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An F, Xin J, Deng C, Tan X, Aras O, Chen N, Zhang X, Ting R. Facile synthesis of near-infrared bodipy by donor engineering for in vivo tumor targeted dual-modal imaging. J Mater Chem B 2021; 9:9308-9315. [PMID: 34714318 PMCID: PMC8616829 DOI: 10.1039/d1tb01883c] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Bodipy is one of the most popular dyes for bioimaging, however, a complicated synthetic protocol is needed to create and isolate ideal near-infrared (NIR) emissive Bodipy derivatives for optical bioimaging. It is noticed that the donor species impact the wavelength when the π-conjugation system of green light emissive Bodipy is elongated via a one-step reaction. Herein, several Bodipy dyes bearing different common donors are synthesized. Their optical properties confirm that both absorption and emission peaks of the synthesized Bodipy could be tuned to NIR wavelength by using stronger donors via a facile reaction. The synthesized monocarboxyl Bodipy could conjugate with aminated PEG to yield an amphiphilic polymer, which further self-assembles into a NIR nanoparticle (NP). The NIR NP exhibits preferential tumor accumulation via the enhanced permeation and retention (EPR) effect, making it useful for tumor diagnosis by both fluorescence imaging and photoacoustic tomography.
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Discretization of Learned NETT Regularization for Solving Inverse Problems. J Imaging 2021; 7:jimaging7110239. [PMID: 34821870 PMCID: PMC8625045 DOI: 10.3390/jimaging7110239] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/05/2021] [Accepted: 11/08/2021] [Indexed: 11/24/2022] Open
Abstract
Deep learning based reconstruction methods deliver outstanding results for solving inverse problems and are therefore becoming increasingly important. A recently invented class of learning-based reconstruction methods is the so-called NETT (for Network Tikhonov Regularization), which contains a trained neural network as regularizer in generalized Tikhonov regularization. The existing analysis of NETT considers fixed operators and fixed regularizers and analyzes the convergence as the noise level in the data approaches zero. In this paper, we extend the frameworks and analysis considerably to reflect various practical aspects and take into account discretization of the data space, the solution space, the forward operator and the neural network defining the regularizer. We show the asymptotic convergence of the discretized NETT approach for decreasing noise levels and discretization errors. Additionally, we derive convergence rates and present numerical results for a limited data problem in photoacoustic tomography.
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Hsu KT, Guan S, Chitnis PV. Comparing Deep Learning Frameworks for Photoacoustic Tomography Image Reconstruction. PHOTOACOUSTICS 2021; 23:100271. [PMID: 34094851 PMCID: PMC8165448 DOI: 10.1016/j.pacs.2021.100271] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 04/08/2021] [Accepted: 05/11/2021] [Indexed: 05/02/2023]
Abstract
Conventional reconstruction methods for photoacoustic images are not suitable for the scenario of sparse sensing and geometrical limitation. To overcome these challenges and enhance the quality of reconstruction, several learning-based methods have recently been introduced for photoacoustic tomography reconstruction. The goal of this study is to compare and systematically evaluate the recently proposed learning-based methods and modified networks for photoacoustic image reconstruction. Specifically, learning-based post-processing methods and model-based learned iterative reconstruction methods are investigated. In addition to comparing the differences inherently brought by the models, we also study the impact of different inputs on the reconstruction effect. Our results demonstrate that the reconstruction performance mainly stems from the effective amount of information carried by the input. The inherent difference of the models based on the learning-based post-processing method does not provide a significant difference in photoacoustic image reconstruction. Furthermore, the results indicate that the model-based learned iterative reconstruction method outperforms all other learning-based post-processing methods in terms of generalizability and robustness.
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Ai M, Cheng J, Karimi D, Salcudean SE, Rohling R, Abolmaesumi P, Tang S. Investigation of photoacoustic tomography reconstruction with a limited view from linear array. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-210083RR. [PMID: 34585543 PMCID: PMC8477256 DOI: 10.1117/1.jbo.26.9.096009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 09/08/2021] [Indexed: 06/13/2023]
Abstract
SIGNIFICANCE As linear array transducers are widely used in clinical ultrasound imaging, photoacoustic tomography (PAT) with linear arrays is similarly suitable for clinical applications. However, due to the limited-view problem, a linear array has limited performance and leads to artifacts and blurring, which has hindered its broader application. There is a need to address the limited-view problem in PAT imaging with linear arrays. AIM We investigate potential approaches for improving PAT reconstruction from linear array, by optimizing the detection geometry and implementing iterative reconstruction. APPROACH PAT imaging with a single-array, dual-probe configurations in parallel-shape and L-shape, and square-shape configuration are compared in simulations and phantom experiments. An iterative model-based algorithm based on the variance-reduced stochastic gradient descent (VR-SGD) method is implemented. The optimum configuration found in simulation is validated on phantom experiments. RESULTS PAT imaging with dual-probe detection and VR-SGD algorithm is found to improve the limited-view problem compared to a single probe and provide comparable performance as full-view geometry in simulation. This configuration is validated in experiments where more complete structure is obtained with reduced artifacts compared with a single array. CONCLUSIONS PAT with dual-probe detection and iterative reconstruction is a promising solution to the limited-view problem of linear arrays.
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Prakash J, Kalva SK, Pramanik M, Yalavarthy PK. Binary photoacoustic tomography for improved vasculature imaging. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-210135R. [PMID: 34405599 PMCID: PMC8370884 DOI: 10.1117/1.jbo.26.8.086004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 06/29/2021] [Indexed: 05/09/2023]
Abstract
SIGNIFICANCE The proposed binary tomography approach was able to recover the vasculature structures accurately, which could potentially enable the utilization of binary tomography algorithm in scenarios such as therapy monitoring and hemorrhage detection in different organs. AIM Photoacoustic tomography (PAT) involves reconstruction of vascular networks having direct implications in cancer research, cardiovascular studies, and neuroimaging. Various methods have been proposed for recovering vascular networks in photoacoustic imaging; however, most methods are two-step (image reconstruction and image segmentation) in nature. We propose a binary PAT approach wherein direct reconstruction of vascular network from the acquired photoacoustic sinogram data is plausible. APPROACH Binary tomography approach relies on solving a dual-optimization problem to reconstruct images with every pixel resulting in a binary outcome (i.e., either background or the absorber). Further, the binary tomography approach was compared against backprojection, Tikhonov regularization, and sparse recovery-based schemes. RESULTS Numerical simulations, physical phantom experiment, and in-vivo rat brain vasculature data were used to compare the performance of different algorithms. The results indicate that the binary tomography approach improved the vasculature recovery by 10% using in-silico data with respect to the Dice similarity coefficient against the other reconstruction methods. CONCLUSION The proposed algorithm demonstrates superior vasculature recovery with limited data both visually and based on quantitative image metrics.
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Yao J, Wang LV. Perspective on fast-evolving photoacoustic tomography. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-210105-PERR. [PMID: 34196136 PMCID: PMC8244998 DOI: 10.1117/1.jbo.26.6.060602] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 06/17/2021] [Indexed: 05/19/2023]
Abstract
SIGNIFICANCE Acoustically detecting the rich optical absorption contrast in biological tissues, photoacoustic tomography (PAT) seamlessly bridges the functional and molecular sensitivity of optical excitation with the deep penetration and high scalability of ultrasound detection. As a result of continuous technological innovations and commercial development, PAT has been playing an increasingly important role in life sciences and patient care, including functional brain imaging, smart drug delivery, early cancer diagnosis, and interventional therapy guidance. AIM Built on our 2016 tutorial article that focused on the principles and implementations of PAT, this perspective aims to provide an update on the exciting technical advances in PAT. APPROACH This perspective focuses on the recent PAT innovations in volumetric deep-tissue imaging, high-speed wide-field microscopic imaging, high-sensitivity optical ultrasound detection, and machine-learning enhanced image reconstruction and data processing. Representative applications are introduced to demonstrate these enabling technical breakthroughs in biomedical research. CONCLUSIONS We conclude the perspective by discussing the future development of PAT technologies.
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Wang B, Ye T, Wang G, Guo L, Xiao J. Approximate back-projection method for improving lateral resolution in circular-scanning-based photoacoustic tomography. Med Phys 2021; 48:3011-3021. [PMID: 33837541 DOI: 10.1002/mp.14880] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 04/03/2021] [Accepted: 04/03/2021] [Indexed: 12/25/2022] Open
Abstract
PURPOSE In circular-scanning-based photoacoustic tomography (PAT), the effect of finite transducer aperture has not been effectively resolved. The goal of this paper is to propose a practical reconstruction method that accounts for the finite transducer aperture to improve the lateral resolution. METHODS We for the first time propose to calculate the spatial-temporal response (STR) of the employed finite-sized transducer in a forward model, and then compensate the time delay and the directional sensitivity of the transducer in the framework of the back-projection method. Both simulation and phantom experiments were carried out to evaluate the lateral resolution improvement with the proposed method. The performance of this new method for imaging complicated targets was also assessed by calculating the mean image gradient. RESULTS Simulation results showed that with this new method the lateral resolution for off-center targets can be as good as that for the center targets. Phantom experimental results showed that this new method can improve the lateral resolution more than two times for a point target about 5 mm far from the rotation center. Phantom experimental results also showed that many blurred fine structures of a piece of leaf veins at the off-center regions were well restored with the new method, and the mean image gradient improved about 1.3 times. CONCLUSION The proposed new method can effectively account for the effect of finite transducer aperture for circular-scanning-based PAT in homogenous acoustic media. This new method also features its robustness and computational efficiency, so that it is a worthy replacement to the conventional back-projection algorithm in circular-scanning-based PAT. This new method can be of great importance to the design of circular-scanning or spherical-scanning-based PAT systems.
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Du J, Sun Z. [Progress of motion artifact correction in photoacoustic microscopy and photoacoustic tomography]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2021; 38:369-378. [PMID: 33913298 DOI: 10.7507/1001-5515.202009062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Photoacoustic imaging (PAI) is a rapidly developing hybrid biomedical imaging technology, which is capable of providing structural and functional information of biological tissues. Due to inevitable motion of the imaging object, such as respiration, heartbeat or eye rotation, motion artifacts are observed in the reconstructed images, which reduce the imaging resolution and increase the difficulty of obtaining high-quality images. This paper summarizes current methods for correcting and compensating motion artifacts in photoacoustic microscopy (PAM) and photoacoustic tomography (PAT), discusses their advantages and limits and forecasts possible future work.
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Amidi E, Yang G, Uddin KMS, Luo H, Middleton W, Powell M, Siegel C, Zhu Q. Role of blood oxygenation saturation in ovarian cancer diagnosis using multi-spectral photoacoustic tomography. JOURNAL OF BIOPHOTONICS 2021; 14:e202000368. [PMID: 33377620 PMCID: PMC8044001 DOI: 10.1002/jbio.202000368] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 12/18/2020] [Accepted: 12/19/2020] [Indexed: 05/05/2023]
Abstract
In photoacoustic tomography (PAT), a tunable laser typically illuminates the tissue at multiple wavelengths, and the received photoacoustic waves are used to form functional images of relative total haemoglobin (rHbT) and blood oxygenation saturation (%sO2 ). Due to measurement errors, the estimation of these parameters can be challenging, especially in clinical studies. In this study, we use a multi-pixel method to smooth the measurements before calculating rHbT and %sO2 . We first perform phantom studies using blood tubes of calibrated %sO2 to evaluate the accuracy of our %sO2 estimation. We conclude by presenting diagnostic results from PAT of 33 patients with 51 ovarian masses imaged by our co-registered PAT and ultrasound system. The ovarian masses were divided into malignant and benign/normal groups. Functional maps of rHbT and %sO2 and their histograms as well as spectral features were calculated using the PAT data from all ovaries in these two groups. Support vector machine models were trained on different combinations of the significant features. The area under ROC (AUC) of 0.93 (0.95%CI: 0.90-0.96) on the testing data set was achieved by combining mean %sO2 , a spectral feature, and the score of the study radiologist.
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Tordera Mora J, Feng X, Nyayapathi N, Xia J, Gao L. Generalized spatial coherence reconstruction for photoacoustic computed tomography. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-210008R. [PMID: 33880892 PMCID: PMC8056071 DOI: 10.1117/1.jbo.26.4.046002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 03/11/2021] [Indexed: 06/12/2023]
Abstract
SIGNIFICANCE Coherence, a fundamental property of waves and fields, plays a key role in photoacoustic image reconstruction. Previously, techniques such as short-lag spatial coherence (SLSC) and filtered delay, multiply, and sum (FDMAS) have utilized spatial coherence to improve the reconstructed resolution and contrast with respect to delay-and-sum (DAS). While SLSC uses spatial coherence directly as the imaging contrast, FDMAS employs spatial coherence implicitly. Despite being more robust against noise, both techniques have their own drawbacks: SLSC does not preserve a relative signal magnitude, and FDMAS shows a reduced contrast-to-noise ratio. AIM To overcome these limitations, our aim is to develop a beamforming algorithm-generalized spatial coherence (GSC)-that unifies SLSC and FDMAS into a single equation and outperforms both beamformers. APPROACH We demonstrated the application of GSC in photoacoustic computed tomography (PACT) through simulation and experiments and compared it to previous beamformers: DAS, FDMAS, and SLSC. RESULTS GSC outperforms the imaging metrics of previous state-of-the-art coherence-based beamformers in both simulation and experiments. CONCLUSIONS GSC is an innovative reconstruction algorithm for PACT, which combines the strengths of FDMAS and SLSC expanding PACT's applications.
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Dantuma M, Kruitwagen S, Ortega-Julia J, Pompe van Meerdervoort RP, Manohar S. Tunable blood oxygenation in the vascular anatomy of a semi-anthropomorphic photoacoustic breast phantom. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-200370RR. [PMID: 33728828 PMCID: PMC7961914 DOI: 10.1117/1.jbo.26.3.036003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 02/19/2021] [Indexed: 05/21/2023]
Abstract
SIGNIFICANCE Recovering accurate oxygenation estimations in the breast with quantitative photoacoustic tomography (QPAT) is not straightforward. Accurate light fluence models are required, but the unknown ground truth of the breast makes it difficult to validate them. Phantoms are often used for the validation, but most reported phantoms have a simple architecture. Fluence models developed in these simplistic objects are not accurate for application on the complex tissues of the breast. AIM We present a sophisticated breast phantom platform for photoacoustic (PA) and ultrasound (US) imaging in general, and specifically for QPAT. The breast phantom is semi-anthropomorphic in distribution of optical and acoustic properties and contains wall-less channels with blood. APPROACH 3D printing approaches are used to develop the solid 3D breast phantom from custom polyvinyl chloride plastisol formulations and additives for replicating the tissue optical and acoustic properties. A flow circuit was developed to flush the channels with bovine blood with a controlled oxygen saturation level. To showcase the phantom's functionality, PA measurements were performed on the phantom with two oxygenation levels. Image reconstructions with and without fluence compensation from Monte Carlo simulations were analyzed for the accuracy of oxygen saturation estimations. RESULTS We present design aspects of the phantom, demonstrate how it is developed, and present its breast-like appearance in PA and US imaging. The oxygen saturations were estimated in two regions of interest with and without using the fluence models. The fluence compensation positively influenced the SO2 estimations in all cases and confirmed that highly accurate fluence models are required to minimize estimation errors. CONCLUSIONS This phantom allows studies to be performed in PA in carefully controlled laboratory settings to validate approaches to recover both qualitative and quantitative features sought after in in-vivo studies. We believe that testing with phantoms of this complexity can streamline the transition of new PA technologies from the laboratory to studies in the clinic.
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Shahid H, Khalid A, Liu X, Irfan M, Ta D. A Deep Learning Approach for the Photoacoustic Tomography Recovery From Undersampled Measurements. Front Neurosci 2021; 15:598693. [PMID: 33716643 PMCID: PMC7943731 DOI: 10.3389/fnins.2021.598693] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 01/06/2021] [Indexed: 11/13/2022] Open
Abstract
Photoacoustic tomography (PAT) is a propitious imaging modality, which is helpful for biomedical study. However, fast PAT imaging and denoising is an exigent task in medical research. To address the problem, recently, methods based on compressed sensing (CS) have been proposed, which accede the low computational cost and high resolution for implementing PAT. Nevertheless, the imaging results of the sparsity-based methods strictly rely on sparsity and incoherence conditions. Furthermore, it is onerous to ensure that the experimentally acquired photoacoustic data meets CS's prerequisite conditions. In this work, a deep learning-based PAT (Deep-PAT)method is instigated to overcome these limitations. By using a neural network, Deep-PAT is not only able to reconstruct PAT from a fewer number of measurements without considering the prerequisite conditions of CS, but also can eliminate undersampled artifacts effectively. The experimental results demonstrate that Deep-PAT is proficient at recovering high-quality photoacoustic images using just 5% of the original measurement data. Besides this, compared with the sparsity-based method, it can be seen through statistical analysis that the quality is significantly improved by 30% (approximately), having average SSIM = 0.974 and PSNR = 29.88 dB with standard deviation ±0.007 and ±0.089, respectively, by the proposed Deep-PAT method. Also, a comparsion of multiple neural networks provides insights into choosing the best one for further study and practical implementation.
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Li M, Vu T, Sankin G, Winship B, Boydston K, Terry R, Zhong P, Yao J. Internal-Illumination Photoacoustic Tomography Enhanced by a Graded-Scattering Fiber Diffuser. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:346-356. [PMID: 32986546 PMCID: PMC7772228 DOI: 10.1109/tmi.2020.3027199] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
The penetration depth of photoacoustic imaging in biological tissues has been fundamentally limited by the strong optical attenuation when light is delivered externally through the tissue surface. To address this issue, we previously reported internal-illumination photoacoustic imaging using a customized radial-emission optical fiber diffuser, which, however, has complex fabrication, high cost, and non-uniform light emission. To overcome these shortcomings, we have developed a new type of low-cost fiber diffusers based on a graded-scattering method in which the optical scattering of the fiber diffuser is gradually increased as the light travels. The graded scattering can compensate for the optical attenuation and provide relatively uniform light emission along the diffuser. We performed Monte Carlo numerical simulations to optimize several key design parameters, including the number of scattering segments, scattering anisotropy factor, divergence angle of the optical fiber, and reflective index of the surrounding medium. These optimized parameters collectively result in uniform light emission along the fiber diffuser and can be flexibly adjusted to accommodate different applications. We fabricated and characterized the prototype fiber diffuser made of agarose gel and intralipid. Equipped with the new fiber diffuser, we performed thorough proof-of-concept studies on ex vivo tissue phantoms and an in vivo swine model to demonstrate the deep-imaging capability (~10 cm achieved ex vivo) of photoacoustic tomography. We believe that the internal light delivery via the optimized fiber diffuser is an effective strategy to image deep targets (e.g., kidney) in large animals or humans.
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Yalavarthy PK, Kalva SK, Pramanik M, Prakash J. Non-local means improves total-variation constrained photoacoustic image reconstruction. JOURNAL OF BIOPHOTONICS 2021; 14:e202000191. [PMID: 33025761 DOI: 10.1002/jbio.202000191] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 09/30/2020] [Accepted: 10/01/2020] [Indexed: 05/20/2023]
Abstract
Photoacoustic/Optoacoustic tomography aims to reconstruct maps of the initial pressure rise induced by the absorption of light pulses in tissue. This reconstruction is an ill-conditioned and under-determined problem, when the data acquisition protocol involves limited detection positions. The aim of the work is to develop an inversion method which integrates denoising procedure within the iterative model-based reconstruction to improve quantitative performance of optoacoustic imaging. Among the model-based schemes, total-variation (TV) constrained reconstruction scheme is a popular approach. In this work, a two-step approach was proposed for improving the TV constrained optoacoustic inversion by adding a non-local means based filtering step within each TV iteration. Compared to TV-based reconstruction, inclusion of this non-local means step resulted in signal-to-noise ratio improvement of 2.5 dB in the reconstructed optoacoustic images.
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Li M, Nyayapathi N, Kilian HI, Xia J, Lovell JF, Yao J. Sound Out the Deep Colors: Photoacoustic Molecular Imaging at New Depths. Mol Imaging 2020; 19:1536012120981518. [PMID: 33336621 PMCID: PMC7750763 DOI: 10.1177/1536012120981518] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Photoacoustic tomography (PAT) has become increasingly popular for molecular imaging due to its unique optical absorption contrast, high spatial resolution, deep imaging depth, and high imaging speed. Yet, the strong optical attenuation of biological tissues has traditionally prevented PAT from penetrating more than a few centimeters and limited its application for studying deeply seated targets. A variety of PAT technologies have been developed to extend the imaging depth, including employing deep-penetrating microwaves and X-ray photons as excitation sources, delivering the light to the inside of the organ, reshaping the light wavefront to better focus into scattering medium, as well as improving the sensitivity of ultrasonic transducers. At the same time, novel optical fluence mapping algorithms and image reconstruction methods have been developed to improve the quantitative accuracy of PAT, which is crucial to recover weak molecular signals at larger depths. The development of highly-absorbing near-infrared PA molecular probes has also flourished to provide high sensitivity and specificity in studying cellular processes. This review aims to introduce the recent developments in deep PA molecular imaging, including novel imaging systems, image processing methods and molecular probes, as well as their representative biomedical applications. Existing challenges and future directions are also discussed.
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Olefir I, Tzoumas S, Restivo C, Mohajerani P, Xing L, Ntziachristos V. Deep Learning-Based Spectral Unmixing for Optoacoustic Imaging of Tissue Oxygen Saturation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:3643-3654. [PMID: 32746111 PMCID: PMC7671861 DOI: 10.1109/tmi.2020.3001750] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Label free imaging of oxygenation distribution in tissues is highly desired in numerous biomedical applications, but is still elusive, in particular in sub-epidermal measurements. Eigenspectra multispectral optoacoustic tomography (eMSOT) and its Bayesian-based implementation have been introduced to offer accurate label-free blood oxygen saturation (sO2) maps in tissues. The method uses the eigenspectra model of light fluence in tissue to account for the spectral changes due to the wavelength dependent attenuation of light with tissue depth. eMSOT relies on the solution of an inverse problem bounded by a number of ad hoc hand-engineered constraints. Despite the quantitative advantage offered by eMSOT, both the non-convex nature of the optimization problem and the possible sub-optimality of the constraints may lead to reduced accuracy. We present herein a neural network architecture that is able to learn how to solve the inverse problem of eMSOT by directly regressing from a set of input spectra to the desired fluence values. The architecture is composed of a combination of recurrent and convolutional layers and uses both spectral and spatial features for inference. We train an ensemble of such networks using solely simulated data and demonstrate how this approach can improve the accuracy of sO2 computation over the original eMSOT, not only in simulations but also in experimental datasets obtained from blood phantoms and small animals (mice) in vivo. The use of a deep-learning approach in optoacoustic sO2 imaging is confirmed herein for the first time on ground truth sO2 values experimentally obtained in vivo and ex vivo.
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Hauptmann A, Cox B. Deep learning in photoacoustic tomography: current approaches and future directions. JOURNAL OF BIOMEDICAL OPTICS 2020; 25:112903. [PMCID: PMC7593654 DOI: 10.1117/1.jbo.25.11.112903] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 09/24/2020] [Indexed: 05/18/2023]
Abstract
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.
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Tsang VT, Li X, Wong TT. A Review of Endogenous and Exogenous Contrast Agents Used in Photoacoustic Tomography with Different Sensing Configurations. SENSORS 2020; 20:s20195595. [PMID: 33003566 PMCID: PMC7582683 DOI: 10.3390/s20195595] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 09/18/2020] [Accepted: 09/26/2020] [Indexed: 12/17/2022]
Abstract
Optical-based sensing approaches have long been an indispensable way to detect molecules in biological tissues for various biomedical research and applications. The advancement in optical microscopy is one of the main drivers for discoveries and innovations in both life science and biomedical imaging. However, the shallow imaging depth due to the use of ballistic photons fundamentally limits optical imaging approaches’ translational potential to a clinical setting. Photoacoustic (PA) tomography (PAT) is a rapidly growing hybrid imaging modality that is capable of acoustically detecting optical contrast. PAT uniquely enjoys high-resolution deep-tissue imaging owing to the utilization of diffused photons. The exploration of endogenous contrast agents and the development of exogenous contrast agents further improve the molecular specificity for PAT. PAT’s versatile design and non-invasive nature have proven its great potential as a biomedical imaging tool for a multitude of biomedical applications. In this review, representative endogenous and exogenous PA contrast agents will be introduced alongside common PAT system configurations, including the latest advances of all-optical acoustic sensing techniques.
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Wu J, Lee HJ, You L, Luo X, Hasegawa T, Huang KC, Lin P, Ratliff T, Ashizawa M, Mei J, Cheng JX. Functionalized NIR-II Semiconducting Polymer Nanoparticles for Single-cell to Whole-Organ Imaging of PSMA-Positive Prostate Cancer. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 16:e2001215. [PMID: 32307923 DOI: 10.1002/smll.202001215] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 03/22/2020] [Accepted: 03/24/2020] [Indexed: 06/11/2023]
Abstract
Development of molecular probes holds great promise for early diagnosis of aggressive prostate cancer. Here, 2-[3-(1,3-dicarboxypropyl) ureido] pentanedioic acid (DUPA)-conjugated ligand and bis-isoindigo-based polymer (BTII) are synthesized to formulate semiconducting polymer nanoparticles (BTII-DUPA SPN) as a prostate-specific membrane antigen (PSMA)-targeted probe for prostate cancer imaging in the NIR-II window. Insights into the interaction of the imaging probes with the biological targets from single cell to whole organ are obtained by transient absorption (TA) microscopy and photoacoustic (PA) tomography. At single-cell level, TA microscopy reveals the targeting efficiency, kinetics, and specificity of BTII-DUPA SPN to PSMA-positive prostate cancer. At organ level, PA tomographic imaging of BTII-DUPA SPN in the NIR-II window demonstrates superior imaging depth and contrast. By intravenous administration, BTII-DUPA SPN demonstrates selective accumulation and retention in the PSMA-positive tumor, allowing noninvasive PA detection of PSMA overexpressing prostate tumors in vivo. The distribution of nanoparticles inside the tumor tissue is further analyzed through TA microscopy. These results collectively demonstrate BTII-DUPA SPN as a promising probe for prostate cancer diagnosis by PA tomography.
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Yang H, Shan T, Yang L, Jiang H. Fan-shaped scanning approach for miniaturized photoacoustic tomography. JOURNAL OF BIOPHOTONICS 2020; 13:e201960102. [PMID: 31664788 PMCID: PMC8162992 DOI: 10.1002/jbio.201960102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 10/24/2019] [Accepted: 10/25/2019] [Indexed: 06/10/2023]
Abstract
We describe a novel scanning approach for miniaturized photoacoustic tomography (PAT), based on fan-shaped scanning of a single transducer at one or two discrete positions. This approach is tested and evaluated using several phantom and animal experiments. The results obtained show that this new scanning approach provides high image quality in the configuration of miniaturized handheld or endoscopic PAT with improved effective field of view and penetration depth.
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Sharma A, Srishti, Periyasamy V, Pramanik M. Photoacoustic imaging depth comparison at 532-, 800-, and 1064-nm wavelengths: Monte Carlo simulation and experimental validation. JOURNAL OF BIOMEDICAL OPTICS 2019; 24:121904. [PMCID: PMC7005538 DOI: 10.1117/1.jbo.24.12.121904] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 07/18/2019] [Indexed: 07/30/2023]
Abstract
Photoacoustic imaging (PAI) provides high-resolution and high-optical-contrast imaging beyond optical diffusion limit. Further improvement in imaging depth has been achieved by using near-infrared window-I (NIR-I, 700 to 900 nm) for illumination, due to lower scattering and absorption by tissues in this wavelength range. Recently, near-infrared window-II (NIR-II, 900 to 1700 nm) has been explored for PAI. We studied the imaging depths in biological tissues for different illumination wavelengths in visible, NIR-I, and NIR-II regions using Monte Carlo (MC) simulations and validated with experimental results. MC simulations were done to compute fluence in tissue, absorbance in blood vessel, and in a spherical absorber (mimicking sentinel lymph node) embedded at different depths in breast tissue. Photoacoustic tomography and acoustic resolution photoacoustic microscopy experiments were conducted to validate the MC results. We demonstrate that maximum imaging depth is achieved by wavelengths in NIR-I window (∼800 nm) when the energy density deposited is same for all wavelengths. However, illumination using wavelengths around 1064 nm (NIR-II window) gives the maximum imaging depth when the energy density deposited is proportional to maximum permissible exposure (MPE) at corresponding wavelength. These results show that it is the higher MPE of NIR-II window that helps in increasing the PAI depth for chromophores embedded in breast tissue.
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Gehrung M, Bohndiek SE, Brunker J. Development of a blood oxygenation phantom for photoacoustic tomography combined with online pO2 detection and flow spectrometry. JOURNAL OF BIOMEDICAL OPTICS 2019; 24:1-11. [PMID: 31625321 PMCID: PMC7005535 DOI: 10.1117/1.jbo.24.12.121908] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 09/09/2019] [Indexed: 05/07/2023]
Abstract
Photoacoustic tomography (PAT) is intrinsically sensitive to blood oxygen saturation (sO2) in vivo. However, making accurate sO2 measurements without knowledge of tissue- and instrumentation-related correction factors is extremely challenging. We have developed a low-cost flow phantom to facilitate validation of PAT systems. The phantom is composed of a flow circuit of tubing partially embedded within a tissue-mimicking material, with independent sensors providing online monitoring of the optical absorption spectrum and partial pressure of oxygen in the tube. We first test the flow phantom using two small molecule dyes that are frequently used for photoacoustic imaging: methylene blue and indocyanine green. We then demonstrate the potential of the phantom for evaluating sO2 using chemical oxygenation and deoxygenation of blood in the circuit. Using this dynamic assessment of the photoacoustic sO2 measurement in phantoms in relation to a ground truth, we explore the influence of multispectral processing and spectral coloring on accurate assessment of sO2. Future studies could exploit this low-cost dynamic flow phantom to validate fluence correction algorithms and explore additional blood parameters such as pH and also absorptive and other properties of different fluids.
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Vu T, Razansky D, Yao J. Listening to tissues with new light: recent technological advances in photoacoustic imaging. JOURNAL OF OPTICS (2010) 2019; 21:10.1088/2040-8986/ab3b1a. [PMID: 32051756 PMCID: PMC7015182 DOI: 10.1088/2040-8986/ab3b1a] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Photoacoustic tomography (PAT), or optoacoustic tomography, has achieved remarkable progress in the past decade, benefiting from the joint developments in optics, acoustics, chemistry, computing and mathematics. Unlike pure optical or ultrasound imaging, PAT can provide unique optical absorption contrast as well as widely scalable spatial resolution, penetration depth and imaging speed. Moreover, PAT has inherent sensitivity to tissue's functional, molecular, and metabolic state. With these merits, PAT has been applied in a wide range of life science disciplines, and has enabled biomedical research unattainable by other imaging methods. This Review article aims at introducing state-of-the-art PAT technologies and their representative applications. The focus is on recent technological breakthroughs in structural, functional, molecular PAT, including super-resolution imaging, real-time small-animal whole-body imaging, and high-sensitivity functional/molecular imaging. We also discuss the remaining challenges in PAT and envisioned opportunities.
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Huynh N, Lucka F, Zhang E, Betcke M, Arridge SR, Beard PC, Cox BT. Single-pixel camera photoacoustic tomography. JOURNAL OF BIOMEDICAL OPTICS 2019; 24:1-6. [PMID: 31535537 PMCID: PMC7005533 DOI: 10.1117/1.jbo.24.12.121907] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 08/19/2019] [Indexed: 05/18/2023]
Abstract
Since it was first demonstrated more than a decade ago, the single-pixel camera concept has been used in numerous applications in which it is necessary or advantageous to reduce the channel count, cost, or data volume. Here, three-dimensional (3-D), compressed-sensing photoacoustic tomography (PAT) is demonstrated experimentally using a single-pixel camera. A large area collimated laser beam is reflected from a planar Fabry–Pérot ultrasound sensor onto a digital micromirror device, which patterns the light using a scrambled Hadamard basis before it is collected into a single photodetector. In this way, inner products of the Hadamard patterns and the distribution of thickness changes of the FP sensor—induced by the photoacoustic waves—are recorded. The initial distribution of acoustic pressure giving rise to those photoacoustic waves is recovered directly from the measured signals using an accelerated proximal gradient-type algorithm to solve a model-based minimization with total variation regularization. Using this approach, it is shown that 3-D PAT of imaging phantoms can be obtained with compression rates as low as 10%. Compressed sensing approaches to photoacoustic imaging, such as this, have the potential to reduce the data acquisition time as well as the volume of data it is necessary to acquire, both of which are becoming increasingly important in the drive for faster imaging systems giving higher resolution images with larger fields of view.
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Chan J, Zheng Z, Bell K, Le M, Reza PH, Yeow JTW. Photoacoustic Imaging with Capacitive Micromachined Ultrasound Transducers: Principles and Developments. SENSORS (BASEL, SWITZERLAND) 2019; 19:E3617. [PMID: 31434241 PMCID: PMC6720758 DOI: 10.3390/s19163617] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 08/15/2019] [Accepted: 08/18/2019] [Indexed: 12/14/2022]
Abstract
Photoacoustic imaging (PAI) is an emerging imaging technique that bridges the gap between pure optical and acoustic techniques to provide images with optical contrast at the acoustic penetration depth. The two key components that have allowed PAI to attain high-resolution images at deeper penetration depths are the photoacoustic signal generator, which is typically implemented as a pulsed laser and the detector to receive the generated acoustic signals. Many types of acoustic sensors have been explored as a detector for the PAI including Fabry-Perot interferometers (FPIs), micro ring resonators (MRRs), piezoelectric transducers, and capacitive micromachined ultrasound transducers (CMUTs). The fabrication technique of CMUTs has given it an edge over the other detectors. First, CMUTs can be easily fabricated into given shapes and sizes to fit the design specifications. Moreover, they can be made into an array to increase the imaging speed and reduce motion artifacts. With a fabrication technique that is similar to complementary metal-oxide-semiconductor (CMOS), CMUTs can be integrated with electronics to reduce the parasitic capacitance and improve the signal to noise ratio. The numerous benefits of CMUTs have enticed researchers to develop it for various PAI purposes such as photoacoustic computed tomography (PACT) and photoacoustic endoscopy applications. For PACT applications, the main areas of research are in designing two-dimensional array, transparent, and multi-frequency CMUTs. Moving from the table top approach to endoscopes, some of the different configurations that are being investigated are phased and ring arrays. In this paper, an overview of the development of CMUTs for PAI is presented.
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