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A 3D Neurovascular Bundles Segmentation Method based on MR-TRUS Deformable Registration.

Xiaofeng Yang1, Peter Rossi1, Ashesh B Jani1

  • 1Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.

Proceedings of Spie--The International Society for Optical Engineering
|August 31, 2019
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Summary

This study introduces a novel 3D neurovascular bundle (NVB) segmentation method for ultrasound images by integrating MRI and transrectal ultrasound data. The technique accurately maps NVB from MRI to ultrasound, aiding in prostate cancer radiation therapy.

Keywords:
MRI-US registrationNeurovascular bundles (NVB)erectile dysfunctionsegmentation

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Area of Science:

  • Medical Imaging
  • Radiotherapy Physics
  • Urology

Background:

  • Accurate segmentation of neurovascular bundles (NVB) is crucial for sparing them during prostate radiation therapy (RT).
  • Integrating magnetic resonance (MR) imaging and transrectal ultrasound (TRUS) offers complementary information for precise anatomical delineation.
  • Existing methods may lack the accuracy needed for robust NVB segmentation in the context of prostate cancer treatment.

Purpose of the Study:

  • To develop and validate a 3D neurovascular bundle (NVB) segmentation method for ultrasound (US) images by integrating MR and transrectal ultrasound (TRUS) images.
  • To improve the precision of NVB localization for radiation therapy planning and outcome prediction.
  • To assess the accuracy and feasibility of a novel MR-TRUS deformable registration technique for NVB segmentation.

Main Methods:

  • A 3D NVB segmentation method was proposed using MR-TRUS deformable registration to integrate MR and TRUS images.
  • Neurovascular bundles (NVB) were initially contoured in 3D MR images by a physician.
  • A novel MR-TRUS registration method, modeling prostate tissue as elastic, was employed to transform MR-defined NVB into US images, jointly estimating deformations.

Main Results:

  • MR-TRUS registration was successfully performed in all 6 patients undergoing prostate cancer RT.
  • The mean landmark displacement between post-registration MR and TRUS images was less than 2 mm.
  • The average NVB volume Dice Overlap Coefficient exceeded 89%, demonstrating high segmentation accuracy.

Conclusions:

  • The proposed 3D NVB segmentation technique, integrating MR and TRUS images via deformable registration, is accurate and feasible.
  • This method can serve as a valuable tool for sparing NVB during prostate RT, monitoring treatment response, and potentially enhancing post-RT potency outcomes.
  • The high overlap coefficient and low landmark displacement validate the clinical utility of this approach for improving prostate cancer radiotherapy.