David M. Kwartowitz

Mechanisms of Shoulder Range of Motion Deficits in Asymptomatic Baseball Players

Background: Shoulder range of motion (ROM) deficits have been identified as injury risk factors among baseball athletes. Despite the knowledge surrounding these risk factors, there is a lack of consensus regarding the specific tissues responsible for these deficits in ROM. Purpose/Hypothesis: The purpose of this study was to elucidate the primary mechanisms of posterior shoulder tightness (capsular, musculotendinous, bony) by examining the tissue responses that occur with the application of an acute intervention in baseball players with ROM deficits. The hypothesis was that posterior rotator cuff stiffness, not glenohumeral joint mobility, would be primarily responsible for ROM gains observed within an acute treatment setting. Study Design: Controlled laboratory study. Methods: Through use of ultrasound elastography, electromagnetic motion analysis, and ultrasound imaging, posterior rotator cuff stiffness, glenohumeral joint translation, and humeral torsion were examined in 60 asymptomatic baseball players (age, mean ± SD, 19 ± 2 years) with shoulder ROM deficits. Tissue mechanisms were examined concurrently, with the ROM gains elicited by an acute application of instrument-assisted soft tissue mobilization plus self-stretching (n = 30) versus self-stretching only (n = 30). Separate 3-way analyses of variance (group × arm × time) and linear regression analyses were used to determine the treatment effects and relationships between tissue mechanisms and ROM gains. Results: ROM gains were associated with decreases in rotator cuff stiffness (internal rotation: r = 0.35, P = .034; horizontal adduction: r = 0.44, P = .008) and increased humeral retrotorsion (internal rotation: r = −0.35, P = .034), not joint translation (P > .05). Players receiving instrument-assisted soft tissue mobilization plus stretching displayed greater shoulder ROM gains (internal rotation, +5° ± 2° [P = .010]; total arc of motion, +8° ± 6° [P = .010]; horizontal adduction, +7° ± 2° [P = .004]; and decreased posterior rotator cuff stiffness, −0.2 ± 0.3 kPa [P = .050]) compared with players receiving self-stretching alone. Conclusion: Decreases in rotator cuff stiffness were associated with acute ROM gains in baseball players. The study results show that changes in rotator cuff stiffness, not glenohumeral joint mobility or humeral torsion, are most likely associated with the ROM deficits observed in adolescent baseball players. Clinical Relevance: Reducing rotator cuff stiffness may be beneficial in improving the ROM deficits associated with injury risk in overhead athletes.

Toward real-time remote processing of laparoscopic video

Abstract. Laparoscopic surgery is a minimally invasive surgical technique where surgeons insert a small video camera into the patient’s body to visualize internal organs and use small tools to perform surgical procedures. However, the benefit of small incisions has a drawback of limited visualization of subsurface tissues, which can lead to navigational challenges in the delivering of therapy. Image-guided surgery uses the images to map subsurface structures and can reduce the limitations of laparoscopic surgery. One particular laparoscopic camera system of interest is the vision system of the daVinci-Si robotic surgical system (Intuitive Surgical, Sunnyvale, California). The video streams generate approximately 360 MB of data per second, demonstrating a trend toward increased data sizes in medicine, primarily due to higher-resolution video cameras and imaging equipment. Processing this data on a bedside PC has become challenging and a high-performance computing (HPC) environment may not always be available at the point of care. To process this data on remote HPC clusters at the typical 30 frames per second (fps) rate, it is required that each 11.9 MB video frame be processed by a server and returned within 1/30th of a second. The ability to acquire, process, and visualize data in real time is essential for the performance of complex tasks as well as minimizing risk to the patient. As a result, utilizing high-speed networks to access computing clusters will lead to real-time medical image processing and improve surgical experiences by providing real-time augmented laparoscopic data. We have performed image processing algorithms on a high-definition head phantom video (1920 × 1080 pixels) and transferred the video using a message passing interface. The total transfer time is around 53 ms or 19 fps. We will optimize and parallelize these algorithms to reduce the total time to 30 ms.

Quantitative modeling of the accuracy in registering preoperative patient-specific anatomic models into left atrial cardiac ablation procedures

PURPOSE In cardiac ablation therapy, accurate anatomic guidance is necessary to create effective tissue lesions for elimination of left atrial fibrillation. While fluoroscopy, ultrasound, and electroanatomic maps are important guidance tools, they lack information regarding detailed patient anatomy which can be obtained from high resolution imaging techniques. For this reason, there has been significant effort in incorporating detailed, patient-specific models generated from preoperative imaging datasets into the procedure. Both clinical and animal studies have investigated registration and targeting accuracy when using preoperative models; however, the effect of various error sources on registration accuracy has not been quantitatively evaluated. METHODS Data from phantom, canine, and patient studies are used to model and evaluate registration accuracy. In the phantom studies, data are collected using a magnetically tracked catheter on a static phantom model. Monte Carlo simulation studies were run to evaluate both baseline errors as well as the effect of different sources of error that would be present in a dynamic in vivo setting. Error is simulated by varying the variance parameters on the landmark fiducial, physical target, and surface point locations in the phantom simulation studies. In vivo validation studies were undertaken in six canines in which metal clips were placed in the left atrium to serve as ground truth points. A small clinical evaluation was completed in three patients. Landmark-based and combined landmark and surface-based registration algorithms were evaluated in all studies. In the phantom and canine studies, both target registration error and point-to-surface error are used to assess accuracy. In the patient studies, no ground truth is available and registration accuracy is quantified using point-to-surface error only. RESULTS The phantom simulation studies demonstrated that combined landmark and surface-based registration improved landmark-only registration provided the noise in the surface points is not excessively high. Increased variability on the landmark fiducials resulted in increased registration errors; however, refinement of the initial landmark registration by the surface-based algorithm can compensate for small initial misalignments. The surface-based registration algorithm is quite robust to noise on the surface points and continues to improve landmark registration even at high levels of noise on the surface points. Both the canine and patient studies also demonstrate that combined landmark and surface registration has lower errors than landmark registration alone. CONCLUSIONS In this work, we describe a model for evaluating the impact of noise variability on the input parameters of a registration algorithm in the context of cardiac ablation therapy. The model can be used to predict both registration error as well as assess which inputs have the largest effect on registration accuracy.

Efficacy of a novel IGS system in atrial septal defect repair

Congenital heart disease occurs in 107.6 out of 10,000 live births, with Atrial Septal Defects (ASD) accounting for 10% of these conditions. Historically, ASDs were treated with open heart surgery using cardiopulmonary bypass, allowing a patch to be sewn over the defect. In 1976, King et al. demonstrated use of a transcatheter occlusion procedure, thus reducing the invasiveness of ASD repair. Localization during these catheter based procedures traditionally has relied on bi-plane fluoroscopy; more recently trans-esophageal echocardiography (TEE) and intra-cardiac echocardiography (ICE) have been used to navigate these procedures. Although there is a high success rate using the transcatheter occlusion procedure, fluoroscopy poses radiation dose risk to both patient and clinician. The impact of this dose to the patients is important as many of those undergoing this procedure are children, who have an increased risk associated with radiation exposure. Their longer life expectancy than adults provides a larger window of opportunity for expressing the damaging effects of ionizing radiation. In addition, epidemiologic studies of exposed populations have demonstrated that children are considerably more sensitive to the carcinogenic effects radiation. Image-guided surgery (IGS) uses pre-operative and intra-operative images to guide surgery or an interventional procedure. Central to every IGS system is a software application capable of processing and displaying patient images, registration between multiple coordinate systems, and interfacing with a tool tracking system. We have developed a novel image-guided surgery framework called Kit for Navigation by Image Focused Exploration (KNIFE). In this work we assess the efficacy of this image-guided navigation system for ASD repair using a series of mock clinical experiments designed to simulate ASD repair device deployment.

Heuristic estimation of electromagnetically tracked catheter shape for image-guided vascular procedures

In our previous work we presented a novel image-guided surgery (IGS) system, Kit for Navigation by Image Focused Exploration (KNIFE).1,2 KNIFE has been demonstrated to be effective in guiding mock clinical procedures with the tip of an electromagnetically tracked catheter overlaid onto a pre-captured bi-plane fluoroscopic loop. Representation of the catheter in KNIFE differs greatly from what is captured by the fluoroscope, due to distortions and other properties of fluoroscopic images. When imaged by a fluoroscope, catheters can be visualized due to the inclusion of radiopaque materials (i.e. Bi, Ba, W) in the polymer blend.3 However, in KNIFE catheter location is determined using a single tracking seed located in the catheter tip that is represented as a single point overlaid on pre-captured fluoroscopic images. To bridge the gap in catheter representation between KNIFE and traditional methods we constructed a catheter with five tracking seeds positioned along the distal 70 mm of the catheter. We have currently investigated the use of four spline interpolation methods for estimation of true catheter shape and have assesed the error in their estimation of true catheter shape. In this work we present a method for the evaluation of interpolation algorithms with respect to catheter shape determination.

Assessment and Characterization of In Situ Rotator Cuff Biomechanics

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.

Statistical shape modeling based renal volume measurement using tracked ultrasound

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.

HPC enabled real-time remote processing of laparoscopic surgery

Laparoscopic surgery is a minimally invasive surgical technique. The benefit of small incisions has a disadvantage of limited visualization of subsurface tissues. Image-guided surgery (IGS) uses pre-operative and intra-operative images to map subsurface structures. One particular laparoscopic system is the daVinci-si robotic surgical system. The video streams generate approximately 360 megabytes of data per second. Real-time processing this large stream of data on a bedside PC, single or dual node setup, has become challenging and a high-performance computing (HPC) environment may not always be available at the point of care. To process this data on remote HPC clusters at the typical 30 frames per second rate, it is required that each 11.9 MB video frame be processed by a server and returned within 1/30th of a second. We have implement and compared performance of compression, segmentation and registration algorithms on Clemsons Palmetto supercomputer using dual NVIDIA K40 GPUs per node. Our computing framework will also enable reliability using replication of computation. We will securely transfer the files to remote HPC clusters utilizing an OpenFlow-based network service, Steroid OpenFlow Service (SOS) that can increase performance of large data transfers over long-distance and high bandwidth networks. As a result, utilizing high-speed OpenFlow- based network to access computing clusters with GPUs will improve surgical procedures by providing real-time medical image processing and laparoscopic data.

Towards predictive diagnosis and management of rotator cuff disease: using curvelet transform for edge detection and segmentation of tissue

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.

Towards real-time remote processing of laparoscopic video

Laparoscopic surgery is a minimally invasive surgical technique where surgeons insert a small video camera into the patients body to visualize internal organs and small tools to perform surgical procedures. However, the benefit of small incisions has a drawback of limited visualization of subsurface tissues, which can lead to navigational challenges in the delivering of therapy. Image-guided surgery (IGS) uses images to map subsurface structures and can reduce the limitations of laparoscopic surgery. One particular laparoscopic camera system of interest is the vision system of the daVinci-Si robotic surgical system (Intuitive Surgical, Sunnyvale, CA, USA). The video streams generate approximately 360 megabytes of data per second, demonstrating a trend towards increased data sizes in medicine, primarily due to higher-resolution video cameras and imaging equipment. Processing this data on a bedside PC has become challenging and a high-performance computing (HPC) environment may not always be available at the point of care. To process this data on remote HPC clusters at the typical 30 frames per second (fps) rate, it is required that each 11.9 MB video frame be processed by a server and returned within 1/30th of a second. The ability to acquire, process and visualize data in real-time is essential for performance of complex tasks as well as minimizing risk to the patient. As a result, utilizing high-speed networks to access computing clusters will lead to real-time medical image processing and improve surgical experiences by providing real-time augmented laparoscopic data. We aim to develop a medical video processing system using an OpenFlow software defined network that is capable of connecting to multiple remote medical facilities and HPC servers.