Maximum A Posteriori material decomposition for spectral photon-counting CT: application to human blood iron level estimation

Adam P. Harrison; Stefan Ulzheimer; Amir Pourmorteza

doi:10.1117/12.2582207

15 February 2021 Maximum A Posteriori material decomposition for spectral photon-counting CT: application to human blood iron level estimation

Adam P. Harrison, Stefan Ulzheimer, Amir Pourmorteza

Author Affiliations +

Proceedings Volume 11595, Medical Imaging 2021: Physics of Medical Imaging; 115954H (2021) https://doi.org/10.1117/12.2582207
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Purpose: To develop a dose-efficient image-based material decomposition technique for spectral photon-counting computed tomography (PCCT) data and investigate estimating human blood iron concentration from contrast- enhanced scans. Methods: We adapt a maximum a posteriori (MAP) approach to decomposition, formulating spectral material decomposition as maximizing a posterior likelihood that incorporates both the standard linear generative model of decomposition and a smoothness prior. Our approach employs numeric tensor algebra and software, which can naturally handle the high-dimensional nature of decomposition. To ensure accurate priors, we compute smoothness weights using the image created from all detected photons. Our MAP approach only requires a large and sparse linear system to solve, with one tuning parameter. Results: We test the algorithm on 4-energy threshold spectral PCCT scans of a human subject pre- and post- contrast. MAP estimates remain stable while reducing noise standard deviation by 80.1% and 75.4% for iron and iodine, respectively, which again suggests over 4x decrease in radiation. Aortic iron concentration measured from MAP had small bias post-contrast, but with a noise reduction of roughly 80%. This small bias (-5%) in iron content may be attributed to the blood volume increase after contrast injection. Conclusion: The dose-efficient MAP decomposition method shows improved precision over the standard approach in estimating blood-iron concentration. Future work will include additional human studies to determine the optimal trade-off between precision and algorithmic bias.

Conference Presentation

Citation Download Citation

Adam P. Harrison, Stefan Ulzheimer, and Amir Pourmorteza "Maximum A Posteriori material decomposition for spectral photon-counting CT: application to human blood iron level estimation", Proc. SPIE 11595, Medical Imaging 2021: Physics of Medical Imaging, 115954H (15 February 2021); https://doi.org/10.1117/12.2582207

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