Biomechanics

Biomechanics is a vast discipline within the field of Biomedical Engineering. It explores the underlying mechanics of how biological and physiological systems move. It encompasses important clinical applications to address questions related to medicine using engineering mechanics principles. Biomechanics includes interdisciplinary concepts from engineers, physicians, therapists, biologists, physicists, and mathematicians. Through their collaborative efforts, biomechanics research is ever changing and expanding, explaining new mechanisms and principles for dynamic human systems. Biomechanics is used to describe how the human body moves, walks, and breathes, in addition to how it responds to injury and rehabilitation. Advanced biomechanical modeling methods, such as inverse dynamics, finite element analysis, and musculoskeletal modeling are used to simulate and investigate human situations in regard to movement and injury. Biomechanical technologies are progressing to answer contemporary medical questions. The future of biomechanics is dependent on interdisciplinary research efforts and the education of tomorrow’s scientists

Deformable mapping technique to correlate lesions in digital breast tomosynthesis and automated breast ultrasound images

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146324/1/mp13113.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146324/2/mp13113_am.pd

IMAGE-BASED RESPIRATORY MOTION EXTRACTION AND RESPIRATION-CORRELATED CONE BEAM CT (4D-CBCT) RECONSTRUCTION

Accounting for respiration motion during imaging helps improve targeting precision in radiation therapy. Respiratory motion can be a major source of error in determining the position of thoracic and upper abdominal tumor targets during radiotherapy. Thus, extracting respiratory motion is a key task in radiation therapy planning. Respiration-correlated or four-dimensional CT (4DCT) imaging techniques have been recently integrated into imaging systems for verifying tumor position during treatment and managing respiration-induced tissue motion. The quality of the 4D reconstructed volumes is highly affected by the respiratory signal extracted and the phase sorting method used. This thesis is divided into two parts. In the first part, two image-based respiratory signal extraction methods are proposed and evaluated. Those methods are able to extract the respiratory signals from CBCT images without using external sources, implanted markers or even dependence on any structure in the images such as the diaphragm. The first method, called Local Intensity Feature Tracking (LIFT), extracts the respiratory signal depending on feature points extracted and tracked through the sequence of projections. The second method, called Intensity Flow Dimensionality Reduction (IFDR), detects the respiration signal by computing the optical flow motion of every pixel in each pair of adjacent projections. Then, the motion variance in the optical flow dataset is extracted using linear and non-linear dimensionality reduction techniques to represent a respiratory signal. Experiments conducted on clinical datasets showed that the respiratory signal was successfully extracted using both proposed methods and it correlates well with standard respiratory signals such as diaphragm position and the internal markers’ signal. In the second part of this thesis, 4D-CBCT reconstruction based on different phase sorting techniques is studied. The quality of the 4D reconstructed images is evaluated and compared for different phase sorting methods such as internal markers, external markers and image-based methods (LIFT and IFDR). Also, a method for generating additional projections to be used in 4D-CBCT reconstruction is proposed to reduce the artifacts that result when reconstructing from an insufficient number of projections. Experimental results showed that the feasibility of the proposed method in recovering the edges and reducing the streak artifacts

Real-time intrafraction motion monitoring in external beam radiotherapy

© 2019 Institute of Physics and Engineering in Medicine. Radiotherapy (RT) aims to deliver a spatially conformal dose of radiation to tumours while maximizing the dose sparing to healthy tissues. However, the internal patient anatomy is constantly moving due to respiratory, cardiac, gastrointestinal and urinary activity. The long term goal of the RT community to 'see what we treat, as we treat' and to act on this information instantaneously has resulted in rapid technological innovation. Specialized treatment machines, such as robotic or gimbal-steered linear accelerators (linac) with in-room imaging suites, have been developed specifically for real-time treatment adaptation. Additional equipment, such as stereoscopic kilovoltage (kV) imaging, ultrasound transducers and electromagnetic transponders, has been developed for intrafraction motion monitoring on conventional linacs. Magnetic resonance imaging (MRI) has been integrated with cobalt treatment units and more recently with linacs. In addition to hardware innovation, software development has played a substantial role in the development of motion monitoring methods based on respiratory motion surrogates and planar kV or Megavoltage (MV) imaging that is available on standard equipped linacs. In this paper, we review and compare the different intrafraction motion monitoring methods proposed in the literature and demonstrated in real-time on clinical data as well as their possible future developments. We then discuss general considerations on validation and quality assurance for clinical implementation. Besides photon RT, particle therapy is increasingly used to treat moving targets. However, transferring motion monitoring technologies from linacs to particle beam lines presents substantial challenges. Lessons learned from the implementation of real-time intrafraction monitoring for photon RT will be used as a basis to discuss the implementation of these methods for particle RT

EVA Glove Research Team

The goal of the basic research portion of the extravehicular activity (EVA) glove research program is to gain a greater understanding of the kinematics of the hand, the characteristics of the pressurized EVA glove, and the interaction of the two. Examination of the literature showed that there existed no acceptable, non-invasive method of obtaining accurate biomechanical data on the hand. For this reason a project was initiated to develop magnetic resonance imaging as a tool for biomechanical data acquisition and visualization. Literature reviews also revealed a lack of practical modeling methods for fabric structures, so a basic science research program was also initiated in this area

Quantitative Analysis of Three-Dimensional Cone-Beam Computed Tomography Using Image Quality Phantoms

In the clinical setting, weight-bearing static 2D radiographic imaging and supine 3D radiographic imaging modalities are used to evaluate radiographic changes such as, joint space narrowing, subchondral sclerosis, and osteophyte formation. These respective imaging modalities cannot distinguish between tissues with similar densities (2D imaging), and do not accurately represent functional joint loading (supine 3D imaging). Recent advances in cone-beam CT (CBCT) have allowed for scanner designs that can obtain weight-bearing 3D volumetric scans. The purpose of this thesis was to analyze, design, and implement advanced imaging techniques to quantify image quality parameters of reconstructed image volumes generated by a commercially-available CBCT scanner, and a novel ceiling-mounted CBCT scanner. In addition, imperfections during rotation of the novel ceiling-mounted CBCT scanner were characterized using a 3D printed calibration object with a modification to the single marker bead method, and prospective geometric calibration matrices

On uncertainty propagation in image-guided renal navigation: Exploring uncertainty reduction techniques through simulation and in vitro phantom evaluation

Image-guided interventions (IGIs) entail the use of imaging to augment or replace direct vision during therapeutic interventions, with the overall goal is to provide effective treatment in a less invasive manner, as an alternative to traditional open surgery, while reducing patient trauma and shortening the recovery time post-procedure. IGIs rely on pre-operative images, surgical tracking and localization systems, and intra-operative images to provide correct views of the surgical scene. Pre-operative images are used to generate patient-specific anatomical models that are then registered to the patient using the surgical tracking system, and often complemented with real-time, intra-operative images. IGI systems are subject to uncertainty from several sources, including surgical instrument tracking / localization uncertainty, model-to-patient registration uncertainty, user-induced navigation uncertainty, as well as the uncertainty associated with the calibration of various surgical instruments and intra-operative imaging devices (i.e., laparoscopic camera) instrumented with surgical tracking sensors. All these uncertainties impact the overall targeting accuracy, which represents the error associated with the navigation of a surgical instrument to a specific target to be treated under image guidance provided by the IGI system. Therefore, understanding the overall uncertainty of an IGI system is paramount to the overall outcome of the intervention, as procedure success entails achieving certain accuracy tolerances specific to individual procedures. This work has focused on studying the navigation uncertainty, along with techniques to reduce uncertainty, for an IGI platform dedicated to image-guided renal interventions. We constructed life-size replica patient-specific kidney models from pre-operative images using 3D printing and tissue emulating materials and conducted experiments to characterize the uncertainty of both optical and electromagnetic surgical tracking systems, the uncertainty associated with the virtual model-to-physical phantom registration, as well as the uncertainty associated with live augmented reality (AR) views of the surgical scene achieved by enhancing the pre-procedural model and tracked surgical instrument views with live video views acquires using a camera tracked in real time. To better understand the effects of the tracked instrument calibration, registration fiducial configuration, and tracked camera calibration on the overall navigation uncertainty, we conducted Monte Carlo simulations that enabled us to identify optimal configurations that were subsequently validated experimentally using patient-specific phantoms in the laboratory. To mitigate the inherent accuracy limitations associated with the pre-procedural model-to-patient registration and their effect on the overall navigation, we also demonstrated the use of tracked video imaging to update the registration, enabling us to restore targeting accuracy to within its acceptable range. Lastly, we conducted several validation experiments using patient-specific kidney emulating phantoms using post-procedure CT imaging as reference ground truth to assess the accuracy of AR-guided navigation in the context of in vitro renal interventions. This work helped find answers to key questions about uncertainty propagation in image-guided renal interventions and led to the development of key techniques and tools to help reduce optimize the overall navigation / targeting uncertainty