Automatic tandem and ring reconstruction using MRI for cervical cancer brachytherapy

William T. Hrinivich, Marc Morcos, Akila Viswanathan, Junghoon Lee

Research output: Contribution to journalArticle

Abstract

Purpose: The MRI-guided cervical cancer brachytherapy provides unparalleled soft-tissue contrast for target and normal tissue contouring, but eliminates the ability to use conventional metallic fiducials for radiation source path reconstruction as required for treatment planning. Instead, the source path is reconstructed by manually aligning a library model to the signal void produced by the applicator, which takes time intraoperatively and precludes fully automated treatment planning. The purpose of this study is to present and validate an algorithm to automatically reconstruct tandem and ring applicators using MRI for cervical cancer brachytherapy treatment planning. Methods: Applicators were reconstructed using T2-weighted MR images acquired at 1.5 T from 33 brachytherapy fractions including 10 patients using a model-to-image registration algorithm. The algorithm involves (a) image filtering and maximum intensity projection to highlight the applicator, (b) ring center identification using the circular Hough transform, and (c) three-dimensional surface model registration, optimized by maximizing the image intensity gradient normal to the model surface. Two independent observers manually reconstructed all applicators, enabling the calculation of interobserver variability and establishing a ground truth. Algorithm variability was calculated by comparing algorithm results to each individual observer, and algorithm accuracy was calculated by comparing algorithm results to the ground truth. The algorithm variability and accuracy were compared to the interobserver variability using paired t-tests. Results: Mean ± SD interobserver variability was 0.83 ± 0.31 mm and 0.78 ± 0.29 mm for the ring and tandem, respectively. The algorithm had mean ± SD variability and accuracy of 0.72 ± 0.32 mm (P = 0.02) and 0.60 ± 0.24 mm (P = 0.0005) for the ring, and 0.70 ± 0.29 mm (P = 0.11) and 0.58 ± 0.24 mm (P = 0.004) for the tandem, respectively. Conclusions: The algorithm variability and accuracy were within the interobserver variability measured in this study. The algorithm accuracy and mean execution time of 10.0 s are sufficient for clinical tandem and ring reconstruction, and are a step toward fully automated tandem and ring brachytherapy treatment planning.

Original languageEnglish (US)
JournalMedical physics
DOIs
StateAccepted/In press - Jan 1 2019

Fingerprint

Brachytherapy
Uterine Cervical Neoplasms
Observer Variation
Therapeutics
Libraries
Radiation

Keywords

  • cervical cancer brachytherapy
  • HDR
  • intracavitary
  • MRI
  • surface registration

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

Cite this

Automatic tandem and ring reconstruction using MRI for cervical cancer brachytherapy. / Hrinivich, William T.; Morcos, Marc; Viswanathan, Akila; Lee, Junghoon.

In: Medical physics, 01.01.2019.

Research output: Contribution to journalArticle

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abstract = "Purpose: The MRI-guided cervical cancer brachytherapy provides unparalleled soft-tissue contrast for target and normal tissue contouring, but eliminates the ability to use conventional metallic fiducials for radiation source path reconstruction as required for treatment planning. Instead, the source path is reconstructed by manually aligning a library model to the signal void produced by the applicator, which takes time intraoperatively and precludes fully automated treatment planning. The purpose of this study is to present and validate an algorithm to automatically reconstruct tandem and ring applicators using MRI for cervical cancer brachytherapy treatment planning. Methods: Applicators were reconstructed using T2-weighted MR images acquired at 1.5 T from 33 brachytherapy fractions including 10 patients using a model-to-image registration algorithm. The algorithm involves (a) image filtering and maximum intensity projection to highlight the applicator, (b) ring center identification using the circular Hough transform, and (c) three-dimensional surface model registration, optimized by maximizing the image intensity gradient normal to the model surface. Two independent observers manually reconstructed all applicators, enabling the calculation of interobserver variability and establishing a ground truth. Algorithm variability was calculated by comparing algorithm results to each individual observer, and algorithm accuracy was calculated by comparing algorithm results to the ground truth. The algorithm variability and accuracy were compared to the interobserver variability using paired t-tests. Results: Mean ± SD interobserver variability was 0.83 ± 0.31 mm and 0.78 ± 0.29 mm for the ring and tandem, respectively. The algorithm had mean ± SD variability and accuracy of 0.72 ± 0.32 mm (P = 0.02) and 0.60 ± 0.24 mm (P = 0.0005) for the ring, and 0.70 ± 0.29 mm (P = 0.11) and 0.58 ± 0.24 mm (P = 0.004) for the tandem, respectively. Conclusions: The algorithm variability and accuracy were within the interobserver variability measured in this study. The algorithm accuracy and mean execution time of 10.0 s are sufficient for clinical tandem and ring reconstruction, and are a step toward fully automated tandem and ring brachytherapy treatment planning.",
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