Automatic cancer tissue detection using multispectral photoacoustic imaging

Jnawali, Kamal; Chinni, Bhargava; Dogra, Vikram S.; Rao, Navalgund

doi:10.1007/s11548-019-02101-1

Cited by 23 publications

(11 citation statements)

References 59 publications

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“…These results are encouraging to be used for various clinical applications requiring chromophore quantification. Indeed, the evaluation of chromophore concentrations is of major interest for various applications, like concentration of contrast agent in the body [ 5 ], calculation of the concentration of oxygenated and deoxygenated blood for oxygenation rate evaluation [ 6 , 7 ], or cancer tissue evaluation [ 8 ]. The endmembers extraction could be either automatic or manually selected by the user coupled with a fast inverse problem (FCLS).…”

Section: Discussionmentioning

confidence: 99%

“…These two properties, discrimination of tissues and concentration evaluation, are of major interest, depending on the application at hand. They can be exploited to determine different concentrations of the same chromophore (e.g., estimation of the concentration of a contrast agent in the body [ 5 ]), or to distinguish one particular chromophore from all of the other imaged ones without considering its dilution (e.g., determination of the level of vascularization and calculation of the concentration of oxygenated and deoxygenated blood, for oxygenation rate evaluation [ 6 , 7 ], cancer tissue evaluation [ 8 ], or imaging connectivity in brain [ 9 ]).…”

Section: Introductionmentioning

confidence: 99%

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In Vitro and In Vivo Multispectral Photoacoustic Imaging for the Evaluation of Chromophore Concentration

Dolet

Ammanouil

Pétrilli

et al. 2021

Sensors

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Multispectral photoacoustic imaging is a powerful noninvasive medical imaging technique that provides access to functional information. In this study, a set of methods is proposed and validated, with experimental multispectral photoacoustic images used to estimate the concentration of chromophores. The unmixing techniques used in this paper consist of two steps: (1) automatic extraction of the reference spectrum of each pure chromophore; and (2) abundance calculation of each pure chromophore from the estimated reference spectra. The compared strategies bring positivity and sum-to-one constraints, from the hyperspectral remote sensing field to multispectral photoacoustic, to evaluate chromophore concentration. Particularly, the study extracts the endmembers and compares the algorithms from the hyperspectral remote sensing domain and a dedicated algorithm for segmentation of multispectral photoacoustic data to this end. First, these strategies are tested with dilution and mixing of chromophores on colored 4% agar phantom data. Then, some preliminary in vivo experiments are performed. These consist of estimations of the oxygen saturation rate (sO2) in mouse tumors. This article proposes then a proof-of-concept of the interest to bring hyperspectral remote sensing algorithms to multispectral photoacoustic imaging for the estimation of chromophore concentration.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In Vitro and In Vivo Multispectral Photoacoustic Imaging for the Evaluation of Chromophore Concentration

Dolet

Ammanouil

Pétrilli

et al. 2021

Sensors

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“…The advances in deep learning (DL) have been progressively incorporated with PAI. Over the past few years, researchers have primarily focused on applying DL in photoacoustic computed tomography (PACT) for artifact removal, target identification, and sparse sampling [ [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] ]. Recently, DL has been used to reduce the laser pulse energy [ 27 ] and improve the undersampling in PAM [ 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Deep image prior for undersampling high-speed photoacoustic microscopy

DiSpirito

et al. 2021

Photoacoustics

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Photoacoustic microscopy (PAM) is an emerging imaging method combining light and sound. However, limited by the laser’s repetition rate, state-of-the-art high-speed PAM technology often sacrifices spatial sampling density ( i.e. , undersampling) for increased imaging speed over a large field-of-view. Deep learning (DL) methods have recently been used to improve sparsely sampled PAM images; however, these methods often require time-consuming pre-training and large training dataset with ground truth. Here, we propose the use of deep image prior (DIP) to improve the image quality of undersampled PAM images. Unlike other DL approaches, DIP requires neither pre-training nor fully-sampled ground truth, enabling its flexible and fast implementation on various imaging targets. Our results have demonstrated substantial improvement in PAM images with as few as 1.4 % of the fully sampled pixels on high-speed PAM. Our approach outperforms interpolation, is competitive with pre-trained supervised DL method, and is readily translated to other high-speed, undersampling imaging modalities.

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“…Deep learning (DL) has been increasingly applied in enhancing PACT performance, including localizing wavefronts, 17 improving LED-based PAT, 18 and assisting cancer detection. 19–21 DL has also been extensively explored for PACT artifact removal. For example, several groups have reported the use of UNet and other deep convolutional neural networks (CNNs) to address the limited-view and sparse-sampling issues as postprocessing correction, 22–24 direct reconstruction, 25 and model-based learning.…”

Section: Introductionmentioning

confidence: 99%

A generative adversarial network for artifact removal in photoacoustic computed tomography with a linear-array transducer

Humayun

et al. 2020

Exp Biol Med (Maywood)

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With balanced spatial resolution, penetration depth, and imaging speed, photoacoustic computed tomography (PACT) is promising for clinical translation such as in breast cancer screening, functional brain imaging, and surgical guidance. Typically using a linear ultrasound (US) transducer array, PACT has great flexibility for hand-held applications. However, the linear US transducer array has a limited detection angle range and frequency bandwidth, resulting in limited-view and limited-bandwidth artifacts in the reconstructed PACT images. These artifacts significantly reduce the imaging quality. To address these issues, existing solutions often have to pay the price of system complexity, cost, and/or imaging speed. Here, we propose a deep-learning-based method that explores the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to reduce the limited-view and limited-bandwidth artifacts in PACT. Compared with existing reconstruction and convolutional neural network approach, our model has shown improvement in imaging quality and resolution. Our results on simulation, phantom, and in vivo data have collectively demonstrated the feasibility of applying WGAN-GP to improve PACT’s image quality without any modification to the current imaging set-up. Impact statement This study has the following main impacts. It offers a promising solution for removing limited-view and limited-bandwidth artifact in PACT using a linear-array transducer and conventional image reconstruction, which have long hindered its clinical translation. Our solution shows unprecedented artifact removal ability for in vivo image, which may enable important applications such as imaging tumor angiogenesis and hypoxia. The study reports, for the first time, the use of an advanced deep-learning model based on stabilized generative adversarial network. Our results have demonstrated its superiority over other state-of-the-art deep-learning methods.

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Automatic cancer tissue detection using multispectral photoacoustic imaging

Cited by 23 publications

References 59 publications

In Vitro and In Vivo Multispectral Photoacoustic Imaging for the Evaluation of Chromophore Concentration

In Vitro and In Vivo Multispectral Photoacoustic Imaging for the Evaluation of Chromophore Concentration

Deep image prior for undersampling high-speed photoacoustic microscopy

A generative adversarial network for artifact removal in photoacoustic computed tomography with a linear-array transducer

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