On Learning the Invisible in Photoacoustic Tomography with Flat Directionally Sensitive Detector
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 20 of its 70 references that are in the map. First posted on arXiv 21 Apr 2022.
Builds on
20 of 70 references.
References · 20
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8 more
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.