Solving Inverse Problems in Medical Imaging with Score-Based Generative Models

Understand

Reconstructing medical images from partial measurements is an important inverse problem in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI).

• Existing solutions based on machine learning typically train a model to directly map measurements to medical images, leveraging a training dataset of paired images and measurements.
• These measurements are typically synthesized from images using a fixed physical model of the measurement process, which hinders the generalization capability of models to unknown measurement processes.
• To address this issue, we propose a fully unsupervised technique for inverse problem solving, leveraging the recently introduced score-based generative models.