3D-WDA-PMorph: Efficient 3D MRI/TRUS Prostate Registration using Transformer-CNN Network and Wavelet-3D-Depthwise-Attention.

Multimodal image registration is crucial in medical imaging, particularly for aligning Magnetic Resonance Imaging (MRI) and Transrectal Ultrasound (TRUS) data, which are widely used in prostate cancer diagnosis and treatment planning. However, this task presents significant challenges due to the inherent differences between these imaging modalities, including variations in resolution, contrast, and noise. Recently, conventional Convolutional Neural Network (CNN)-based registration methods, while effective at extracting local features, often struggle to capture global contextual information and fail to adapt to complex deformations in multimodal data. Conversely, Transformer-based methods excel at capturing long-range dependencies and hierarchical features but face difficulties in integrating fine-grained local details, which are essential for accurate spatial alignment. To address these limitations, we propose a novel 3D image registration framework that combines the strengths of both paradigms. Our method employs a Swin Transformer (ST)-CNN encoder-decoder architecture, with a key innovation focusing on enhancing the skip connection stages. Specifically, we introduce an innovative module named Wavelet-3D-Depthwise-Attention (WDA). The WDA module leverages an attention mechanism that integrates wavelet transforms for multi-scale spatial-frequency representation and 3D-Depthwise convolution to improve computational efficiency and modality fusion. Experimental evaluations on clinical MRI/TRUS datasets confirm that the proposed method achieves a median Dice score of 0.94 and a target registration error of 0.85, indicating an improvement in registration accuracy and robustness over existing state-of-the-art (SOTA) methods. The WDA-enhanced skip connections significantly empower the registration network to preserve critical anatomical details, making our method a promising advancement in prostate multimodal registration. Furthermore, the proposed framework shows strong potential for generalization to other image registration tasks.