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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Modeling dose uncertainty in cone-beam computed tomography: Predictive approach for deep learning-based synthetic

Cédric Hémon¹, Lucía Cubero¹, Valentin Boussot¹

¹Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France.

Physics and Imaging in Radiation Oncology

|February 13, 2025

Summary

This study introduces an uncertainty estimator for synthetic CT (sCT) generated from cone-beam CT (CBCT) in radiotherapy. The method accurately predicts sCT quality and estimates dose uncertainty, improving treatment accuracy.

Keywords:

CBCT-to-CT generation Dose uncertainty Head and Neck Uncertainty estimation

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Area of Science:

Medical Physics
Radiotherapy
Medical Imaging

Background:

Cone-beam computed tomography (CBCT) is crucial for image-guided radiotherapy (RT) but exhibits variable CT numbers compared to standard CT.
Synthetic CT (sCT) generation from CBCT is necessary for accurate dose calculations but remains challenging due to inherent uncertainties.

Purpose of the Study:

To develop and validate a voxel-wise uncertainty estimation method for CBCT-to-sCT synthesis.
To correlate uncertainty maps with sCT-CT errors and quantify dose uncertainties.

Main Methods:

Developed and validated an uncertainty estimation method using 85 head and neck (H&N) patients treated with photon RT.
Included three external patients to assess robustness on out-of-distribution images.
Generated 'plausible' sCTs to explore error scenarios and quantify dose uncertainties.

Main Results:

Uncertainty maps showed a strong correlation (Pearson's r = 0.65–0.72) with absolute sCT-CT error maps.
Dose uncertainty was quantified using dose-volume histograms (DVHs).
Reference CT DVHs were within the uncertainty intervals derived from sCT for most patients.

Conclusions:

The proposed method effectively predicts uncertainty maps, aiding in sCT quality assessment.
A novel approach for estimating dose uncertainty is provided by defining confidence intervals around CT DVHs.