Anatomy-based PET image reconstruction using nonlocal regularization

Van-Giang Nguyen; Soo-Jin Lee

doi:10.1117/12.911690

3 March 2012 Anatomy-based PET image reconstruction using nonlocal regularization

Van-Giang Nguyen, Soo-Jin Lee

Proceedings Volume 8313, Medical Imaging 2012: Physics of Medical Imaging; 83133T (2012) https://doi.org/10.1117/12.911690
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

We propose a new nonlocal regularization method for PET image reconstruction with the aid of high-resolution anatomical images. Unlike conventional reconstruction methods using prior anatomical information, our method using nonlocal regularization does not require additional processes to extract anatomical boundaries or segmented regions. The nonlocal regularization method applied to anatomy-based PET image reconstruction is expected to effectively reduce the error that often occurs due to signal mismatch between the PET image and the anatomical image. We also show that our method can be useful for enhancing the image resolution. To reconstruct the high-resolution image that represents the original underlying source distribution effectively sampled at a higher spatial sampling rate, we model the underlying PET image on a higher-resolution grid and perform our nonlocal regularization method with the aid of the side information obtained from high-resolution anatomical images. Our experimental results demonstrate that, compared to the conventional method based on local smoothing, our nonlocal regularization method enhances the resolution as well as the reconstruction accuracy even with the imperfect prior anatomical information or in the presence of signal mismatch between the PET image and the anatomical image.

Citation Download Citation

Van-Giang Nguyen and Soo-Jin Lee "Anatomy-based PET image reconstruction using nonlocal regularization", Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 83133T (3 March 2012); https://doi.org/10.1117/12.911690

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