Temporal Evolution of Knee Osteoarthritis: A Diffusion-based Morphing Model for X-ray Medical Image Synthesis

TL;DR

This paper presents a novel diffusion-based morphing model that synthesizes intermediate knee X-ray images to simulate osteoarthritis progression, aiding data augmentation and disease monitoring.

Contribution

It introduces a diffusion-based morphing model that generates continuous X-ray sequences between healthy and severe KOA stages, a novel approach for temporal medical image synthesis.

Findings

Achieves high-quality temporal frame synthesis performance.

Effectively augments data for KOA classification models.

Simulates disease progression along a geodesic path.

Abstract

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.