Machine-learning enhanced photoacoustic computed tomography in a limited view configuration

Deng, Handi; Wang, Xuanhao; Cai, Chuangjian; Luo, Jianwen; Ma, Cheng

doi:10.1117/12.2539148

Cited by 9 publications

(11 citation statements)

References 12 publications

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“…In comparison to other approaches, U-Net-based networks generally performed better than other architectures, e.g., compared to a simple three-layer CNN, 98 VGG, 101 and compared to applying U-Net directly to the measurement data g, 104 especially with respect to robustness. It is interesting that Antholzer et al 99 compare their results to a classic l 1 -regularization approach for compressed sensing and report that when the system matrix is randomly sampled, and hence undersampling artifacts change as well, the classical variational approach clearly outperforms the network-based postprocessing approach.…”

Section: Postprocessingmentioning

confidence: 94%

“…This observation was confirmed and clearly demonstrated in the study by Guan et al, 100 who proposed a dense U-Net to ameliorate this negative effect. Other extensions have been proposed too: using a leaky ReLU nonlinearity 101 or using the first iterate of a model-based iterative approach (Sec. 3.1.4) instead of a backprojection-type reconstruction.…”

Section: Postprocessingmentioning

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: Postprocessingmentioning

confidence: 94%

Section: Postprocessingmentioning

confidence: 99%

Deep learning in photoacoustic tomography: current approaches and future directions

2020

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“…They utilized the PA signal acquired by a ring array to generate the data set. 92 The PA images reconstructed from a full ring were used as the ground truth, and images reconstructed from a partial ring (1/4 ring, 90-deg coverage) were used as the images with limited-view artifacts. (A similar approach was demonstrated by Davoudi et al.…”

Section: Applications Of DL In Paimentioning

confidence: 99%

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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“…A core strength of such approaches is that experimental PA data can be utilized for training, by artificially undersampling the available channels and training the algorithm to predict the reconstructions from (1) full data, (2) sparse data, or (3) limited-view data. [91][92][93][94][95][96] Reflection artifact can be introduced by the presence of acoustic reflectors in the medium (for example air). Allman et al 97 showed that deep learning can be used to distinguish between artifacts and true signals and Shan et al 98 demonstrated that the technology is also capable of removing such artifacts from the images.…”

Section: Artifact Removalmentioning

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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Machine-learning enhanced photoacoustic computed tomography in a limited view configuration

Cited by 9 publications

References 12 publications

Deep learning in photoacoustic tomography: current approaches and future directions

Deep learning in photoacoustic tomography: current approaches and future directions

Deep learning in photoacoustic imaging: a review

Deep learning for biomedical photoacoustic imaging: A review

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