Browsing by Author "Ellingson, Arin M"

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    OrthoFusion: A Super-Resolution Algorithm to Fuse Orthogonal CT Volumes
    (2024-10-21) Ellingson, Arin M; ellin224@umn.edu; Ellingson, Arin M
    OrthoFusion, an intuitive super-resolution algorithm, is presented in this study to enhance the spatial resolution of clinical CT volumes. The efficacy of OrthoFusion is evaluated, relative to high-resolution CT volumes (ground truth), by assessing image volume and derived bone morphological similarity, as well as its performance in specific applications in 2D-3D registration tasks. Results demonstrate that OrthoFusion significantly reduced segmentation time, while improving structural similarity of bone images and relative accuracy of derived bone model geometries. Moreover, it proved beneficial in the context of biplane videoradiography, enhancing the similarity of digitally reconstructed radiographs to radiographic images and improving the accuracy of relative bony kinematics. OrthoFusion's simplicity, ease of implementation, and generalizability make it a valuable tool for researchers and clinicians seeking high spatial resolution from existing clinical CT data. This study opens new avenues for retrospectively utilizing clinical images for research and advanced clinical purposes, while reducing the need for additional scans, mitigating associated costs and radiation exposure.
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    Validation of an automated shape-matching algorithm for biplane radiographic spine osteokinematics and radiostereometric analysis error quantification
    (2020-01-24) Ellingson, Arin M; Kage, Craig C; ellin224@umn.edu; Ellingson, Arin M; Minnesota Rehabilitation Biomechanics Laboratory
    Biplane radiography and associated shape-matching provides non-invasive, dynamic, 3D osteo- and arthrokinematic analysis. Due to the complexity of data acquisition, each system should be validated for the anatomy of interest. The purpose of this study was to assess our system’s acquisition methods and validate a custom, automated 2D/3D shape-matching algorithm relative to radiostereometric analysis (RSA) for the cervical and lumbar spine. Additionally, two sources of RSA error were examined via a Monte Carlo simulation: 1) static bead centroid identification and 2) dynamic bead tracking error. Tantalum beads were implanted into a cadaver for RSA and cervical and lumbar spine flexion and lateral bending were passively simulated. A bead centroid identification reliability analysis was performed and a vertebral validation block was used to determine bead tracking accuracy. Our system’s overall root mean square error (RMSE) for the cervical spine ranged between 0.21-0.49mm and 0.42-1.80º and the lumbar spine ranged between 0.35-1.17mm and 0.49-1.06º. The RMSE associated with RSA ranged between 0.14-0.69mm and 0.96-2.33º for bead centroid identification and 0.25-1.19mm and 1.69-4.06º for dynamic bead tracking. The results of this study demonstrate our system’s ability to accurately quantify segmental spine motion. Additionally, RSA errors should be considered when interpreting biplane validation results.