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Expert variability as a benchmark for validating automatic CT-MRI registration in brain stereotactic radiosurgery.

Valeria Faccenda1, Denis Panizza1, Valentina Pinzi2

  • 1Medical Physics, Fondazione IRCCS San Gerardo Dei Tintori, Monza, Italy; School of Medicine and Surgery, University of Milan Bicocca, Milan, Italy.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)
|March 19, 2026
PubMed
Summary

Defining a probabilistic gold standard for CT-MRI registration is crucial for validating stereotactic radiosurgery (SRS) algorithms. An AI-driven contour-based method sets a new benchmark for accuracy and reliability in brain metastases treatment.

Keywords:
AI-driven automatic registration algorithmBrain MetastasesIntra- and Interobserver variabilityMagnetic Resonance ImagingRegistrationStereotactic Radiotherapy

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Area of Science:

  • Medical Imaging
  • Radiosurgery
  • Computational Anatomy

Background:

  • Accurate computed tomography (CT) and magnetic resonance imaging (MRI) registration is essential for stereotactic radiosurgery (SRS) of brain metastases (BM).
  • Existing manual registration methods exhibit variability, necessitating a reliable benchmark for algorithm validation.
  • A probabilistic gold standard (GS) and acceptance thresholds are needed to evaluate automatic CT-MRI registration algorithms.

Purpose of the Study:

  • To establish a probabilistic gold standard (GS) for rigid CT-MRI registration using multi-expert variability.
  • To define clinically meaningful acceptance thresholds for automatic registration algorithms.
  • To evaluate the performance of mutual-information (MI)-based registration methods, including a contour-based (CB) approach.

Main Methods:

  • Twenty CT-MRI pairs with 39 brain metastases (BM) were registered by six experts twice.
  • Variability was quantified using bootstrap resampling to estimate confidence intervals for key registration metrics.
  • A contour-based (CB) mutual-information (MI) registration algorithm, using manual or AI-generated contours, was evaluated against expert performance.

Main Results:

  • Expert CT-MRI registration showed low but significant variability (99% CI: 0.31° rotation, 0.34 mm translation).
  • The contour-based (CB) algorithm achieved performance within expert acceptance ranges for most metrics, irrespective of contour source (manual vs. AI).
  • Less-experienced operators' deviations were reduced when using CB-aligned datasets, highlighting the algorithm's utility.

Conclusions:

  • Quantifying multi-expert variability provides a probabilistic gold standard and relevant thresholds for CT-MRI registration validation.
  • The AI-driven CB algorithm demonstrates high reliability and reduces operator dependence in CT-MRI registration.
  • This validated benchmark and reliable algorithm can streamline workflows for brain metastases SRS.