CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 12925 > Article
Paper
1 April 2024 Multi-source semi-stationary CT for brain imaging: development and assessment of a prototype system and image formation algorithms
T. McSkimming, A. Lopez-Montes, A. Skeats, C. Delnooz, B. Gonzales, E. Perilli, K. Reynolds, J. H. Siewerdsen, W. Zbijewski, A. Sisniega
Author Affiliations +
Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 129251B (2024) https://doi.org/10.1117/12.3006970
Event: SPIE Medical Imaging, 2024, San Diego, California, United States
Abstract
Multi-source array (MXA) Computed Tomography systems pose challenges related to sampling and x-ray scatter. We present a semi-stationary head CT system and image formation pipeline including adaptive scatter estimation and image reconstruction based on learned diffusion models. The CT was evaluated on a robotic bench system including a miniaturized carbon-nanotube x-ray source and a curved-panel detector. Scatter correction was achieved with an Adaptive Deep Scatter Estimation (ADSE) method combining geometry-invariant projection-based scatter estimation with geometry-adaptive registration and scaling. Image reconstruction followed a Diffusion Posterior Sampling method (DPS-Recon) combining an unconditional diffusion model with measured data consistency. Image quality was assessed using anthropomorphic phantoms for a semi-stationary protocol involving a 21-source MXA rotated to three positions. ADSE resulted in 118% mean increase in feature contrast accuracy, 1.75 to 13-fold improvement in CNR for variable contrast features (-337HU to 885HU), and 3.56-fold improvement in CNR for variable size features (2mm to 12mm, 110HU) compared to uncorrected reconstructions. Non-uniformity reduced 50% for the three slices. DPS-Recon reduced limited sampling artifacts and improved visualization of soft-tissue structures, particularly in less densely sampled and bony anatomy locations, and further reduced non-uniformity by 20% in the superior brain location. We present first experimental results from a semi-stationary, multi-source CT utilizing CNT x-ray sources and curved-panel detector coupled to an imaging chain that addressed the main challenges inherent to the architecture. Metrics of CT number accuracy, image uniformity, and soft-tissue visualization showed promising performance for visualization of stroke radiological markers with the proposed approach.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
T. McSkimming, A. Lopez-Montes, A. Skeats, C. Delnooz, B. Gonzales, E. Perilli, K. Reynolds, J. H. Siewerdsen, W. Zbijewski, and A. Sisniega "Multi-source semi-stationary CT for brain imaging: development and assessment of a prototype system and image formation algorithms", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 129251B (1 April 2024); https://doi.org/10.1117/12.3006970
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
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
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 7 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
X-ray computed tomography
Brain
Algorithm development
3D image reconstruction
CT reconstruction
Deep convolutional neural networks
Deep learning
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top