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Image-driven, model-based 3D abdominal motion estimation for MR-guided radiotherapy.

Bjorn Stemkens1, Rob H N Tijssen, Baudouin Denis de Senneville

  • 1Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.

Physics in Medicine and Biology
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Summary
This summary is machine-generated.

This study introduces a novel method to precisely track 3D tumor motion during radiotherapy using fast 2D MRI and a personalized motion model, improving accuracy and reducing margins for better patient outcomes.

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Area of Science:

  • Medical Physics
  • Radiotherapy Technology
  • Medical Imaging

Background:

  • Respiratory motion significantly impacts abdominal radiotherapy accuracy, necessitating large safety margins.
  • Advances in MR-Linac technology allow for real-time imaging during treatment.
  • Current volumetric MRI lacks the temporal resolution to accurately capture 3D organ motion.

Purpose of the Study:

  • To develop and validate a method for estimating high-resolution 3D deformation vector fields (DVFs) of organ motion during radiotherapy.
  • To improve the accuracy of tumor and organ-at-risk motion characterization in MR-guided radiotherapy.
  • To enable subject-specific dose accumulation and treatment evaluation.

Main Methods:

  • Utilized a subject-specific motion model derived from respiratory-correlated 4D-MRI and principal component analysis.
  • Integrated fast 2D cine-MR images acquired during treatment with the motion model.
  • Generated full field-of-view 3D DVFs with a temporal resolution of 476 ms.

Main Results:

  • Achieved geometrical accuracy of 1.0-1.5 mm using an MR-compatible motion phantom.
  • Validated the framework in seven healthy volunteers, with an average error of 1.45 mm for motion estimation.
  • Demonstrated the capability to generate high spatio-temporal resolution 3D DVFs.

Conclusions:

  • The proposed method accurately estimates 3D organ motion for MR-guided radiotherapy.
  • Calculated 3D DVFs can be used for retrospective treatment simulations and plan evaluations.
  • Enables precise, subject-specific dose calculations for tumors and organs-at-risk, potentially reducing treatment margins.