JOURNAL ARTICLE
RESEARCH SUPPORT, NON-U.S. GOV'T
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Real-time segmentation of multiple implanted cylindrical liver markers in kilovoltage and megavoltage x-ray images.

Gold markers implanted in or near a tumor can be used as x-ray visible landmarks for image based tumor localization. The aim of this study was to develop and demonstrate fast and reliable real-time segmentation of multiple liver tumor markers in intra-treatment kV and MV images and in cone-beam CT (CBCT) projections, for real-time motion management. Thirteen patients treated with conformal stereotactic body radiation therapy in three fractions had 2-3 cylindrical gold markers implanted in the liver prior to treatment. At each fraction, the projection images of a pre-treatment CBCT scan were used for automatic generation of a 3D marker model that consisted of the size, orientation, and estimated 3D trajectory of each marker during the CBCT scan. The 3D marker model was used for real-time template based segmentation in subsequent x-ray images by projecting each marker's 3D shape and likely 3D motion range onto the imager plane. The segmentation was performed in intra-treatment kV images (526 marker traces, 92,097 marker projections) and MV images (88 marker traces, 22,382 marker projections), and in post-treatment CBCT projections (42 CBCT scans, 71,381 marker projections). 227 kV marker traces with low mean contrast-to-noise ratio were excluded as markers were not visible due to MV scatter. Online segmentation times measured for a limited dataset were used for estimating real-time segmentation times for all images. The percentage of detected markers was 94.8% (kV), 96.1% (MV), and 98.6% (CBCT). For the detected markers, the real-time segmentation was erroneous in 0.2-0.31% of the cases. The mean segmentation time per marker was 5.6 ms [2.1-12 ms] (kV), 5.5 ms [1.6-13 ms] (MV), and 6.5 ms [1.8-15 ms] (CBCT). Fast and reliable real-time segmentation of multiple liver tumor markers in intra-treatment kV and MV images and in CBCT projections was demonstrated for a large dataset.

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