If unaligned multimodal medical images can be simultaneously aligned and fused using a single-stage approach within a unified processing framework, it will not only achieve mutual promotion of dual tasks but also help reduce the complexity of the model. However, the design of this model faces the challenge of incompatible requirements for feature fusion and alignment; specifically, feature alignment requires consistency among corresponding features, whereas feature fusion requires the features to be complementary to each other. To address this challenge, this paper proposes an unaligned medical image fusion method called Bidirectional Stepwise Feature Alignment and Fusion (BSFA-F) strategy. To reduce the negative impact of modality differences on cross-modal feature matching, we incorporate the Modal Discrepancy-Free Feature Representation (MDF-FR) method into BSFA-F. MDF-FR utilizes a Modality Feature Representation Head (MFRH) to integrate the global information of the input image. By injecting the information contained in MFRH of the current image into other modality images, it effectively reduces the impact of modality differences on feature alignment while preserving the complementary information carried by different images. In terms of feature alignment, BSFA-F employs a bidirectional stepwise alignment deformation field prediction strategy based on the path independence of vector displacement between two points. This strategy solves the problem of large spans and inaccurate deformation field prediction in single-step alignment. Finally, Multi-Modal Feature Fusion block achieves the fusion of aligned features. The experimental results across multiple datasets demonstrate the effectiveness of our method. The source code is available at https://github.com/slrl123/BSAFusion.

翻译：若能在统一处理框架内通过单阶段方法同时实现未对齐多模态医学图像的配准与融合，不仅能实现双任务的相互促进，还有助于降低模型复杂度。然而，该模型的设计面临特征融合与配准需求不兼容的挑战：具体而言，特征配准要求对应特征具有一致性，而特征融合则要求特征间具有互补性。为应对这一挑战，本文提出一种未对齐医学图像融合方法——双向渐进特征对齐与融合（BSFA-F）策略。为降低模态差异对跨模态特征匹配的负面影响，我们将模态无差异特征表示（MDF-FR）方法融入BSFA-F。MDF-FR通过模态特征表示头（MFRH）整合输入图像的全局信息，通过将当前图像的MFRH信息注入其他模态图像，在保留不同图像所携带互补信息的同时，有效降低了模态差异对特征对齐的影响。在特征对齐方面，BSFA-F基于两点间向量位移的路径无关性，采用双向渐进对齐形变场预测策略，解决了单步对齐中形变场预测跨度大且不准确的问题。最后，通过多模态特征融合模块实现对齐特征的融合。多数据集实验结果表明了本方法的有效性。源代码公开于：https://github.com/slrl123/BSAFusion。