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Related Experiment Video

Updated: Nov 27, 2025

Author Spotlight: Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT) for NonInvasive Whole-Brain Imaging of Ischemic Stroke

Author Spotlight: Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT) for NonInvasive Whole-Brain Imaging of Ischemic Stroke

Published on: June 2, 2023

Stochastic Proximal Gradient Algorithms for Multi-Source Quantitative Photoacoustic Tomography.

Simon Rabanser¹, Lukas Neumann², Markus Haltmeier¹

¹Department of Mathematics, University of Innsbruck, Technikerstraße 13, A-6020 Innsbruck, Austria.

Entropy (Basel, Switzerland)

|December 3, 2020

Summary

This summary is machine-generated.

This study introduces efficient stochastic methods for multi-source quantitative photoacoustic tomography (QPAT) image reconstruction. A novel multilinear formulation of QPAT is presented, improving computational efficiency and accuracy for tissue parameter imaging.

Keywords:

Dykstra algorithm image reconstruction limited data limited view multilinear inverse problem photoacoustic tomography radiative transfer equation stochastic gradient method

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Area of Science:

Medical Imaging
Computational Physics
Biomedical Engineering

Background:

Accurate and efficient image reconstruction is crucial for quantitative photoacoustic tomography (QPAT).
Multi-source QPAT presents computational challenges for existing algorithms.
Modeling light transport with the radiative transfer equation (RTE) is essential for accurate QPAT.

Purpose of the Study:

To develop efficient image reconstruction algorithms for multi-source QPAT.
To introduce a novel multilinear (MULL) formulation for QPAT inverse problems.
To enhance the accuracy and computational performance of QPAT.

Main Methods:

Development of stochastic proximal gradient methods for multi-source QPAT.
Formulation of QPAT as a multilinear (MULL) inverse problem, avoiding direct RTE solution.
Joint reconstruction of tissue parameters from acoustical data across multiple sources.

Main Results:

Stochastic proximal gradient methods demonstrate improved efficiency over standard methods for multi-source QPAT.
The novel MULL formulation of QPAT offers a new, efficient approach to image reconstruction.
Numerical results validate the performance of both proposed stochastic and MULL-based methods.

Conclusions:

Stochastic proximal gradient algorithms are effectively applied to QPAT.
The new MULL formulation represents a significant contribution to QPAT, enhancing efficiency and potentially accuracy.
The study advances QPAT by providing more efficient and accurate image reconstruction techniques.