Y-Net: Hybrid deep learning image reconstruction for photoacoustic tomography in vivo

Lan, Hengrong; Jiang, Daohuai; Yang, Changchun; Gao, Feng; Gao, Fei

doi:10.1016/j.pacs.2020.100197

Cited by 92 publications

(57 citation statements)

References 42 publications

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“…According to the published results, we find that: (1) U-Net is almost a panacea that has been applied in most of the tasks; (2) the application of DL in the traditional reconstruction frameworks usually generates better results than other approaches; and (3) the better results are often accompanied by increased network complexity, such as more branches, 36 , 126 more connections, 86 , 87 and more complex layers or blocks. 107 , 149 …”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

“… 34 In PAI, many published works were based on this architecture or its variants. 35 – 37 The basic U-Net architecture is shown in Fig. 1 .…”

Section: Deep Learningmentioning

confidence: 99%

Deep learning in photoacoustic imaging: a review

et al. 2021

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“…To try to mitigate these, a few groups have investigated hybrid approaches. For instance, to overcome the missing model dependence in the fully learned approach, the work by Lan et al [145][146][147] proposed augmenting the end-to-end approaches 127,128 by additionally feeding the network a reconstructed image, either directly into the network at a suitable location 147 or with a separate processing branch. 145,146…”

Section: Hybrid Approachesmentioning

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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“…Many studies have reported reconstruction algorithms that yield high-quality PA images [30][31][32][33][34][35][36][37]. Here, high-quality images could be acquired from noisy signals through an effective signal de-noising technique followed by a simple reconstruction method.…”

Section: Introductionmentioning

confidence: 99%

Dictionary learning technique enhances signal in LED-based photoacoustic imaging

Farnia

Najafzadeh

Hariri

et al. 2020

Biomed. Opt. Express

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There has been growing interest in low-cost light sources such as light-emitting diodes (LEDs) as an excitation source in photoacoustic imaging. However, LED-based photoacoustic imaging is limited by low signal due to low energy per pulse-the signal is easily buried in noise leading to low quality images. Here, we describe a signal de-noising approach for LED-based photoacoustic signals based on dictionary learning with an alternating direction method of multipliers. This signal enhancement method is then followed by a simple reconstruction approach delay and sum. This approach leads to sparse representation of the main components of the signal. The main improvements of this approach are a 38% higher contrast ratio and a 43% higher axial resolution versus the averaging method but with only 4% of the frames and consequently 49.5% less computational time. This makes it an appropriate option for real-time LED-based photoacoustic imaging.

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Y-Net: Hybrid deep learning image reconstruction for photoacoustic tomography in vivo

Cited by 92 publications

References 42 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

Dictionary learning technique enhances signal in LED-based photoacoustic imaging

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