Learning a semi-analytic reconstruction method for photoacoustic computed tomography with hemispherical measurement geometries

Panpan Chen; Seonyeong Park; Refik Mert Cam; Hsuan-Kai Huang; Umberto Villa; Mark A. Anastasio

doi:10.1117/12.3008778

12 March 2024 Learning a semi-analytic reconstruction method for photoacoustic computed tomography with hemispherical measurement geometries

Panpan Chen, Seonyeong Park, Refik Mert Cam, Hsuan-Kai Huang, Umberto Villa, Mark A. Anastasio

Author Affiliations +

Proceedings Volume 12842, Photons Plus Ultrasound: Imaging and Sensing 2024; 128420U (2024) https://doi.org/10.1117/12.3008778
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

Photoacoustic computed tomography (PACT) is being actively developed for breast cancer imaging. In 3D PACT imagers for breast imaging, a hemispherical measurement geometry that encloses the breast has been employed. Such measurement data are referred to as “half-scan” data. Existing closed-form reconstruction methods assume a closed measurement aperture; however, the direct application of these methods to half-scan data results in inaccurate images with artifacts. Previous studies have demonstrated that half-scan data are “complete” in the sense that they contain sufficient information for accurate and stable reconstruction of an object contained within a hemispherical measurement aperture. However, direct closed-form methods for use with half-scan data have not been reported. Although optimization-based iterative image reconstruction methods are applicable, they are computationally intensive. In this work, for the first time, a semi-analytic image reconstruction method of filtered backprojection (FBP) form was proposed for use with half-scan PACT data. To accomplish this, the unknown data filtering operation is learned in a data-driven way by use of a linear U-Net neural network. To investigate the method, stochastic 3D numerical breast phantoms (NBPs) were used for model training and testing. As a result of the completeness of the half-scan data, we demonstrate that the learned FBP method can be widely applied, even when the to-be-reconstructed object differs considerably from those that were used to learn the data filtering. This is a key feature of the method that will enable it to have an important practical impact on PACT.

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

Citation Download Citation

Panpan Chen, Seonyeong Park, Refik Mert Cam, Hsuan-Kai Huang, Umberto Villa, and Mark A. Anastasio "Learning a semi-analytic reconstruction method for photoacoustic computed tomography with hemispherical measurement geometries", Proc. SPIE 12842, Photons Plus Ultrasound: Imaging and Sensing 2024, 128420U (12 March 2024); https://doi.org/10.1117/12.3008778

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