Ms. Panpan Chen Profile

Panpan Chen

at Univ. of Illinois

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Proceedings Article | 12 March 2024 Poster + Paper

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 Anastasio

Proceedings Volume 12842, 128420U (2024) https://doi.org/10.1117/12.3008778

KEYWORDS: Image restoration, Photoacoustic tomography, Tunable filters, Data modeling, Education and training, Data acquisition, Signal filtering, Linear filtering, Image filtering, 3D modeling

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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.

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