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Updated: Jun 23, 2026

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
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Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

Simultaneous multi-modality ROI delineation in clinical practice.

Gijsbert H Bol1, Alexis N T J Kotte, Uulke A van der Heide

  • 1Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands. G.H.Bol@umcutrecht.nl

Computer Methods and Programs in Biomedicine
|May 16, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for radiation therapy planning, improving tumor delineation using multiple imaging types. The system enhances accuracy by transforming delineations between different image datasets without resampling.

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Area of Science:

  • Medical Imaging
  • Radiation Oncology
  • Image Processing

Background:

  • Accurate tumor and organ delineation is crucial for effective radiation therapy planning.
  • Multiple imaging modalities like CT, MRI, and PET improve delineation quality.
  • Existing tools often require resampling, potentially degrading image quality.

Purpose of the Study:

  • To present a practical framework for utilizing multi-modal imaging datasets during radiation therapy delineation.
  • To introduce a novel approach that transforms delineations between datasets instead of resampling images.

Main Methods:

  • Displaying all image datasets in their original form (coordinate system, resolution, voxel aspect ratio).
  • Allowing delineations on all orthogonal views of each dataset with real-time visualization across all sets.
  • Utilizing rigid normalized mutual information registration for transforming delineations between modalities.

Main Results:

  • The framework enables direct feedback to the delineator by visualizing changes across all image sets.
  • The transformation method avoids image resampling, preserving original data integrity.
  • The system facilitates accurate and efficient multi-modal image-guided delineation.

Conclusions:

  • The presented framework offers a practical and effective solution for multi-modal image-guided radiation therapy planning.
  • Transforming delineations between datasets, rather than resampling, is a key innovation.
  • This approach enhances the accuracy and efficiency of the delineation process in radiation oncology.