Learned backprojection for sparse and limited view photoacoustic tomography

Schwab, Johannes; Antholzer, Stephan; Haltmeier, Markus

doi:10.1117/12.2508438

Cited by 10 publications

(9 citation statements)

References 25 publications

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“…Similar approaches were reported by Schwab et al. 155 and Guan et al. 111 Since these works deal with situations beyond merely sparse sampling, they will be discussed in Sec.…”

Section: Applications Of DL In Paimentioning

confidence: 62%

“…also proposed a similar network to learn the weights in UBP to improve the PAT image quality under limited-view and sparse-sampling conditions. 155 The network only had two layers. The first layer received raw data as input and carried out temporal filtering without any trainable weights, and the second layer performed back projection with adjustable weights.…”

Section: Applications Of DL In Paimentioning

confidence: 99%

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Deep learning in photoacoustic imaging: a review

et al. 2021

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. Significance: Photoacoustic (PA) imaging can provide structural, functional, and molecular information for preclinical and clinical studies. For PA imaging (PAI), non-ideal signal detection deteriorates image quality, and quantitative PAI (QPAI) remains challenging due to the unknown light fluence spectra in deep tissue. In recent years, deep learning (DL) has shown outstanding performance when implemented in PAI, with applications in image reconstruction, quantification, and understanding. Aim: We provide (i) a comprehensive overview of the DL techniques that have been applied in PAI, (ii) references for designing DL models for various PAI tasks, and (iii) a summary of the future challenges and opportunities. Approach: Papers published before November 2020 in the area of applying DL in PAI were reviewed. We categorized them into three types: image understanding, reconstruction of the initial pressure distribution, and QPAI. Results: When applied in PAI, DL can effectively process images, improve reconstruction quality, fuse information, and assist quantitative analysis. Conclusion: DL has become a powerful tool in PAI. With the development of DL theory and technology, it will continue to boost the performance and facilitate the clinical translation of PAI.

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“…Similar approaches were reported by Schwab et al. 155 and Guan et al. 111 Since these works deal with situations beyond merely sparse sampling, they will be discussed in Sec.…”

Section: Applications Of DL In Paimentioning

confidence: 62%

Section: Applications Of DL In Paimentioning

confidence: 99%

Deep learning in photoacoustic imaging: a review

et al. 2021

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“…where P NðAÞ denotes the orthogonal projection to the null space. Schwab et al 109,110 combined postprocessing by a U-Net with a learning-based filter in the backprojection step [κ in Eq. 17] to improve initial reconstructions from limited-view measurements.…”

Section: Extensions Of the Postprocessing Approachmentioning

confidence: 99%

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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“…The central idea is to leverage the flexibility of deep learning to enhance already existing modelbased reconstruction algorithms, 69,70 by introducing learnable components. To this end, Schwab et al 71 proposed an extension of the weighted universal back-projection algorithm. The core idea is to add additional weights to the original algorithm, with the task of the learning algorithm then being to find optimal weights for the reconstruction formula.…”

Section: Deep Learning-enhanced Model-based Image Reconstructionmentioning

confidence: 99%

Deep learning for biomedical photoacoustic imaging: A review

Gröhl,

Schellenberg,

Dreher

et al. 2020

Preprint

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Photoacoustic Imaging (PAI) is a promising emerging imaging modality that enables spatially resolved imaging of optical tissue properties up to several centimeters deep in tissue, creating the potential for numerous exciting clinical applications. However, extraction of relevant tissue parameters from the raw data requires the solving of inverse image reconstruction problems, which have proven extremely difficult to solve. The application of deep learning methods has recently exploded in popularity, leading to impressive successes in the context of medical imaging and also finding first use in the field of PAI. Deep learning methods possess unique advantages that can facilitate the clinical translation of PAI, such as extremely fast computation times and the fact that they can be adapted to any given problem. In this review, we examine the current state of the art regarding deep learning in PAI and identify potential directions of research that will help to reach the goal of clinical applicability.

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Learned backprojection for sparse and limited view photoacoustic tomography

Cited by 10 publications

References 25 publications

Deep learning in photoacoustic imaging: a review

Deep learning in photoacoustic imaging: a review

Deep learning in photoacoustic tomography: current approaches and future directions

Deep learning for biomedical photoacoustic imaging: A review

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