  Knee Osteoarthritis (KOA) is a common musculoskeletal disorder that
significantly affects the mobility of older adults. In the medical domain,
images containing temporal data are frequently utilized to study temporal
dynamics and statistically monitor disease progression. While deep
learning-based generative models for natural images have been widely
researched, there are comparatively few methods available for synthesizing
temporal knee X-rays. In this work, we introduce a novel deep-learning model
designed to synthesize intermediate X-ray images between a specific patient's
healthy knee and severe KOA stages. During the testing phase, based on a
healthy knee X-ray, the proposed model can produce a continuous and effective
sequence of KOA X-ray images with varying degrees of severity. Specifically, we
introduce a Diffusion-based Morphing Model by modifying the Denoising Diffusion
Probabilistic Model. Our approach integrates diffusion and morphing modules,
enabling the model to capture spatial morphing details between source and
target knee X-ray images and synthesize intermediate frames along a geodesic
path. A hybrid loss consisting of diffusion loss, morphing loss, and
supervision loss was employed. We demonstrate that our proposed approach
achieves the highest temporal frame synthesis performance, effectively
augmenting data for classification models and simulating the progression of
KOA.
