Toward Closing the Loop in Image-to-Image Conversion in Radiotherapy: A Quality Control Tool to Predict Synthetic Computed Tomography Hounsfield Unit Accuracy

In recent years, synthetic Computed Tomography (CT) images generated from Magnetic
Resonance (MR) or Cone Beam Computed Tomography (CBCT) acquisitions have been shown to be
comparable to real CT images in terms of dose computation for radiotherapy simulation. However,
until now, there has been no independent strategy to assess the quality of each synthetic image in the
absence of ground truth. In this work, we propose a Deep Learning (DL)-based framework to predict
the accuracy of synthetic CT in terms of Mean Absolute Error (MAE) without the need for a ground
truth (GT). The proposed algorithm generates a volumetric map as an output, informing clinicians of
the predicted MAE slice-by-slice. A cascading multi-model architecture was used to deal with the
complexity of the MAE prediction task. The workflow was trained and tested on two cohorts of head
and neck cancer patients with different imaging modalities: 27 MR scans and 33 CBCT. The algorithm
evaluation revealed an accurate HU prediction (a median absolute prediction deviation equal to 4
HU for CBCT-based synthetic CTs and 6 HU for MR-based synthetic CTs), with discrepancies that do
... mehr
not affect the clinical decisions made on the basis of the proposed estimation. The workflow exhibited
no systematic error in MAE prediction. This work represents a proof of concept about the feasibility
of synthetic CT evaluation in daily clinical practice, and it paves the way for future patient-specific
quality assessment strategies.