Solving Inverse Problems in Medical Imaging with Score-Based Generative Models. (arXiv:2111.08005v2 [eess.IV] UPDATED)

Web: http://arxiv.org/abs/2111.08005

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 …

arxiv imaging medical medical imaging models