Robustness of a partially learned photoacoustic reconstruction algorithm

Boink, Yoeri E.; Brüne, Christoph; Manohar, Srirang

doi:10.1117/12.2507446

Cited by 3 publications

(2 citation statements)

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“…For reconstructions in PAT, the work by Boink et al [137][138][139] has demonstrated the robustness of these learned iterative schemes to a number of in silico phantoms as well as in an experimental study. The authors consider an extension to the learned gradient schemes introduced above called learned primal dual 18 (LPD) based on the successful primal-dual hybrid gradient method 140 (also known as the Chambolle-Pock algorithm).…”

Section: Learned Primal Dual In 2dmentioning

confidence: 99%

“…5(b). In their work, [137][138][139] the authors examined the robustness of LPD with respect to changes in the target, including the contrast, background, structural changes, and noise level. They found that if the network is trained only on the basic training data, it generalizes fairly well with respect to noise (1 dB degradation in PSNR) and structural changes (3 dB), but is most sensitive to changes in background (7 dB) and contrast (11 dB).…”

Section: Learned Primal Dual In 2dmentioning

confidence: 99%

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Deep learning in photoacoustic tomography: current approaches and future directions

2020

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Biomedical photoacoustic tomography, which can provide high-resolution 3D soft tissue images based on optical absorption, has advanced to the stage at which translation from the laboratory to clinical settings is becoming possible. The need for rapid image formation and the practical restrictions on data acquisition that arise from the constraints of a clinical workflow are presenting new image reconstruction challenges. There are many classical approaches to image reconstruction, but ameliorating the effects of incomplete or imperfect data through the incorporation of accurate priors is challenging and leads to slow algorithms. Recently, the application of deep learning (DL), or deep neural networks, to this problem has received a great deal of attention. We review the literature on learned image reconstruction, summarizing the current trends and explain how these approaches fit within, and to some extent have arisen from, a framework that encompasses classical reconstruction methods. In particular, it shows how these techniques can be understood from a Bayesian perspective, providing useful insights. We also provide a concise tutorial demonstration of three prototypical approaches to learned image reconstruction. The code and data sets for these demonstrations are available to researchers. It is anticipated that it is in in vivo applications-where data may be sparse, fast imaging critical, and priors difficult to construct by hand-that DL will have the most impact. With this in mind, we conclude with some indications of possible future research directions. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Learned Primal Dual In 2dmentioning

confidence: 99%

Section: Learned Primal Dual In 2dmentioning

confidence: 99%

Deep learning in photoacoustic tomography: current approaches and future directions

2020

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Multi-Scale Learned Iterative Reconstruction

Hauptmann

Adler

Arridge

et al. 2020

IEEE Trans. Comput. Imaging

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Model-based learned iterative reconstruction methods have recently been shown to outperform classical reconstruction methods. Applicability of these methods to large scale inverse problems is however limited by the available memory for training and extensive training times. As a possible solution to these restrictions we propose a multi-scale learned iterative reconstruction algorithm that computes iterates on discretisations of increasing resolution. This procedure does not only reduce memory requirements, it also considerably speeds up reconstruction and training times, but most importantly is scalable to large scale inverse problems, like those that arise in 3D tomographic imaging. Feasibility of the proposed method to speed up training and computation times in comparison to established learned reconstruction methods in 2D is demonstrated for low dose computed tomography (CT), for which we utilise the data base of abdominal CT scans provided for the 2016 AAPM lowdose CT grand challenge. * Equal contribution.A. Hauptmann is with the Research

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Least-squares reverse time migration via deep learning-based updating operators

Torres

Sacchi

2022

GEOPHYSICS

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Two common issues of least-squares reverse time migration (LSRTM) consist of the many iterations required to produce substantial subsurface imaging improvements and the difficulty of choosing adequate regularization strategies with optimal hyperparameters. We investigate how supervised learning can mitigate these shortcomings by solving the LSRTM problem through an iterative deep learning framework inspired by the projected gradient descent algorithm. In particular, we develop an image-to-image approach interlacing the gradient steps at each iteration with blocks of residual convolutional neural networks (CNN) that capture the prior information in the training phase. By including the least-squares data-misfit gradient into the learning process, we force the solution to comply with the observed seismic data, while the CNN projections implicitly account for the regularization effects that lead to high-resolution reflectivity updates. After training with nine hundred randomly generated instances, our network ensemble can estimate accurate reflectivity distributions in only a few iterations. To demonstrate the effectiveness and generalization properties of the method, we consider three synthetic cases: a folded and faulted model, the Marmousi model, and the Sigsbee2a model. We empirically show that it is possible to obtain an improved reflectivity model for out-of-distribution instances by using the learned reconstructions as warm-starts for the conjugate gradient algorithm, bridging the gap between learned and conventional LSRTM schemes. Finally, we apply the proposed network with transfer learning on a 2D towed-streamer Gulf of Mexico field data set, producing high-resolution images comparable to traditional LSRTM but drastically reducing the required number of iterations.

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Robustness of a partially learned photoacoustic reconstruction algorithm

Cited by 3 publications

References 20 publications

Deep learning in photoacoustic tomography: current approaches and future directions

Deep learning in photoacoustic tomography: current approaches and future directions

Multi-Scale Learned Iterative Reconstruction

Least-squares reverse time migration via deep learning-based updating operators

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