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)=106$\pm$20.7 HU (mean$\pm\sigma$). Performance on FLAIR significantly
improved for the DR model with MAE=99.0$\pm$14.9 HU, but still inferior to the
performance of the Baseline+FLAIR model (MAE=72.6$\pm$10.1 HU). Similarly, an
improvement in $\gamma$-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.

研究领域：通过深度学习及领域随机化技术生成高质量头部合成计算机断层扫描图像，提高医疗影像辅助治疗的精度。

探索基于深度学习的脑 MRI 到 CT 合成中的对比度泛化

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

Glioblastoma Multiforme is a high grade, very aggressive, brain tumor, with
patients having a poor prognosis. Lower grade gliomas are less aggressive, but
they can evolve into higher grade tumors over time. Patient management and
treatment can vary considerably with tumor grade, ranging from tumor resection
followed by a combined radio- and chemotherapy to a "wait and see" approach.
Hence, tumor grading is important for adequate treatment planning and
monitoring. The gold standard for tumor grading relies on histopathological
diagnosis of biopsy specimens. However, this procedure is invasive, time
consuming, and prone to sampling error. Given these disadvantages, automatic
tumor grading from widely used MRI protocols would be clinically important, as
a way to expedite treatment planning and assessment of tumor evolution. In this
paper, we propose to use Convolutional Neural Networks for predicting tumor
grade directly from imaging data. In this way, we overcome the need for expert
annotations of regions of interest. We evaluate two prediction approaches: from
the whole brain, and from an automatically defined tumor region. Finally, we
employ interpretability methodologies as a quality assurance stage to check if
the method is using image regions indicative of tumor grade for classification.

本文提出使用卷积神经网络（Convolutional Neural Networks）直接从影像数据预测脑胶质母细胞瘤分级的方法，从而避免了需要专家标注感兴趣区域的需求，并且通过可解释性方法检查分类是否使用了与肿瘤分级相关的影像区域作为质量保证阶段。