Paper
20 March 2014 Improved apparatus for predictive diagnosis of rotator cuff disease
Anup Pillai, Brittany N. Hall, Charles A. Thigpen, David M. Kwartowitz
Author Affiliations +
Abstract
Rotator cuff disease impacts over 50% of the population over 60, with reports of incidence being as high as 90% within this population, causing pain and possible loss of function. The rotator cuff is composed of muscles and tendons that work in tandem to support the shoulder. Heavy use of these muscles can lead to rotator cuff tear, with the most common causes is age-related degeneration or sport injuries, both being a function of overuse. Tears ranges in severity from partial thickness tear to total rupture. Diagnostic techniques are based on physical assessment, detailed patient history, and medical imaging; primarily X-ray, MRI and ultrasonography are the chosen modalities for assessment. The final treatment technique and imaging modality; however, is chosen by the clinician is at their discretion. Ultrasound has been shown to have good accuracy for identification and measurement of full-thickness and partial-thickness rotator cuff tears. In this study, we report on the progress and improvement of our method of transduction and analysis of in situ measurement of rotator cuff biomechanics. We have improved the ability of the clinician to apply a uniform force to the underlying musculotendentious tissues while simultaneously obtaining the ultrasound image. This measurement protocol combined with region of interest (ROI) based image processing will help in developing a predictive diagnostic model for treatment of rotator cuff disease and help the clinicians choose the best treatment technique.
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Anup Pillai, Brittany N. Hall, Charles A. Thigpen, and David M. Kwartowitz "Improved apparatus for predictive diagnosis of rotator cuff disease", Proc. SPIE 9040, Medical Imaging 2014: Ultrasonic Imaging and Tomography, 904005 (20 March 2014); https://doi.org/10.1117/12.2043483
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KEYWORDS
Ultrasonography

Tissues

Diagnostics

Magnetic resonance imaging

Image processing

Image segmentation

Medical imaging

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