Toward Robust Quantitative Photoacoustic Tomography via Learned Iterative Methods

IEEE Transactions on Computational Imaging, vol. 12, pp. 863–876

Abstract

Photoacoustic tomography (PAT) is a medical imaging modality that can provide high-resolution tissue images based on the optical absorption. Classical reconstruction methods for quantifying the absorption coefficients rely on sufficient prior information to overcome noisy and imperfect measurements. As these methods utilize computationally expensive forward models, the computation becomes slow, limiting their potential for time-critical applications. As an alternative approach, deep learning-based reconstruction methods have been established for faster and more accurate reconstructions. However, most of these methods rely on having a large amount of training data, which is not the case in practice. In this work, we adopt the model-based learned iterative approach for the use in Quantitative PAT (QPAT), in which additional information from the model is iteratively provided to the updating networks, allowing better generalizability with scarce training data. We compare the performance of different learned updates based on gradient descent, Gauss-Newton, and Quasi-Newton methods. The learning tasks are formulated as greedy, requiring iterate-wise optimality, as well as end-to-end, where all networks are trained jointly. The implemented methods are tested with ideal simulated data as well as against a digital twin dataset that emulates scarce training data and high modeling error.

Authors 3

1. Janek Gröhl Aachen Institute for Experimental Molecular Imaging

   RWTH Aachen University · Universitätsklinikum Aachen

   Affiliation as printed

   Institute for Experimental Molecular Imaging, RWTH Aachen University Clinic, Aachen, Germany

2. Felix F. Lucka

   Centrum Wiskunde & Informatica

   Affiliation as printed

   Centrum Wiskunde & Informatica, Amsterdam, The Netherlands

3. Andreas Hauptmann

   University of Oulu

   Affiliation as printed

   Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland