1 research outputs found
Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis
Background: Synthetic computed tomography (sCT) has been proposed and
increasingly clinically adopted to enable magnetic resonance imaging
(MRI)-based radiotherapy. Deep learning (DL) has recently demonstrated the
ability to generate accurate sCT from fixed MRI acquisitions. However, MRI
protocols may change over time or differ between centres resulting in
low-quality sCT due to poor model generalisation. Purpose: investigating domain
randomisation (DR) to increase the generalisation of a DL model for brain sCT
generation. Methods: CT and corresponding T1-weighted MRI with/without
contrast, T2-weighted, and FLAIR MRI from 95 patients undergoing RT were
collected, considering FLAIR the unseen sequence where to investigate
generalisation. A ``Baseline'' generative adversarial network was trained
with/without the FLAIR sequence to test how a model performs without DR. Image
similarity and accuracy of sCT-based dose plans were assessed against CT to
select the best-performing DR approach against the Baseline. Results: The
Baseline model had the poorest performance on FLAIR, with mean absolute error
(MAE)=10620.7 HU (mean). Performance on FLAIR significantly
improved for the DR model with MAE=99.014.9 HU, but still inferior to the
performance of the Baseline+FLAIR model (MAE=72.610.1 HU). Similarly, an
improvement in -pass rate was obtained for DR vs Baseline. Conclusions:
DR improved image similarity and dose accuracy on the unseen sequence compared
to training only on acquired MRI. DR makes the model more robust, reducing the
need for re-training when applying a model on sequences unseen and unavailable
for retraining.Comment: Preprint submitted to Physica Medica on 2023-02-16 for review. Also
published in Zenodo at https://doi.org/10.5281/zenodo.774264