Image reconstruction in a passive element enriched photoacoustic tomography setup

In an inhomogeneously illuminated photoacoustic image, important information like vascular geometry is not readily available when only the initial pressure is reconstructed. To obtain the desired information, algorithms for image segmentation are often applied as a post-processing step. In this work, we propose to jointly acquire the photoacoustic reconstruction and segmentation, by modifying a recently developed partially learned algorithm based on a convolutional neural network. We investigate the stability of the algorithm against changes in initial pressures and photoacoustic system settings. These insights are used to develop an algorithm that is robust to input and system settings. Our approach can easily be applied to other imaging modalities and can be modified to perform other high-level tasks different from segmentation. The method is validated on challenging synthetic and experimental photoacoustic tomography data in limited angle and limited view scenarios. It is computationally less expensive than classical iterative methods and enables higher quality reconstructions and segmentations than state-of-the-art learned and non-learned methods.

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“…• Direct FBP method as described in [7]. • Iterative TV-regularised method (6) using PDHG (Algorithm 1) as described in [15].…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

A Partially-Learned Algorithm for Joint Photo-acoustic Reconstruction and Segmentation

Boink

Manohar

Brüne

2020

IEEE Trans. Med. Imaging

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“…u cal (x) ≈ δ(x − x p ). After finding an analytic expression for p cal (x, t) in terms of ∂I(t) ∂t and h IR (t) [39], we obtain…”

Section: Forward Modelmentioning

confidence: 99%

A framework for directional and higher-order reconstruction in photoacoustic tomography

Boink¹,

Lagerwerf²,

Steenbergen³

et al. 2018

Phys. Med. Biol.

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Photoacoustic tomography is a hybrid imaging technique that combines high optical tissue contrast with high ultrasound resolution. Direct reconstruction methods such as filtered back-projection, time reversal and least squares suffer from curved line artefacts and blurring, especially in the case of limited angles or strong noise. In recent years, there has been great interest in regularised iterative methods. These methods employ prior knowledge of the image to provide higher quality reconstructions. However, easy comparisons between regularisers and their properties are limited, since many tomography implementations heavily rely on the specific regulariser chosen. To overcome this bottleneck, we present a modular reconstruction framework for photoacoustic tomography, which enables easy comparisons between regularisers with different properties, e.g. nonlinear, higher-order or directional. We solve the underlying minimisation problem with an efficient first-order primal-dual algorithm. Convergence rates are optimised by choosing an operator-dependent preconditioning strategy. A variety of reconstruction methods are tested on challenging 2D synthetic and experimental data sets. They outperform direct reconstruction approaches for strong noise levels and limited angle measurements, offering immediate benefits in terms of acquisition time and quality. This work provides a basic platform for the investigation of future advanced regularisation methods in photoacoustic tomography.

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“…Therefore three photoacoustic measurements are necessary. 4 In the first measurement, see Fig. 2(a), the speed of sound in the water and the center of rotation is determined by using a special calibration object.…”

Section: The Per -Pat Setupmentioning

confidence: 99%

“…The first subsection of section two describes the experimental setup as it is used in a recent study. 4 Hereafter we analyze in the second subsection the standard description of PA in order to derive a "signature" waveform generated by the passive element with the purpose of optimal detection by a mathed filter. The final subsection describes the developed numerical simulation model used in the reconstruction simulation experiments.…”

Section: Introductionmentioning

confidence: 99%

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On the role of passive elements in photoacoustic reconstruction

et al. 2011

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In the reconstruction process of photo acoustic experiments, it was observed that adding a passive element to the experimental setup, improves the quality of the reconstruction of the object. This contribution analyzes this effect in some detail. We consider a cylindrical configuration. We start from an artificial and theoretically constructed optical absorption distribution that radiates sound waves when interrogated by the optical pulse. We analyze in the experimental setup the addition of the passive element to this example. The reported investigation is a part of a larger study on the existence, uniqueness and stability of photo acoustic inverse source reconstructions.

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Image reconstruction in a passive element enriched photoacoustic tomography setup

Cited by 3 publications

References 62 publications

A Partially-Learned Algorithm for Joint Photo-acoustic Reconstruction and Segmentation

A Partially-Learned Algorithm for Joint Photo-acoustic Reconstruction and Segmentation

A framework for directional and higher-order reconstruction in photoacoustic tomography

On the role of passive elements in photoacoustic reconstruction

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