13 March 2017 3D printing for orthopedic applications: from high resolution cone beam CT images to life size physical models
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Abstract
With increasing resolution in image acquisition, the project explores capabilities of printing toward faithfully reflecting detail and features depicted in medical images. To improve safety and efficiency of orthopedic surgery and spatial conceptualization in training and education, this project focused on generating virtual models of orthopedic anatomy from clinical quality computed tomography (CT) image datasets and manufacturing life-size physical models of the anatomy using 3D printing tools. Beginning with raw micro CT data, several image segmentation techniques including thresholding, edge recognition, and region-growing algorithms available in packages such as ITK-SNAP, MITK, or Mimics, were utilized to separate bone from surrounding soft tissue. After converting the resulting data to a standard 3D printing format, stereolithography (STL), the STL file was edited using Meshlab, Netfabb, and Meshmixer. The editing process was necessary to ensure a fully connected surface (no loose elements), positive volume with manifold geometry (geometry possible in the 3D physical world), and a single, closed shell. The resulting surface was then imported into a “slicing” software to scale and orient for printing on a Flashforge Creator Pro. In printing, relationships between orientation, print bed volume, model quality, material use and cost, and print time were considered. We generated anatomical models of the hand, elbow, knee, ankle, and foot from both low-dose high-resolution cone-beam CT images acquired using the soon to be released scanner developed by Carestream, as well as scaled models of the skeletal anatomy of the arm and leg, together with life-size models of the hand and foot.
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Amiee Jackson, Lawrence A. Ray, Shusil Dangi, Yehuda K. Ben-Zikri, Cristian A. Linte, "3D printing for orthopedic applications: from high resolution cone beam CT images to life size physical models", Proc. SPIE 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, 101380T (13 March 2017); doi: 10.1117/12.2256181; https://doi.org/10.1117/12.2256181
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