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Updated: Feb 15, 2026

Treatment of Liver Metastases Using an Internal Target Volume Method for Stereotactic Body Radiotherapy
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Surface tracking-assisted multi-cycle 4D MRI motion modeling for lung radiotherapy: a preliminary validation study.

Mumtaz Hussain Soomro1, Xiao Liang2, Steve Roys2

  • 1Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD 21201, United States of America.

Biomedical Physics & Engineering Express
|February 13, 2026
PubMed
Summary

This study validates a novel respiratory motion model using electromagnetic surface tracking and 4D MRI. The model accurately estimates breathing-induced changes for potential radiotherapy applications.

Keywords:
electromagnetic position trackinglung radiotherapymulti-cycle 4D MRIreal-time surface monitoringtime-resolvedvolumetric motion model

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Area of Science:

  • Medical Imaging
  • Radiotherapy Physics
  • Machine Learning in Medicine

Background:

  • Accurate modeling of respiratory motion is crucial for effective radiotherapy planning.
  • Current methods for tracking respiratory motion during MRI can be invasive or limited in scope.
  • Time-resolved multi-cycle 4D MRI (TRMC-MRI) offers detailed volumetric data but requires robust motion estimation.

Purpose of the Study:

  • To validate a non-invasive respiratory motion model using real-time electromagnetic (EM) surface tracking and TRMC-MRI.
  • To estimate respiration-induced internal anatomical changes within the entire irradiated volume.
  • To assess the feasibility of this model for MR-guided and conventional radiotherapy.

Main Methods:

  • Four volunteers underwent TRMC-MRI with concurrent real-time EM surface motion tracking.
  • Deformation vector fields (DVFs) were generated using deformable image registration.
  • Two machine learning models (PCA and ICA) were developed to map surface motion to internal DVFs.
  • Adaptive median filtering was applied to improve model performance.

Main Results:

  • Both PCA- and ICA-based models achieved comparable Mutual Information (MI) scores.
  • Adaptive median filtering significantly improved MI to ~66%, outperforming no filtering.
  • Diaphragm profile analysis showed close agreement with ground truth (mean RMSE 3.77-4.71 mm).

Conclusions:

  • This proof-of-concept study demonstrates the feasibility of a non-invasive respiratory motion model.
  • The model integrates EM-based surface tracking with TRMC-MRI for accurate motion estimation.
  • This approach holds potential for enhancing precision in radiotherapy targeting.