Clinical decision making from magnetic resonance imaging (MRI) combines complementary information from multiple MRI sequences (defined as 'modalities'). MRI image registration aims to geometrically 'pair' diagnoses from different modalities, time points and slices. Both intra- and inter-modality MRI registration are essential components in clinical MRI settings. Further, an MRI image processing pipeline that can address both afine and non-rigid registration is critical, as both types of deformations may be occuring in real MRI data scenarios. Unlike image classification, explainability is not commonly addressed in image registration deep learning (DL) methods, as it is challenging to interpet model-data behaviours against transformation fields. To properly address this, we incorporate Grad-CAM-based explainability frameworks in each major component of our unsupervised multi-modal and multi-organ image registration DL methodology. We previously demonstrated that we were able to reach superior performance (against the current standard Syn method). In this work, we show that our DL model becomes fully explainable, setting the framework to generalise our approach on further medical imaging data.

翻译：磁共振成像（MRI）的临床决策融合了来自多个MRI序列（定义为“模态”）的互补信息。MRI图像配准旨在从几何上“配对”不同模态、时间点和切片的诊断结果。模态内与模态间MRI配准均是临床MRI设置中的关键组成部分。此外，能够同时处理仿射和非刚性配准的MRI图像处理流程至关重要，因为实际MRI数据场景中可能同时存在这两种形变类型。与图像分类不同，图像配准深度学习方法通常不涉及可解释性，这是因为针对变换场解释模型与数据的行为极具挑战性。为妥善解决此问题，我们在无监督多模态多器官图像配准深度学习方法的主要组件中引入了基于Grad-CAM的可解释性框架。此前我们已证明该方法能达到优于当前标准Syn方法的性能。本研究进一步展示，我们的深度学习模型具备完全可解释性，从而为将该方法泛化至更多医学影像数据奠定了基础框架。