Poster — Thur Eve — 71: A 4D Multimodal Lung Phantom for Regmentation Evaluation

Authors

  • Markel D,

    1. McGill University, Physics, Montreal QC, Canada
    2. McGill University, Oncology, Montreal QC, Canada
    3. Research Institute of the McGill University Health Centre, Montreal, QC, Canada
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  • Levesque I R,

    1. McGill University, Physics, Montreal QC, Canada
    2. McGill University, Oncology, Montreal QC, Canada
    3. Research Institute of the McGill University Health Centre, Montreal, QC, Canada
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  • El Naqa I

    1. McGill University, Physics, Montreal QC, Canada
    2. McGill University, Oncology, Montreal QC, Canada
    3. Research Institute of the McGill University Health Centre, Montreal, QC, Canada
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Abstract

Segmentation and registration of medical imaging data are two processes that can be integrated (a process termed regmentation) to iteratively reinforce each other, potentially improving efficiency and overall accuracy. A significant challenge is presented when attempting to validate the joint process particularly with regards to minimizing geometric uncertainties associated with the ground truth while maintaining anatomical realism. This work demonstrates a 4D MRI, PET, and CT compatible tissue phantom with a known ground truth for evaluating registration and segmentation accuracy. The phantom consists of a preserved swine lung connected to an air pump via a PVC tube for inflation. Mock tumors were constructed from sea sponges contained within two vacuum-sealed compartments with catheters running into each one for injection of radiotracer solution. The phantom was scanned using a GE Discovery-ST PET/CT scanner and a 0.23T Phillips MRI, and resulted in anatomically realistic images. A bifurcation tracking algorithm was implemented to provide a ground truth for evaluating registration accuracy. This algorithm was validated using known deformations of up to 7.8 cm using a separate CT scan of a human thorax. Using the known deformation vectors to compare against, 76 bifurcation points were selected. The tracking accuracy was found to have maximum mean errors of −0.94, 0.79 and −0.57 voxels in the left-right, anterior-posterior and inferior-superior directions, respectively. A pneumatic control system is under development to match the respiratory profile of the lungs to a breathing trace from an individual patient.

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