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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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Toward Closing the Loop in Image-to-Image Conversion in Radiotherapy: A Quality Control Tool to Predict Synthetic

Paolo Zaffino^1,2, Ciro Benito Raggio³, Adrian Thummerer^2,4

¹Department of Experimental and Clinical Medicine, Magna Graecia University, viale Europa, 88100 Catanzaro, Italy.

Journal of Imaging

|December 27, 2024

Summary

A new deep learning framework accurately predicts synthetic CT image quality without ground truth, ensuring reliable radiotherapy simulations. This method provides slice-by-slice accuracy maps, aiding clinical decisions in head and neck cancer treatment.

Keywords:

MR-only adaptive radiotherapy conversion prediction deep learning synthetic CT

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiotherapy Physics

Background:

Synthetic Computed Tomography (CT) images from Magnetic Resonance (MR) or Cone Beam CT (CBCT) are comparable to real CT for radiotherapy simulation.
Assessing synthetic CT quality without ground truth has been a significant challenge.

Purpose of the Study:

To develop a Deep Learning (DL) framework for predicting synthetic CT accuracy (Mean Absolute Error - MAE) without ground truth.
To provide clinicians with a volumetric map indicating predicted MAE for each slice.

Main Methods:

A cascading multi-model Deep Learning architecture was employed for MAE prediction.
The framework was trained and validated on patient cohorts using MR and CBCT imaging modalities.
The algorithm outputs a slice-by-slice volumetric map of predicted MAE.

Main Results:

The DL framework achieved accurate Hounsfield Unit (HU) prediction, with median absolute deviations of 4 HU for CBCT-based and 6 HU for MR-based synthetic CTs.
Predicted discrepancies were found to be clinically insignificant for decision-making.
The workflow demonstrated no systematic error in MAE prediction.

Conclusions:

This work presents a feasible proof of concept for evaluating synthetic CT quality in clinical practice.
The developed framework enables patient-specific quality assessment for synthetic CT images.
This approach facilitates the reliable use of synthetic CT in radiotherapy simulation and planning.