64-line-sensor array: fast imaging system for photoacoustic tomography

Gratt, Sibylle; Nuster, Robert; Wurzinger, Gerhild; Bugl, Markus; Paltauf, Guenther

doi:10.1117/12.2041242

Cited by 7 publications

(8 citation statements)

References 12 publications

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“…As the fabrication of an array of parallel line detectors is demanding, most experiments using integrating line detectors have been carried out using a single line detector, scanned on circular paths using scanning stages [51,30]. Recently, systems using arrays of 64 parallel line detectors have been demonstrated [29,2]. The most costly building blocks in such devices are the analog to digital converters (ADC).…”

Section: Compressed Sensing Patmentioning

confidence: 99%

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A Novel Compressed Sensing Scheme for Photoacoustic Tomography

Sandbichler¹,

Krahmer²,

Berer³

et al. 2015

SIAM J. Appl. Math.

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Speeding up the data acquisition is one of the central aims to advance tomographic imaging. On the one hand, this reduces motion artifacts due to undesired movements, and on the other hand this decreases the examination time for the patient. In this article, we propose a new scheme for speeding up the data collection process in photoacoustic tomography. Our proposal is based on compressed sensing and reduces acquisition time and system costs while maintaining image quality. As measurement data we use random combinations of pressure values that we use to recover a complete set of pressure data prior to the actual image reconstruction. We obtain theoretical recovery guarantees for our compressed sensing scheme and support the theory by reconstruction results on simulated data as well as on experimental data.

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Section: Compressed Sensing Patmentioning

confidence: 99%

“…This, again, leads to an increased overall measurement time. For example, using an excitation laser with a repetition rate of 10 Hz and two times multiplexing, a measurement time of 0.2 s for a projection image has been reported in [29]. Without multiplexing, this measurement time would reduce to the half.…”

Section: Compressed Sensing Patmentioning

confidence: 99%

A Novel Compressed Sensing Scheme for Photoacoustic Tomography

Sandbichler¹,

Krahmer²,

Berer³

et al. 2015

SIAM J. Appl. Math.

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show abstract

“…As the fabrication of an array of detectors is demanding, experiments using integrating line detectors are often carried out using a single line detector, scanned on circular paths using scanning stages [28,57], which is very time consuming. Recently, systems using arrays of 64 parallel line detectors have been demonstrated [6,26]. To keep production costs low and to allow fast imaging the number M will typically be kept much smaller than advised by Shannon's sampling theory and one deals with highly under-sampled data.…”

Section: Discretization and Sparse Samplingmentioning

confidence: 99%

Deep learning for photoacoustic tomography from sparse data

Antholzer

Haltmeier

Schwab

2018

Inverse Problems in Science and Engineering

235

173

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The development of fast and accurate image reconstruction algorithms is a central aspect of computed tomography. In this paper, we investigate this issue for the sparse data problem in photoacoustic tomography (PAT). We develop a direct and highly efficient reconstruction algorithm based on deep learning. In our approach, image reconstruction is performed with a deep convolutional neural network (CNN), whose weights are adjusted prior to the actual image reconstruction based on a set of training data. The proposed reconstruction approach can be interpreted as a network that uses the PAT filtered backprojection algorithm for the first layer, followed by the U-net architecture for the remaining layers. Actual image reconstruction with deep learning consists in one evaluation of the trained CNN, which does not require time-consuming solution of the forward and adjoint problems. At the same time, our numerical results demonstrate that the proposed deep learning approach reconstructs images with a quality comparable to state of the art iterative approaches for PAT from sparse data.

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“…Since we need a separate sensor for each spatial measurement sample, the number of spatial samples s ½ s Å ¾ Ê is limited. Recently, systems with 64 line detectors have been demonstrated to work [23,24]. To keep costs low, the number of detectors will still be limited in the future and smaller than required for artifact-free imaging advised by Shannon's sampling theory [25].…”

Section: Photoacoustic Tomographymentioning

confidence: 99%

Photoacoustic image reconstruction via deep learning

Antholzer,

Schwab,

Nuster

et al. 2019

Preprint

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Applying standard algorithms to sparse data problems in photoacoustic tomography (PAT) yields low-quality images containing severe under-sampling artifacts. To some extent, these artifacts can be reduced by iterative image reconstruction algorithms which allow to include prior knowledge such as smoothness, total variation (TV) or sparsity constraints. These algorithms tend to be time consuming as the forward and adjoint problems have to be solved repeatedly. Further, iterative algorithms have additional drawbacks. For example, the reconstruction quality strongly depends on a-priori model assumptions about the objects to be recovered, which are often not strictly satisfied in practical applications. To overcome these issues, in this paper, we develop direct and efficient reconstruction algorithms based on deep learning. As opposed to iterative algorithms, we apply a convolutional neural network, whose parameters are trained before the reconstruction process based on a set of training data. For actual image reconstruction, a single evaluation of the trained network yields the desired result. Our presented numerical results (using two different network architectures) demonstrate that the proposed deep learning approach reconstructs images with a quality comparable to state of the art iterative reconstruction methods.

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64-line-sensor array: fast imaging system for photoacoustic tomography

Cited by 7 publications

References 12 publications

A Novel Compressed Sensing Scheme for Photoacoustic Tomography

A Novel Compressed Sensing Scheme for Photoacoustic Tomography

Deep learning for photoacoustic tomography from sparse data

Photoacoustic image reconstruction via deep learning

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