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Deformable Image Registration Uncertainty-Encompassing Dose Accumulation for Adaptive Radiation Therapy.

Sebastian Meyer¹, Yu-Chi Hu¹, Andreas Rimner²

¹Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.

International Journal of Radiation Oncology, Biology, Physics

|April 16, 2025

Summary

This summary is machine-generated.

This study introduces a novel framework to integrate deformable image registration (DIR) uncertainty into radiation therapy dose accumulation. Accounting for DIR uncertainty is crucial for accurate adaptive radiation therapy and personalized patient treatments.

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Area of Science:

Medical Physics
Radiation Oncology
Image Processing

Background:

Deformable image registration (DIR) is critical for adaptive radiation therapy but is inherently ill-posed.
DIR quality is algorithm and user-dependent, impacting its clinical application.
Integrating DIR uncertainty into dose accumulation is essential for accurate treatment planning.

Purpose of the Study:

To propose an automated framework for incorporating deformable image registration uncertainty into dose accumulation.
To enhance the reliability and precision of dose calculations in adaptive radiation therapy.
To improve clinical decision-making and personalize radiation therapy treatments.

Main Methods:

A hyperparameter perturbation approach was used to generate an ensemble of deformation vector fields.
Principal component analysis on homologous points created voxel-specific DIR uncertainty confidence ellipsoids.
Dose limits were estimated via interpolation within these ellipsoids during dose mapping.

Main Results:

The framework is clinically feasible, with an average computational time of 30 minutes.
Significant DIR uncertainty was observed, particularly for the gross tumor volume and lungs.
Considering DIR uncertainty revealed cases exceeding institutional dose-volume histogram limits, emphasizing its importance.

Conclusions:

The proposed framework facilitates clinical implementation of dose accumulation by accounting for DIR uncertainty.
This approach can improve clinical decision-making in adaptive radiation therapy.
It holds the potential to enable more personalized radiation therapy treatments.