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Automatic Tandem and Ring Digitization for MRI-Guided Cervical Cancer Brachytherapy

WT Hrinivich*, M Morcos, J Lee, A Viswanathan, T McNutt, Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD


(Sunday, 7/29/2018) 2:05 PM - 3:00 PM

Room: Davidson Ballroom A

Purpose: MRI-guided cervical cancer brachytherapy provides unparalleled soft-tissue contrast for target and organ delineation; however, traditional fiducials cannot be used to identify the radiation source path for planning. Instead, a model is manually aligned to the MRI signal void produced by the applicator, providing the source path in-vivo. The purpose of this study is to develop and validate an algorithm to automatically digitize the applicator using MRI, towards the goal of real-time plan optimization.

Methods: Tandem and ring applicators were automatically digitized using anonymized T2-weighted MR images acquired at 1.5 T from 21 brachytherapy fractions including 9 patients. The two-step model matching algorithm was implemented in C++ involving a 2D matched filter to identify the ring center, and a 3D surface model to identify local position. The algorithm requires no manual initialization. Output is a DICOM-RT file containing the source path in-vivo, calculated using the surface model position and a calibration with our treatment planning system. Errors in the algorithm results were calculated as the 3D distances of the tandem tips and ring centers from those identified manually.

Results: Mean execution time of the algorithm was 2.5 s. The algorithm failed for 1 out of 21 images, identifying an air-filled rectum as a ring. For the successful 20 images, mean±SD error of the tandem tip and ring center was 1.2±0.7 mm and 1.4±1.0 mm, respectively. Mean±SD [x, y, z] signed error components of the tandem tip and ring center were [0.0±0.6, 0.4±0.5, 0.2±1.1] mm and [-0.2±0.5, -0.1±0.8, 0.7±1.3] mm, respectively.

Conclusion: The algorithm shows promise for real-time applicator digitization with mean error <1.5 mm. Biases in the error components suggest that accuracy could be improved by improving the surface model calibration. This algorithm is being refined for an intra-operative planning workflow to be validated in a larger patient cohort.


Brachytherapy, MRI, Image Processing


TH- Brachytherapy: GYN brachytherapy

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