Non‐local means improves total‐variation constrained photoacoustic image reconstruction

Yalavarthy, Phaneendra K.; Kalva, Sandeep Kumar; Pramanik, Manojit; Prakash, Jaya

doi:10.1002/jbio.202000191

Cited by 14 publications

(8 citation statements)

References 21 publications

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“…This was exploited to enhance the spatial resolution beyond the acoustic diffraction barrier [76,92,93]. TV-based regularization is also commonly used as it enables mitigating noise while preserving sharp edges [52,53,[94][95][96][97][98]. Figure 3A displays a comparison of the images of a freely-swimming zebrafish larvae reconstructed with FBP and MB including a TV regularization term in the spatial and temporal domains, i.e., R(p 0 ) (…”

Section: The Inverse Problemmentioning

confidence: 99%

A practical guide for model-based reconstruction in optoacoustic imaging

Deán‐Ben

Razansky

2022

Front. Phys.

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Optoacoustic (OA, photoacoustic) imaging capitalizes on the low scattering of ultrasound within biological tissues to provide optical absorption-based contrast with high resolution at depths not reachable with optical microscopy. For deep tissue imaging applications, OA image formation commonly relies on acoustic inversion of time-resolved tomographic data. The excitation of OA responses and subsequent propagation of ultrasound waves can be mathematically described as a forward model enabling image reconstruction via algebraic inversion. These model-based reconstruction methods have been shown to outperform alternative inversion approaches and can further render OA images from incomplete datasets, strongly distorted signals or other suboptimally recorded data. Herein, we provide a general perspective on model-based OA reconstruction methods, review recent progress, and discuss the performance of the different algorithms under practical imaging scenarios.

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Section: The Inverse Problemmentioning

confidence: 99%

A practical guide for model-based reconstruction in optoacoustic imaging

Deán‐Ben

Razansky

2022

Front. Phys.

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“…PACT reconstruction is often ill posed and prone to artifacts, mostly due to heterogeneous target properties (e.g., speed of sound) and system parameters such as limitedview, limited-bandwidth detection, and sparse sampling. Traditional reconstruction methods often incorporate implicit or explicit prior knowledge such as l 1 , l 2 , and total variation (TV) regularization 16,36 to optimize the illposed inverse process, which are typically very time consuming and highly sensitive to noise. By contrast, DL-based approaches, such as model-based learning, have replaced the traditional regularization terms with a learned regularization term, and thus can be less time consuming.…”

Section: Deep Learning In Pactmentioning

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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“…These acoustic waves (also known as PA waves) are later converted into an absorption map of the tissue with the help of various reconstruction algorithms. 26 – 35 Aided with modern deep learning methods, the recovery of absorption maps has become more accurate and faster with better resolution and fewer artifacts. 36 – 44 These absorption maps provide the structural/functional information of the tissue inside the body.…”

Section: Introductionmentioning

confidence: 99%

Looking deep inside tissue with photoacoustic molecular probes: a review

2022

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. Significance: Deep tissue noninvasive high-resolution imaging with light is challenging due to the high degree of light absorption and scattering in biological tissue. Photoacoustic imaging (PAI) can overcome some of the challenges of pure optical or ultrasound imaging to provide high-resolution deep tissue imaging. However, label-free PAI signals from light absorbing chromophores within the tissue are nonspecific. The use of exogeneous contrast agents (probes) not only enhances the imaging contrast (and imaging depth) but also increases the specificity of PAI by binding only to targeted molecules and often providing signals distinct from the background. Aim: We aim to review the current development and future progression of photoacoustic molecular probes/contrast agents. Approach: First, PAI and the need for using contrast agents are briefly introduced. Then, the recent development of contrast agents in terms of materials used to construct them is discussed. Then, various probes are discussed based on targeting mechanisms, in vivo molecular imaging applications, multimodal uses, and use in theranostic applications. Results: Material combinations are being used to develop highly specific contrast agents. In addition to passive accumulation, probes utilizing activation mechanisms show promise for greater controllability. Several probes also enable concurrent multimodal use with fluorescence, ultrasound, Raman, magnetic resonance imaging, and computed tomography. Finally, targeted probes are also shown to aid localized and molecularly specific photo-induced therapy. Conclusions: The development of contrast agents provides a promising prospect for increased contrast, higher imaging depth, and molecularly specific information. Of note are agents that allow for controlled activation, explore other optical windows, and enable multimodal use to overcome some of the shortcomings of label-free PAI.

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Non‐local means improves total‐variation constrained photoacoustic image reconstruction

Cited by 14 publications

References 21 publications

A practical guide for model-based reconstruction in optoacoustic imaging

A practical guide for model-based reconstruction in optoacoustic imaging

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

Looking deep inside tissue with photoacoustic molecular probes: a review

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