Deep learning protocol for improved photoacoustic brain imaging

Manwar, Rayyan; Li, Xin; Mahmoodkalayeh, Sadreddin; Asano, Eishi; Zhu, Dongxiao; Avanaki, Kamran

doi:10.1002/jbio.202000212

Cited by 65 publications

(52 citation statements)

References 10 publications

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“…Manwar et al. 122 also reported a similar idea and used U-Net to enhance the SNR of deep structures in brain tissue. They got B-scan images from an ex vivo sheep brain by a linear array at 20 and 100 mJ as the input and the label, respectively.…”

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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Section: Applications Of DL In Paimentioning

confidence: 99%

Deep learning in photoacoustic imaging: a review

et al. 2021

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“…Manwar et al successfully used a U-Net to improve the SNR of in vivo deep tissue regions when using low laser energy. 60 DL-based resolution enhancement has been explored in PACT. For a circular detection geometry, Rajendran and Pramanik have applied an advanced FD U-Net, named TARES, to improve tangential resolution of reconstructed PACT images far away from the scanning center (or close to the transducer surface).…”

Section: For Pre-processing Channel Datamentioning

confidence: 99%

Sounding out the hidden data: A concise review of deep learning in photoacoustic imaging

DiSpirito

Pramanik

et al. 2021

Exp Biol Med (Maywood)

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The rapidly evolving field of photoacoustic tomography utilizes endogenous chromophores to extract both functional and structural information from deep within tissues. It is this power to perform precise quantitative measurements in vivo—with endogenous or exogenous contrast —that makes photoacoustic tomography highly promising for clinical translation in functional brain imaging, early cancer detection, real-time surgical guidance, and the visualization of dynamic drug responses. Considering photoacoustic tomography has benefited from numerous engineering innovations, it is of no surprise that many of photoacoustic tomography’s current cutting-edge developments incorporate advances from the equally novel field of artificial intelligence. More specifically, alongside the growth and prevalence of graphical processing unit capabilities within recent years has emerged an offshoot of artificial intelligence known as deep learning. Rooted in the solid foundation of signal processing, deep learning typically utilizes a method of optimization known as gradient descent to minimize a loss function and update model parameters. There are already a number of innovative efforts in photoacoustic tomography utilizing deep learning techniques for a variety of purposes, including resolution enhancement, reconstruction artifact removal, undersampling correction, and improved quantification. Most of these efforts have proven to be highly promising in addressing long-standing technical obstacles where traditional solutions either completely fail or make only incremental progress. This concise review focuses on the history of applied artificial intelligence in photoacoustic tomography, presents recent advances at this multifaceted intersection of fields, and outlines the most exciting advances that will likely propagate into promising future innovations.

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“…Morphologic features can be examined through different non-invasive imaging modalities including Optical Coherence Tomography (OCT), Reflectance Confocal Microscopy (RCT), and Ultrasonography see the complete list of these imaging modalities in [ 43 , 50 , 51 , 52 , 53 ], including quantitative dynamic infrared imaging, hyperspectral imaging, multispectral imaging, electrical impedance spectroscopy, and photoacoustic imaging (both microscopy and tomography) [ 54 , 55 , 56 , 57 , 58 , 59 ]. Raman spectrometry, real-time elastography, terahertz pulse imaging, multiphoton imaging, magnetic resonance imaging, positron emission tomography, fiber diffraction, Fourier transform infrared spectroscopy, and reflex transmission imaging.…”

Section: Melanoma Biomarkersmentioning

confidence: 99%

Melanoma Biomarkers and Their Potential Application for In Vivo Diagnostic Imaging Modalities

Hessler

Jalilian

et al. 2020

IJMS

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Melanoma is the deadliest form of skin cancer and remains a diagnostic challenge in the dermatology clinic. Several non-invasive imaging techniques have been developed to identify melanoma. The signal source in each of these modalities is based on the alteration of physical characteristics of the tissue from healthy/benign to melanoma. However, as these characteristics are not always sufficiently specific, the current imaging techniques are not adequate for use in the clinical setting. A more robust way of melanoma diagnosis is to “stain” or selectively target the suspect tissue with a melanoma biomarker attached to a contrast enhancer of one imaging modality. Here, we categorize and review known melanoma diagnostic biomarkers with the goal of guiding skin imaging experts to design an appropriate diagnostic tool for differentiating between melanoma and benign lesions with a high specificity and sensitivity.

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Deep learning protocol for improved photoacoustic brain imaging

Cited by 65 publications

References 10 publications

Deep learning in photoacoustic imaging: a review

Deep learning in photoacoustic imaging: a review

Sounding out the hidden data: A concise review of deep learning in photoacoustic imaging

Melanoma Biomarkers and Their Potential Application for In Vivo Diagnostic Imaging Modalities

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