objective measurement using nonlinear mathematical parameters of structure (Fractal Dimension, FD), gives insights into muscle function and structure.
Method: We analyzed records of 22 patients (9 females; aged 18–72 y.o.) with various conditions affecting muscles: clinically diagnosed muscle pain with muscle trigger points, symptoms of spasticity (post-stroke patiets) with pre-/ post-tretment, athletes, elderly, overweight patients (BMI>30), with T2DM and healthy controls. All patients underwent general exam, precise physical tests, functional neuromuscular US using 4-8 MHz /5-12 MHz using M-mode. We evaluated muscle thickness (gastrocnemius, soleus muscles), CSA and motion, muscle trabecularity (number of visible bands/ lines per cm), detected trigger points, spasticity. We calculated FD manually using the basic generalized formula as in . We considered cross section areas / diameters of muscle, fascicles, its ratio and numbers of sub-units. M-mode was used to detect functional unit to be taken for evaluating.
Results: We detected different ultrasound pattern in spasticity, pain, T2DM and `healthy` subjects. we determined FD in trigger points, frailty, T2DM, in senile patients, women and athletes. Muscle structure in T2DM was as follows: increased echogenicity, more trabecular structure, enhanced network of hyperechoic bands 3-6/cm vs 5-10/cm with smaller hypoechoic areas (glycogen depos) ; lower motility, contractility (muscle contracted/rested thickness). FD was estimated as 1.52-1.66 for normal muscle tissue; in spasticity we detected hypoechoic areas with higher diameters, lower number of sub-units that led to decreased (<1.4) in areas of spasticity; was increased(> 1.7) or unchanged (1.5-1.62) in frailty and hypotrophia.
Conclusion: Muscle has typical fractal structure, can be easily calculated based on US CSA. Spasticity leads to losing complexity; postural systems can potentially hide complexity and should be the context of further studies.
References: 1. Bubnov RV. Ultrasonography for local muscle spasticity management. Mov Disord 2012, 27(Suppl 1):336. 2. Bubnov RV, Melnyk IM. The methods of fractal analysis of diagnostic images. Initial clinical experience. Lik Sprava. 2011 Apr-Jun;(3-4):108-13. 3. Bubnov R, Melnyk I. A novel approach to image analysis for hepatic oncology diagnosis based on fractal geometry. Preliminary results. J Hepatol. 2013;58:S258–259.
To cite this abstract in AMA style:R. Bubnov, M. Spivak. Fractal analysis of muscle ultrasound imaging to evaluate muscle health and spasticity [abstract]. Mov Disord. 2020; 35 (suppl 1). https://www.mdsabstracts.org/abstract/fractal-analysis-of-muscle-ultrasound-imaging-to-evaluate-muscle-health-and-spasticity/. Accessed December 7, 2023.
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