SGM-based sparsity reconstruction under non-standard geometry of robotic CT

Xuan Zhou; Qiong Xu; Yuedong Liu; Cunfeng Wei; Long Wei

doi:10.1117/12.3006170

1 April 2024 SGM-based sparsity reconstruction under non-standard geometry of robotic CT

Xuan Zhou, Qiong Xu, Yuedong Liu, Cunfeng Wei, Long Wei

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 1292524 (2024) https://doi.org/10.1117/12.3006170
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Robotic CT is a novel imaging platform built on two manipulators with great flexibility and convenience. But it suffers from limited mechanical motion accuracy, which brings artifacts into images. Acquiring true geometry parameters is critical for accurate image reconstruction. While it’s impractical to monitor all geometry positions in practice. Down-sampling the projection number and using sparsity reconstruction offers a feasible way of solving this problem. Score-based generative model (SGM) is a powerful generative model able to produce directional samples guided by prior information. Through combining prior data and generated data, images quality can be significantly improved. In this work, we trained a score-based generative model using images from two real CT scan datasets. In sampling of score-based net, prior sparsity projection was added through cone-beam projection and image reconstruction. Full projection under non-standard geometry was simulated by adding deviation into standard circular geometry. We compared performance of several algorithms on sparsity and full data, under true and ideal geometry. Image was evaluated by typical indexes and visible details. Images of SART under ideal geometry showed severe artifacts, with strip artifacts in soft tissue. Compared with images under ideal geometry, SGM-based sparsity reconstruction showed visual fuzzier image but with higher index, which improved by 59.0% and 41.1% for PSNR and SSIM. Compared with sparsity reconstruction under true geometry using SART, SGM-based method showed clearer image and higher indexes, with 11.4% and 24.7% improvement of PSNR and SSIM. SGM-based sparsity reconstruction showed great potential in sparsity reconstruction under non-standard geometry.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xuan Zhou, Qiong Xu, Yuedong Liu, Cunfeng Wei, and Long Wei "SGM-based sparsity reconstruction under non-standard geometry of robotic CT", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 1292524 (1 April 2024); https://doi.org/10.1117/12.3006170

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