Simultaneous reconstruction of the initial pressure and sound speed in photoacoustic tomography using a deep-learning approach

Shan, Hongming; Wiedeman, Christopher; Wang, Ge; Yang, Yang

doi:10.1117/12.2529984

Cited by 16 publications

(13 citation statements)

References 67 publications

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“…Note that 800 random 64 × 64 Shepp‐Logan phantoms were first generated using methods similar to those in Shan et al 23 . In short, these images were composed of an outer shell containing four to six ellipses with varying electron density and attenuation values selected to mimic real biological tissues.…”

Section: Methodsmentioning

confidence: 99%

Feasibility analysis on simultaneous electron density and attenuation coefficient reconstruction

2021

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Creating a viable reconstruction method for Compton scatter tomography remains challenging. Accounting for scatter attenuation when the underlying attenuation map is not known is particularly challenging, and current mathematical approaches to this vary widely. This work explores a novel approach to joint scatter and attenuation image reconstruction, which leverages the underlying structural similarity between the two images and incorporates a deep learning model in an alternating iterative reconstruction scheme. Methods: A single-view computed tomography (CT) imaging procedure for recording Compton scatter is first described. A joint reconstruction model, which iterates between algebraically reconstructing scatter images and estimating the attenuation via deep learning, is then proposed. This model is tested on both a generated dataset of 2D phantom images designed to mimic human tissues as well as a realistically simulated dataset based on real CT images. Results: Testing results yield convergence of the model and decent reconstruction quality to distinguish crucial features such as tumors and lesions, demonstrating the potential principled utilities of this configuration and deep learning approach. The model achieved a structural similarity index measure of at least 0.82 for scatter and 0.88 for attenuation reconstructions with the realistically simulated dataset. Conclusion:The iterative, deep learning approach outlined in this work shows potential for future efficient medical imaging procedures, reconstructing images with limited scatter information.

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Section: Methodsmentioning

confidence: 99%

Feasibility analysis on simultaneous electron density and attenuation coefficient reconstruction

2021

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“…Shan et al. 113 developed a CNN-based model to reconstruct the SoS and initial pressure jointly using the framework of the model-based method. The updates of initial pressure and SoS were computed based on three types of input: (1) the initial pressure distribution, (2) the negative gradient of the L2 distance between the reconstructed image and the ground truth, and (3) the current SoS distribution.…”

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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“…In Ref. 135, the basic learned iterative reconstruction approach has been applied with an extension to simultaneously reconstruct sound speed as well, which constitutes a learned version of Ref. 136.…”

Section: Learned Iterative Reconstructionsmentioning

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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Simultaneous reconstruction of the initial pressure and sound speed in photoacoustic tomography using a deep-learning approach

Cited by 16 publications

References 67 publications

Feasibility analysis on simultaneous electron density and attenuation coefficient reconstruction

Feasibility analysis on simultaneous electron density and attenuation coefficient reconstruction

Deep learning in photoacoustic imaging: a review

Deep learning in photoacoustic tomography: current approaches and future directions

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