Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography

Abstract

Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed up. In this paper, we present a deep neural network that is specifically designed to provide high resolution 3-D images from restricted photoacoustic measurements. The network is designed to represent an iterative scheme and incorporates gradient information of the data fit to compensate for limited view artifacts. Due to the high complexity of the photoacoustic forward operator, we separate training and computation of the gradient information. A suitable prior for the desired image structures is learned as part of the training. The resulting network is trained and tested on a set of segmented vessels from lung computed tomography scans and then applied to in-vivo photoacoustic measurement data.

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Cited by papers we hold 3

• Fast iterative reconstruction for photoacoustic tomography using learned physical model: Theoretical validation · 2023
  Not checked
• Deep learning for biomedical photoacoustic imaging: A review · 2021
  No notice
• All-optical forward-viewing photoacoustic probe for high-resolution 3D endoscopy · 2018
  No notice

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