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Optimising inter-patient image registration for image-based data mining in breast radiotherapy.

Tanwiwat Jaikuna1,2, Fiona Wilson1, David Azria3

  • 1Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom.

Physics and Imaging in Radiation Oncology
|September 23, 2024
PubMed
Summary

Optimizing spatial normalization for breast image-based data mining (IBDM) is crucial. The B-spline algorithm with normalized mutual information (NMI) offers the best strategy for accurate breast IBDM, registering patient positions independently.

Keywords:
Breast radiotherapyImage registrationImage-based data miningSpatial normalisation

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Data Mining

Background:

  • Spatial normalization is essential for image-based data mining (IBDM) in breast radiotherapy.
  • Variations in patient positioning, breast shape, and volume present significant challenges for accurate spatial normalization.

Purpose of the Study:

  • To optimize spatial normalization techniques for breast image-based data mining (IBDM).
  • To evaluate different deformable image registration (DIR) configurations for accurate spatial normalization in breast radiotherapy.

Main Methods:

  • Utilized data from 996 early-stage breast cancer patients from the REQUITE study.
  • Tested four deformable image registration (DIR) configurations for extrathoracic spatial normalization.
  • Evaluated DIR accuracy using metrics such as Normalized Cross Correlation (NCC) and Dice Similarity Coefficient (DSC).

Main Results:

  • The B-spline algorithm with normalized mutual information (NMI) demonstrated superior performance across all evaluation metrics.
  • Independent registration of supine and prone cohorts yielded the highest accuracy.
  • Arm positioning in the supine cohort did not significantly impact registration performance.

Conclusions:

  • The B-spline algorithm utilizing NMI, with independent registration of supine and prone cohorts, is the optimal spatial normalization strategy for breast IBDM.
  • This optimized strategy minimizes uncertainty in the breast and shoulder regions, improving data reliability.