On Learning the Invisible in Photoacoustic Tomography with Flat Directionally Sensitive Detector

TL;DR

This paper introduces a novel method for photoacoustic tomography with flat sensors that separates visible and invisible wavefronts using wavefront analysis, and employs deep learning to reconstruct both, improving image quality under limited data conditions.

Contribution

It adapts wedge restricted Curvelet decomposition for wavefront separation and combines it with neural networks for efficient reconstruction of invisible components.

Findings

Effective separation of visible and invisible wavefronts.

Deep neural networks successfully reconstruct invisible wavefronts.

Improved image reconstruction quality under limited data conditions.

Abstract

In photoacoustic tomography (PAT) with flat sensor, we routinely encounter two types of limited data. The first is due to using a finite sensor and is especially perceptible if the region of interest is large relative to the sensor or located farther away from the sensor. In this paper, we focus on the second type caused by a varying sensitivity of the sensor to the incoming wavefront direction which can be modelled as binary i.e. by a cone of sensitivity. Such visibility conditions result, in the Fourier domain, in a restriction of both the image and the data to a bow-tie, akin to the one corresponding to the range of the forward operator. The visible wavefrontsets in image and data domains, are related by the wavefront direction mapping. We adapt the wedge restricted Curvelet decomposition, we previously proposed for the representation of the full PAT data, to separate the visible and invisible wavefronts in the image. We optimally combine fast approximate operators with tailored deep neural network architectures into efficient learned reconstruction methods which perform reconstruction of the visible coefficients and the invisible coefficients are learned from a training set of similar data.