Autosomal dominant polycystic kidney disease (ADPKD) is the fourth most common cause of kidney transplant worldwide accounting for 7-10% of all cases. Although ADPKD usually progresses over many decades, accurate risk prediction is an important task.1 Identifying patients with progressive disease is vital to providing new treatments being developed and enable them to enter clinical trials for new therapy. Among other factors, total kidney volume (TKV) is a major biomarker predicting the progression of ADPKD. Consortium for Radiologic Imaging Studies in Polycystic Kidney Disease (CRISP)2 have shown that TKV is an early, and accurate measure of cystic burden and likely growth rate. It is strongly associated with loss of renal function.3 While ultrasound (US) has proven as an excellent tool for diagnosing the disease; monitoring short-term changes using ultrasound has been shown to not be accurate. This is attributed to high operator variability and reproducibility as compared to tomographic modalities such as CT and MR (Gold standard). Ultrasound has emerged as one of the standout modality for intra-procedural imaging and with methods for spatial localization has afforded us the ability to track 2D ultrasound in physical space which it is being used. In addition to this, the vast amount of recorded tomographic data can be used to generate statistical shape models that allow us to extract clinical value from archived image sets. In this work, we aim at improving the prognostic value of US in managing ADPKD by assessing the accuracy of using statistical shape model augmented US data, to predict TKV, with the end goal of monitoring short-term changes.
Degradation and injury of the rotator cuff is one of the most common diseases of the shoulder among the general population. In orthopedic injuries, rotator cuff disease is only second to back pain in terms of overall reduced quality of life for patients. Clinically, this disease is managed via pain and activity assessment and diagnostic imaging using ultrasound and MRI. Ultrasound has been shown to have good accuracy for identification and measurement of rotator cuff tears. In our previous work, we have developed novel, real-time techniques to biomechanically assess the condition of the rotator cuff based on Musculoskeletal Ultrasound. Of the rotator cuff tissues, supraspinatus is the first that sees degradation and is the most commonly affected. In our work, one of the challenges lies in effectively segmenting and characterizing the supraspinatus. We are exploring the possibility of using curvelet transform for improving techniques to segment tissue in ultrasound. Curvelets have been shown to give optimal multi-scale representation of edges in images. They are designed to represent edges and singularities along curves in images which makes them an attractive proposition for use in ultrasound segmentation. In this work, we present a novel approach to the possibility of using curvelet transforms for automatic edge and feature extraction for the supraspinatus.
Rotator cuff disease is a degenerative disorder that is a common, costly, and often debilitating, ranging in severity from partial thickness tear, which may cause pain, to total rupture, leading to loss in function. Currently, clinical diagnosis and determination of disease extent relies primarily on subjective assessment of pain, range of motion, and possibly X-ray or ultrasound images. The final treatment plan however is at the discretion of the clinician, who often bases their decision on personal experiences, and not quantitative standards.
The use of ultrasound for the assessment of tissue biomechanics is established, such as in ultrasound elastography, where soft tissue biomechanics are measured. Few studies have investigated the use of ultrasound elastography in the characterization of musculoskeletal biomechanics. To assess tissue biomechanics we have developed a device, which measures the force applied to the underlying musculotendentious tissue while simultaneously obtaining the related ultrasound images. In this work, the musculotendinous region of the infraspinatus of twenty asymptomatic male organized baseball players was examined to access the variability in tissue properties within a single patient and across a normal population. Elastic moduli at percent strains less than 15 were significantly different than those above 15 percent strain within the normal population. No significant difference in tissue properties was demonstrated within a single patient. This analysis demonstrated elastic moduli are variable across individuals and incidence. Therefore threshold elastic moduli will likely be a function of variation in local-tissue moduli as opposed to a specific global value.
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