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Registration error quantification of a surface-based multimodality image fusion system

P F Hemler1, S Napel, T S Sumanaweera

  • 1Stanford University Medical Center, Stanford University, California, USA.

Medical Physics
|July 1, 1995
PubMed
Summary
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This study introduces a new method for registering computerized tomography (CT) and magnetic resonance (MR) images. Surface-based registration accuracy was assessed using fiducial tubes, revealing that surface alignment does not guarantee accurate internal structure registration.

Area of Science:

  • Medical Imaging
  • Image Registration
  • Computational Anatomy

Background:

  • Accurate registration of multi-modal medical images like CT and MR is crucial for diagnosis and treatment planning.
  • Existing registration methods often lack robust validation, particularly concerning internal anatomical structures.

Purpose of the Study:

  • To develop and validate a novel surface-based system for CT-MR image registration.
  • To introduce a new reference dataset and quantification methodology for assessing registration accuracy.
  • To evaluate the system's performance using fiducial markers inserted in a cadaver.

Main Methods:

  • A semiautomatic surface-based system was developed for registering CT and MR images.
  • Registration involved identifying corresponding anatomical surfaces in each image modality.

Related Experiment Videos

  • A nonlinear optimization procedure determined the transformation for best surface alignment.
  • Registration accuracy was quantified using inserted fiducial tubes as ground truth.

    • Main Results:

    • The developed system achieved comparable root-mean-square (rms) distances at registered surfaces to existing methods.
    • However, rms distances measured for the fiducial tubes were significantly larger than surface-based rms distances.
    • Optimizing for minimal surface distance did not ensure minimal distance for the internal fiducial tubes.

    Conclusions:

    • Current surface-based registration methods may not accurately reflect the registration of internal anatomical structures.
    • A more comprehensive validation strategy, including internal markers, is necessary for assessing CT-MR image registration accuracy.
    • The proposed methodology provides a critical reference for evaluating registration algorithms in medical imaging.