Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and Synthesis

In medical imaging, the diffusion models have shown great potential in synthetic image generation tasks. However, these models often struggle with the interpretable connections between the generated and existing images and could create illusions. To address these challenges, our research proposes a novel diffusion-based generative model based on deformation diffusion and recovery. This model, named Deformation-Recovery Diffusion Model (DRDM), diverges from traditional score/intensity and latent feature-based approaches, emphasizing morphological changes through deformation fields rather than direct image synthesis. This is achieved by introducing a topological-preserving deformation field generation method, which randomly samples and integrates a set of multi-scale Deformation Vector Fields (DVF). DRDM is trained to learn to recover unreasonable deformation components, thereby restoring each randomly deformed image to a realistic distribution. These innovations facilitate the generation of diverse and anatomically plausible deformations, enhancing data augmentation and synthesis for further analysis in downstream tasks, such as few-shot learning and image registration. Experimental results in cardiac MRI and pulmonary CT show DRDM is capable of creating diverse, large (over 10% image size deformation scale), and high-quality (negative ratio of folding rate is lower than 1%) deformation fields. The further experimental results in downstream tasks, 2D image segmentation and 3D image registration, indicate significant improvements resulting from DRDM, showcasing the potential of our model to advance image manipulation and synthesis in medical imaging and beyond. Our implementation will be available at https://github.com/jianqingzheng/def_diff_rec.

Multimodal Deformable Image Registration for Long-COVID Analysis Based on Progressive Alignment and Multi-perspective Loss

Long COVID is characterized by persistent symptoms, particularly pulmonary impairment, which necessitates advanced imaging for accurate diagnosis. Hyperpolarised Xenon-129 MRI (XeMRI) offers a promising avenue by visualising lung ventilation, perfusion, as well as gas transfer. Integrating functional data from XeMRI with structural data from Computed Tomography (CT) is crucial for comprehensive analysis and effective treatment strategies in long COVID, requiring precise data alignment from those complementary imaging modalities. To this end, CT-MRI registration is an essential intermediate step, given the significant challenges posed by the direct alignment of CT and Xe-MRI. Therefore, we proposed an end-to-end multimodal deformable image registration method that achieves superior performance for aligning long-COVID lung CT and proton density MRI (pMRI) data. Moreover, our method incorporates a novel Multi-perspective Loss (MPL) function, enhancing state-of-the-art deep learning methods for monomodal registration by making them adaptable for multimodal tasks. The registration results achieve a Dice coefficient score of 0.913, indicating a substantial improvement over the state-of-the-art multimodal image registration techniques. Since the XeMRI and pMRI images are acquired in the same sessions and can be roughly aligned, our results facilitate subsequent registration between XeMRI and CT, thereby potentially enhancing clinical decision-making for long COVID management.