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Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis.

Lotte Nijskens¹, Cornelis A T van den Berg¹, Joost J C Verhoeff²

¹Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Science, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584CX, The Netherlands; Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584CX, The Netherlands.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)

|July 20, 2023

Summary

Domain randomization (DR) enhances deep learning models for synthetic CT generation from MRI, improving robustness and reducing retraining needs. This approach boosts generalization for unseen MRI sequences in radiotherapy applications.

Keywords:

Artificial intelligence Computed tomography Domain shift Generalisation Machine learning Magnetic resonance imaging Medical imaging Radiotherapy Regression

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Area of Science:

Medical Imaging
Radiotherapy Physics
Artificial Intelligence in Medicine

Background:

Synthetic computed tomography (sCT) is crucial for MRI-based radiotherapy, with deep learning (DL) models showing promise for its generation.
Challenges arise from varying MRI protocols across centers, leading to poor DL model generalization and low-quality sCT.
This necessitates methods to improve the robustness and generalizability of DL models for sCT generation.

Purpose of the Study:

To investigate the efficacy of domain randomization (DR) in enhancing the generalization capabilities of DL models for brain sCT generation.
To assess the impact of DR on the accuracy and robustness of sCT generated from unseen MRI sequences.

Main Methods:

A generative adversarial network (GAN) was trained using CT and various MRI sequences (T1, T2, FLAIR) from 95 patients undergoing radiotherapy.
A baseline model was trained without DR, and its performance on the unseen FLAIR sequence was compared against a DR-enhanced model.
Image similarity metrics and the accuracy of sCT-based radiotherapy dose plans were evaluated against the ground truth CT.

Main Results:

The baseline model exhibited the highest mean absolute error (MAE) on the unseen FLAIR sequence (106 ± 20.7 HU).
The DR model showed improved performance on FLAIR (MAE = 99.0 ± 14.9 HU) compared to the baseline, though still inferior to a model trained with FLAIR data (MAE = 72.6 ± 10.1 HU).
DR also led to an improved gamma-pass rate compared to the baseline, indicating better dose plan accuracy.

Conclusions:

Domain randomization effectively improves image similarity and dose accuracy for sCT generation on unseen MRI sequences.
DR enhances the robustness of DL models, mitigating the need for retraining when encountering new or varied MRI data.
This approach supports wider clinical adoption of MRI-based radiotherapy by improving the reliability of synthetic CT generation across different imaging protocols.