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8 March 2019 Automatic applicator digitization for MRI-based cervical cancer brachytherapy planning using two surface models
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Abstract
Modern image-guided cervical cancer brachytherapy involves the insertion of hollow applicators in the uterus and surrounding the cervix to deliver a radioactive source. These applicators are imaged and manually digitized following insertion for treatment planning. We present an algorithm to automatically digitize these applicators using MRI for cervical cancer brachytherapy planning. Applicators were digitized in vivo using T2-weighted MR images (1.5 T) from 21 brachytherapy fractions including 9 patients. The model-to-image registration algorithm was implemented in C++ involving a 2D matched filter to identify the applicator center, and a 3D surface model to identify local position by maximizing the intensity gradient normal to the surface. Surface models were produced using training MR images. Errors in the algorithm results were calculated as the 3D distances of the applicator tip and center from those identified manually. A model based on manufacturer data was also used for applicator digitization to assess algorithm sensitivity to surface model variation. The algorithm correctly identified the applicator in 20 out of 21 images with mean execution time of 2.5 s. Mean±SD error following digitization using the MRI and manufacturer-based surface models was 1.2±0.6 mm and 1.3±0.7 mm for the tandem tip (p = 0.52), and 1.4±0.9 mm and 1.3±0.7 mm for the ring center (p = 0.61). The algorithm requires no manual initialization with consistent results across surface models, showing promise for clinical implementation.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
William T. Hrinivich, Marc Morcos, Akila Viswanathan, Todd McNutt, and Junghoon Lee "Automatic applicator digitization for MRI-based cervical cancer brachytherapy planning using two surface models", Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 1095116 (8 March 2019); https://doi.org/10.1117/12.2513003
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