Diffusion posterior sampling for nonlinear CT reconstruction

Shudong Li; Matthew Tivnan; J. Webster Stayman

doi:10.1117/12.3007693

1 April 2024 Diffusion posterior sampling for nonlinear CT reconstruction

Shudong Li, Matthew Tivnan, J. Webster Stayman

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 1292519 (2024) https://doi.org/10.1117/12.3007693
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Diffusion models have been demonstrated as powerful deep learning tools for image generation in CT reconstruction and restoration. Recently, diffusion posterior sampling, where a score-based diffusion prior is combined with a likelihood model, has been used to produce high quality CT images given low-quality measurements. This technique is attractive since it permits a one-time, unsupervised training of a CT prior; which can then be incorporated with an arbitrary data model. However, current methods only rely on a linear model of x-ray CT physics to reconstruct or restore images. While it is common to linearize the transmission tomography reconstruction problem, this is an approximation to the true and inherently nonlinear forward model. We propose a new method that solves the inverse problem of nonlinear CT image reconstruction via diffusion posterior sampling. We implement a traditional unconditional diffusion model by training a prior score function estimator, and apply Bayes rule to combine this prior with a measurement likelihood score function derived from the nonlinear physical model to arrive at a posterior score function that can be used to sample the reverse-time diffusion process. This plug-and-play method allows incorporation of a diffusion-based prior with generalized nonlinear CT image reconstruction into multiple CT system designs with different forward models, without the need for any additional training. We demonstrate the technique in both fully sampled low dose data and sparse-view geometries using a single unsupervised training of the prior.

Conference Presentation

Citation Download Citation

Shudong Li, Matthew Tivnan, and J. Webster Stayman "Diffusion posterior sampling for nonlinear CT reconstruction", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 1292519 (1 April 2024); https://doi.org/10.1117/12.3007693

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available