OPTIMIZATION OF DEDICATED BREAST COMPUTED TOMOGRAPHY: BOWTIE FILTER DESIGN AND OPTIMAL SPECTRUM ANALYSIS

Recently, researchers have been investigating the use of a new imaging modality called dedicated breast CT as a means of alleviating the problem of tissue superposition that comes from acquiring a two-dimensional image of a three-dimensional object in conventional mammography. Several groups have investigated the optimal spectrum for this new imaging modality using the dose efficiency as the FOM, but results are inconsistent. None of these groups have employed the use of bowtie filtration in their optimal spectrum studies. Given the right design, the inclusion of bowtie filtration will lead to improved dose efficiency as well as consistency in the metric independent of position in a given phantom. Bowtie filters can improve performance in several ways, including DR reduction, scatter reduction, patient dose reduction, and reduction of beam-hardening effects. In this dissertation, three different filter types with different choices for the tradeoffs between the performance improvements listed above are described. Examples of each type of bowtie filter are created for computational and Monte Carlo analyses, and two designs were fabricated for experimental analysis. Studies analyzing the material selection for each bowtie filter design and characterizing the scatter were also completed. Verification of the performance of the designs was done by calculating/measuring the HVL, intensity, and µeff behind the phantom as a function of fan-angle. The performance of the designs depended only weakly on incident spectrum and tissue composition. With various breast diameters, the calculated parameters varied the most, but the variation was substantially less than the no-bowtie filter case. For all designs, the DR requirement on the detector was reduced compared to the no-bowtie filter case. Simulation and experimental data showed that the use of our bowtie filters can reduce the peripheral dose to the breast by 61%, and provide uniform noise and CNR distributions. The best performing bowtie filter design was implemented in simulation studies analyzing the optimal spectrum through calculation of the dose efficiency metric. The results from this study show the improvement and consistency that can be obtained with the inclusion of the proper bowtie filter, and provide the research community with a methodology that will help lead to more consistent optimal spectrum results

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

Kinematic analysis of motor learning in upper limb body-powered bypass prosthesis training.

Motor learning and compensatory movement are important aspects of prosthesis training yet relatively little quantitative evidence supports our current understanding of how motor control and compensation develop in the novel body-powered prosthesis user. The goal of this study is to assess these aspects of prosthesis training through functional, kinematic, and kinetic analyses using a within-subject paradigm compared across two training time points. The joints evaluated include the left and right shoulders, torso, and right elbow. Six abled-bodied subjects (age 27 ± 3) using a body-powered bypass prosthesis completed the Jebsen-Taylor Hand Function Test and the targeted Box and Blocks Test after five training sessions and again after ten sessions. Significant differences in movement parameters included reduced times to complete tasks, reduced normalized jerk for most joints and tasks, and more variable changes in efficiency and compensation parameters for individual tasks and joints measured as range of motion, maximum angle, and average moment. Normalized jerk, joint specific path length, range of motion, maximum angle, and average moment are presented for the first time in this unique training context and for this specific device type. These findings quantitatively describe numerous aspects of motor learning and control in able-bodied subjects that may be useful in guiding future rehabilitation and training of body-powered prosthesis users

Application of machine learning to the identification of joint degrees of freedom involved in abnormal movement during upper limb prosthesis use.

To evaluate movement quality of upper limb (UL) prosthesis users, performance-based outcome measures have been developed that examine the normalcy of movement as compared to a person with a sound, intact hand. However, the broad definition of "normal movement" and the subjective nature of scoring can make it difficult to know which areas of the body to evaluate, and the expected magnitude of deviation from normative movement. To provide a more robust approach to characterizing movement differences, the goals of this work are to identify degrees of freedom (DOFs) that will inform abnormal movement for several tasks using unsupervised machine learning (clustering methods) and elucidate the variations in movement approach across two upper-limb prosthesis devices with varying DOFs as compared to healthy controls. 24 participants with no UL disability or impairment were recruited for this study and trained on the use of a body-powered bypass (n = 6) or the DEKA limb bypass (n = 6) prosthetic devices or included as normative controls. 3D motion capture data were collected from all participants as they performed the Jebsen-Taylor Hand Function Test (JHFT) and targeted Box and Blocks Test (tBBT). Range of Motion, peak angle, angular path length, mean angle, peak angular velocity, and number of zero crossings were calculated from joint angle data for the right/left elbows, right/left shoulders, torso, and neck and fed into a K-means clustering algorithm. Results show right shoulder and torso DOFs to be most informative in distinguishing between bypass user and norm group movement. The JHFT page turning task and the seated tBBT elicit movements from bypass users that are most distinctive from the norm group. Results can be used to inform the development of movement quality scoring methodology for UL performance-based outcome measures. Identifying tasks across two different devices with known variations in movement can inform the best tasks to perform in a rehabilitation setting that challenge the prosthesis user's ability to achieve normative movement

Comparison of Motion Analysis Systems in Tracking Upper Body Movement of Myoelectric Bypass Prosthesis Users

Current literature lacks a comparative analysis of different motion capture systems for tracking upper limb (UL) movement as individuals perform standard tasks. To better understand the performance of various motion capture systems in quantifying UL movement in the prosthesis user population, this study compares joint angles derived from three systems that vary in cost and motion capture mechanisms: a marker-based system (Vicon), an inertial measurement unit system (Xsens), and a markerless system (Kinect). Ten healthy participants (5F/5M; 29.6 ± 7.1 years) were trained with a TouchBionic i-Limb Ultra myoelectric terminal device mounted on a bypass prosthetic device. Participants were simultaneously recorded with all systems as they performed standardized tasks. Root mean square error and bias values for degrees of freedom in the right elbow, shoulder, neck, and torso were calculated. The IMU system yielded more accurate kinematics for shoulder, neck, and torso angles while the markerless system performed better for the elbow angles. By evaluating the ability of each system to capture kinematic changes of simulated upper limb prosthesis users during a variety of standardized tasks, this study provides insight into the advantages and limitations of using different motion capture technologies for upper limb functional assessment

Comparison of Motion Analysis Systems in Tracking Upper Body Movement of Myoelectric Bypass Prosthesis Users

Current literature lacks a comparative analysis of different motion capture systems for tracking upper limb (UL) movement as individuals perform standard tasks. To better understand the performance of various motion capture systems in quantifying UL movement in the prosthesis user population, this study compares joint angles derived from three systems that vary in cost and motion capture mechanisms: a marker-based system (Vicon), an inertial measurement unit system (Xsens), and a markerless system (Kinect). Ten healthy participants (5F/5M; 29.6 ± 7.1 years) were trained with a TouchBionic i-Limb Ultra myoelectric terminal device mounted on a bypass prosthetic device. Participants were simultaneously recorded with all systems as they performed standardized tasks. Root mean square error and bias values for degrees of freedom in the right elbow, shoulder, neck, and torso were calculated. The IMU system yielded more accurate kinematics for shoulder, neck, and torso angles while the markerless system performed better for the elbow angles. By evaluating the ability of each system to capture kinematic changes of simulated upper limb prosthesis users during a variety of standardized tasks, this study provides insight into the advantages and limitations of using different motion capture technologies for upper limb functional assessment

Kinematic trajectories and RoM of the right shoulder.

<p>Kinematic trajectories and RoM of the right shoulder for the sBBT (blue), mBBT (green), and tBBT (red). (A)Shoulder flexion and (C) shoulder abduction trajectories for each test. (B) Shoulder flexion RoM and (D) shoulder abduction RoM for each test.</p

Description of tests.

<p>Description of tests.</p

Targeted box and blocks test: Normative data and comparison to standard tests

<div><p>Background</p><p>The Box and Block Test (BBT) is a functional outcome measure that is commonly used across multiple clinical populations due to its benefits of ease and speed of implementation; reliable, objective measurement; and repetition of motion. In this study, we introduce a novel outcome measure called the targeted BBT that allows for the study of initiation, grasping, and transport of objects, and also of object release. These modifications to the existing test may increase the ecological validity of the measure while still retaining the previously stated benefits of the standard BBT.</p><p>Methods</p><p>19 able-bodied subjects performed the targeted BBT and two other standard tests. Using an integrated movement analysis framework based on motion capture and ground force data, quantitative information about how subjects completed these tests were captured. Kinematic parameters at the wrist, elbow, shoulder, thorax, and head, as well as measures of postural control, were calculated and statistically compared across the three tests.</p><p>Results</p><p>In general, the targeted BBT required significantly higher RoM at the elbow, shoulder, thorax and head when compared to standard tests. Peak angles at these joints were also higher during performance of the targeted BBT. Peak angles and RoM values for the targeted BBT were close to those found in studies of movements of able-bodied individuals performing activities of daily living.</p><p>Conclusion</p><p>The targeted BBT allows analysis of repetitive movements, and may more closely model common real-world object manipulation scenarios in which a user is required to control a movement from pick-up to release.</p></div

Kinematic trajectories and RoM of the thorax.

<p>Kinematic trajectories and RoM of the thorax for the sBBT (blue), mBBT (green), and tBBT (red). (A) Forward tilt and (C) left tilt trajectories for each test. (B) Forward tilt RoM and (D) left tilt RoM for each test.</p