  Photoacoustic tomography (PAT) offers optical contrast, whereas magnetic
resonance imaging (MRI) excels in imaging soft tissue and organ anatomy. The
fusion of PAT with MRI holds promising application prospects due to their
complementary advantages. Existing image fusion have made considerable progress
in pre-registered images, yet spatial deformations are difficult to avoid in
medical imaging scenarios. More importantly, current algorithms focus on visual
quality and statistical metrics, thus overlooking the requirements of
high-level tasks. To address these challenges, we proposes a unsupervised
fusion model, termed PAMRFuse+, which integrates image generation and
registration. Specifically, a cross-modal style transfer network is introduced
to simplify cross-modal registration to single-modal registration.
Subsequently, a multi-level registration network is employed to predict
displacement vector fields. Furthermore, a dual-branch feature decomposition
fusion network is proposed to address the challenges of cross-modal feature
modeling and decomposition by integrating modality-specific and modality-shared
features. PAMRFuse+ achieves satisfactory results in registering and fusing
unaligned PAT-MRI datasets. Moreover, for the first time, we evaluate the
performance of medical image fusion with contour segmentation and multi-organ
instance segmentation. Extensive experimental demonstrations reveal the
advantages of PAMRFuse+ in improving the performance of medical image analysis
tasks.
