Adaptive smoothing algorithms for MBIR in CT applications

Jingyan Xu; Frederic Noo

doi:10.1117/12.2534928

28 May 2019 Adaptive smoothing algorithms for MBIR in CT applications

Jingyan Xu, Frederic Noo

Proceedings Volume 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine; 110720C (2019) https://doi.org/10.1117/12.2534928
Event: Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, Philadelphia, United States

Abstract

Many model based image reconstruction (MBIR) methods for x-ray CT are formulated as convex minimization problems. If the objective function is nonsmooth. primal-dual algorithms are applicable with the drawback that there is an increased memory cost due to the dual variables. Some algorithms recently developed for large-scale nonsmooth convex programs use adaptive smoothing techniques and are of the primal type. That is, they achieve convergence without introducing the dual variables, hence without the increased memory. We discuss one such algorithm with an O(1/k) convergence rate, where k is the iteration number. We then present an extension of it to handle strong convex objective functions. This new algorithm has the optimal convergence rate of O(1/k ²) for its problem class. Our preliminary numerical studies demonstrate competitive performance with respect to an alternative method.

Citation Download Citation

Jingyan Xu and Frederic Noo "Adaptive smoothing algorithms for MBIR in CT applications", Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 110720C (28 May 2019); https://doi.org/10.1117/12.2534928

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