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Cam RM, Wang C, Thompson W, Ermilov SA, Anastasio MA, Villa U. Spatiotemporal image reconstruction to enable high-frame-rate dynamic photoacoustic tomography with rotating-gantry volumetric imagers. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:S11516. [PMID: 38249994 PMCID: PMC10798269 DOI: 10.1117/1.jbo.29.s1.s11516] [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: 09/30/2023] [Revised: 11/22/2023] [Accepted: 12/20/2023] [Indexed: 01/23/2024]
Abstract
Significance Dynamic photoacoustic computed tomography (PACT) is a valuable imaging technique for monitoring physiological processes. However, current dynamic PACT imaging techniques are often limited to two-dimensional spatial imaging. Although volumetric PACT imagers are commercially available, these systems typically employ a rotating measurement gantry in which the tomographic data are sequentially acquired as opposed to being acquired simultaneously at all views. Because the dynamic object varies during the data-acquisition process, the sequential data-acquisition process poses substantial challenges to image reconstruction associated with data incompleteness. The proposed image reconstruction method is highly significant in that it will address these challenges and enable volumetric dynamic PACT imaging with existing preclinical imagers. Aim The aim of this study is to develop a spatiotemporal image reconstruction (STIR) method for dynamic PACT that can be applied to commercially available volumetric PACT imagers that employ a sequential scanning strategy. The proposed reconstruction method aims to overcome the challenges caused by the limited number of tomographic measurements acquired per frame. Approach A low-rank matrix estimation-based STIR (LRME-STIR) method is proposed to enable dynamic volumetric PACT. The LRME-STIR method leverages the spatiotemporal redundancies in the dynamic object to accurately reconstruct a four-dimensional (4D) spatiotemporal image. Results The conducted numerical studies substantiate the LRME-STIR method's efficacy in reconstructing 4D dynamic images from tomographic measurements acquired with a rotating measurement gantry. The experimental study demonstrates the method's ability to faithfully recover the flow of a contrast agent with a frame rate of 10 frames per second, even when only a single tomographic measurement per frame is available. Conclusions The proposed LRME-STIR method offers a promising solution to the challenges faced by enabling 4D dynamic imaging using commercially available volumetric PACT imagers. By enabling accurate STIRs, this method has the potential to significantly advance preclinical research and facilitate the monitoring of critical physiological biomarkers.
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Affiliation(s)
- Refik Mert Cam
- University of Illinois Urbana-Champaign, Department of Electrical and Computer Engineering, Urbana, Illinois, United States
| | - Chao Wang
- National University of Singapore, Department of Statistics and Data Science, Singapore
| | | | | | - Mark A. Anastasio
- University of Illinois Urbana-Champaign, Department of Bioengineering, Urbana, Illinois, United States
| | - Umberto Villa
- The University of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, Texas, United States
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Zhang Z, Jin H, Zhang W, Lu W, Zheng Z, Sharma A, Pramanik M, Zheng Y. Adaptive enhancement of acoustic resolution photoacoustic microscopy imaging via deep CNN prior. PHOTOACOUSTICS 2023; 30:100484. [PMID: 37095888 PMCID: PMC10121479 DOI: 10.1016/j.pacs.2023.100484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 03/29/2023] [Indexed: 05/03/2023]
Abstract
Acoustic resolution photoacoustic microscopy (AR-PAM) is a promising medical imaging modality that can be employed for deep bio-tissue imaging. However, its relatively low imaging resolution has greatly hindered its wide applications. Previous model-based or learning-based PAM enhancement algorithms either require design of complex handcrafted prior to achieve good performance or lack the interpretability and flexibility that can adapt to different degradation models. However, the degradation model of AR-PAM imaging is subject to both imaging depth and center frequency of ultrasound transducer, which varies in different imaging conditions and cannot be handled by a single neural network model. To address this limitation, an algorithm integrating both learning-based and model-based method is proposed here so that a single framework can deal with various distortion functions adaptively. The vasculature image statistics is implicitly learned by a deep convolutional neural network, which served as plug and play (PnP) prior. The trained network can be directly plugged into the model-based optimization framework for iterative AR-PAM image enhancement, which fitted for different degradation mechanisms. Based on physical model, the point spread function (PSF) kernels for various AR-PAM imaging situations are derived and used for the enhancement of simulation and in vivo AR-PAM images, which collectively proved the effectiveness of proposed method. Quantitatively, the PSNR and SSIM values have all achieve best performance with the proposed algorithm in all three simulation scenarios; The SNR and CNR values have also significantly raised from 6.34 and 5.79 to 35.37 and 29.66 respectively in an in vivo testing result with the proposed algorithm.
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Affiliation(s)
- Zhengyuan Zhang
- Nanyang Technological University, School of Electrical and Electronic Engineering, 639798, Singapore
| | - Haoran Jin
- Zhejiang University, College of Mechanical Engineering, The State Key Laboratory of Fluid Power and Mechatronic Systems, Hangzhou 310027, China
| | - Wenwen Zhang
- Nanyang Technological University, School of Electrical and Electronic Engineering, 639798, Singapore
| | - Wenhao Lu
- Nanyang Technological University, School of Electrical and Electronic Engineering, 639798, Singapore
| | - Zesheng Zheng
- Nanyang Technological University, School of Electrical and Electronic Engineering, 639798, Singapore
| | - Arunima Sharma
- Johns Hopkins University, Electrical and Computer Engineering, Baltimore, MD 21218, USA
| | - Manojit Pramanik
- Iowa State University, Department of Electrical and Computer Engineering, Ames, Iowa, USA
| | - Yuanjin Zheng
- Nanyang Technological University, School of Electrical and Electronic Engineering, 639798, Singapore
- Corresponding author.
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Shi M, Vercauteren T, Xia W. Spatiotemporal singular value decomposition for denoising in photoacoustic imaging with a low-energy excitation light source. BIOMEDICAL OPTICS EXPRESS 2022; 13:6416-6430. [PMID: 36589568 PMCID: PMC9774869 DOI: 10.1364/boe.471198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 08/31/2022] [Accepted: 09/01/2022] [Indexed: 05/12/2023]
Abstract
Photoacoustic (PA) imaging is an emerging hybrid imaging modality that combines rich optical spectroscopic contrast and high ultrasonic resolution, and thus holds tremendous promise for a wide range of pre-clinical and clinical applications. Compact and affordable light sources such as light-emitting diodes (LEDs) and laser diodes (LDs) are promising alternatives to bulky and expensive solid-state laser systems that are commonly used as PA light sources. These could accelerate the clinical translation of PA technology. However, PA signals generated with these light sources are readily degraded by noise due to the low optical fluence, leading to decreased signal-to-noise ratio (SNR) in PA images. In this work, a spatiotemporal singular value decomposition (SVD) based PA denoising method was investigated for these light sources that usually have low fluence and high repetition rates. The proposed method leverages both spatial and temporal correlations between radiofrequency (RF) data frames. Validation was performed on simulations and in vivo PA data acquired from human fingers (2D) and forearm (3D) using a LED-based system. Spatiotemporal SVD greatly enhanced the PA signals of blood vessels corrupted by noise while preserving a high temporal resolution to slow motions, improving the SNR of in vivo PA images by 90.3%, 56.0%, and 187.4% compared to single frame-based wavelet denoising, averaging across 200 frames, and single frame without denoising, respectively. With a fast processing time of SVD (∼50 µs per frame), the proposed method is well suited to PA imaging systems with low-energy excitation light sources for real-time in vivo applications.
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4
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Al Mukaddim R, Weichmann AM, Mitchell CC, Varghese T. Enhancement of in vivo cardiac photoacoustic signal specificity using spatiotemporal singular value decomposition. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-210037RR. [PMID: 33876591 PMCID: PMC8054608 DOI: 10.1117/1.jbo.26.4.046001] [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: 01/31/2021] [Accepted: 03/29/2021] [Indexed: 05/07/2023]
Abstract
SIGNIFICANCE Photoacoustic imaging (PAI) can be used to infer molecular information about myocardial health non-invasively in vivo using optical excitation at ultrasonic spatial resolution. For clinical and preclinical linear array imaging systems, conventional delay-and-sum (DAS) beamforming is typically used. However, DAS cardiac PA images are prone to artifacts such as diffuse quasi-static clutter with temporally varying noise-reducing myocardial signal specificity. Typically, multiple frame averaging schemes are utilized to improve the quality of cardiac PAI, which affects the spatial and temporal resolution and reduces sensitivity to subtle PA signal variation. Furthermore, frame averaging might corrupt myocardial oxygen saturation quantification due to the presence of natural cardiac wall motion. In this paper, a spatiotemporal singular value decomposition (SVD) processing algorithm is proposed to reduce DAS PAI artifacts and subsequent enhancement of myocardial signal specificity. AIM Demonstrate enhancement of PA signals from myocardial tissue compared to surrounding tissues and blood inside the left-ventricular (LV) chamber using spatiotemporal SVD processing with electrocardiogram (ECG) and respiratory signal (ECG-R) gated in vivo murine cardiac PAI. APPROACH In vivo murine cardiac PAI was performed by collecting single wavelength (850 nm) photoacoustic channel data on eight healthy mice. A three-dimensional (3D) volume of complex PAI data over a cardiac cycle was reconstructed using a custom ECG-R gating algorithm and DAS beamforming. Spatiotemporal SVD was applied on a two-dimensional Casorati matrix generated using the 3D volume of PAI data. The singular value spectrum (SVS) was then filtered to remove contributions from diffuse quasi-static clutter and random noise. Finally, SVD processed beamformed images were derived using filtered SVS and inverse SVD computations. RESULTS Qualitative comparison with DAS and minimum variance (MV) beamforming shows that SVD processed images had better myocardial signal specificity, contrast, and target detectability. DAS, MV, and SVD images were quantitatively evaluated by calculating contrast ratio (CR), generalized contrast-to-noise ratio (gCNR), and signal-to-noise ratio (SNR). Quantitative evaluations were done at three cardiac time points (during systole, at end-systole (ES), and during diastole) identified from co-registered ultrasound M-Mode image. Mean CR, gCNR, and SNR values of SVD images at ES were 245, 115.15, and 258.17 times higher than DAS images with statistical significance evaluated with one-way analysis of variance. CONCLUSIONS Our results suggest that significantly better-quality images can be realized using spatiotemporal SVD processing for in vivo murine cardiac PAI.
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Affiliation(s)
- Rashid Al Mukaddim
- University of Wisconsin–Madison, Department of ECE, Madison, Wisconsin, United States
- University of Wisconsin–Madison, School of Medicine and Public Health, Department of Medical Physics, Madison, Wisconsin, United States
- Address all correspondence to Rashid Al Mukaddim,
| | - Ashley M. Weichmann
- Small Animal Imaging and Radiotherapy Facility, UW Carbone Cancer Center, Wisconsin, United States
| | - Carol C. Mitchell
- University of Wisconsin School of Medicine and Public Health, Department of Medicine/Division of Cardiovascular Medicine, Madison, Wisconsin, United States
| | - Tomy Varghese
- University of Wisconsin–Madison, Department of ECE, Madison, Wisconsin, United States
- University of Wisconsin–Madison, School of Medicine and Public Health, Department of Medical Physics, Madison, Wisconsin, United States
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Poudel J, Lou Y, Anastasio MA. A survey of computational frameworks for solving the acoustic inverse problem in three-dimensional photoacoustic computed tomography. Phys Med Biol 2019; 64:14TR01. [PMID: 31067527 DOI: 10.1088/1361-6560/ab2017] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Photoacoustic computed tomography (PACT), also known as optoacoustic tomography, is an emerging imaging technique that holds great promise for biomedical imaging. PACT is a hybrid imaging method that can exploit the strong endogenous contrast of optical methods along with the high spatial resolution of ultrasound methods. In its canonical form that is addressed in this article, PACT seeks to estimate the photoacoustically-induced initial pressure distribution within the object. Image reconstruction methods are employed to solve the acoustic inverse problem associated with the image formation process. When an idealized imaging scenario is considered, analytic solutions to the PACT inverse problem are available; however, in practice, numerous challenges exist that are more readily addressed within an optimization-based, or iterative, image reconstruction framework. In this article, the PACT image reconstruction problem is reviewed within the context of modern optimization-based image reconstruction methodologies. Imaging models that relate the measured photoacoustic wavefields to the sought-after object function are described in their continuous and discrete forms. The basic principles of optimization-based image reconstruction from discrete PACT measurement data are presented, which includes a review of methods for modeling the PACT measurement system response and other important physical factors. Non-conventional formulations of the PACT image reconstruction problem, in which acoustic parameters of the medium are concurrently estimated along with the PACT image, are also introduced and reviewed.
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Affiliation(s)
- Joemini Poudel
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, United States of America
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Abbasi A, Monadjemi A, Fang L, Rabbani H. Optical coherence tomography retinal image reconstruction via nonlocal weighted sparse representation. JOURNAL OF BIOMEDICAL OPTICS 2018; 23:1-11. [PMID: 29575829 DOI: 10.1117/1.jbo.23.3.036011] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Accepted: 03/06/2018] [Indexed: 06/08/2023]
Abstract
We present a nonlocal weighted sparse representation (NWSR) method for reconstruction of retinal optical coherence tomography (OCT) images. To reconstruct a high signal-to-noise ratio and high-resolution OCT images, utilization of efficient denoising and interpolation algorithms are necessary, especially when the original data were subsampled during acquisition. However, the OCT images suffer from the presence of a high level of noise, which makes the estimation of sparse representations a difficult task. Thus, the proposed NWSR method merges sparse representations of multiple similar noisy and denoised patches to better estimate a sparse representation for each patch. First, the sparse representation of each patch is independently computed over an overcomplete dictionary, and then a nonlocal weighted sparse coefficient is computed by averaging representations of similar patches. Since the sparsity can reveal relevant information from noisy patches, combining noisy and denoised patches' representations is beneficial to obtain a more robust estimate of the unknown sparse representation. The denoised patches are obtained by applying an off-the-shelf image denoising method and our method provides an efficient way to exploit information from noisy and denoised patches' representations. The experimental results on denoising and interpolation of spectral domain OCT images demonstrated the effectiveness of the proposed NWSR method over existing state-of-the-art methods.
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Affiliation(s)
- Ashkan Abbasi
- University of Isfahan, Department of Artificial Intelligence, Faculty of Computer Engineering, Isfah, Iran
| | - Amirhassan Monadjemi
- University of Isfahan, Department of Artificial Intelligence, Faculty of Computer Engineering, Isfah, Iran
| | - Leyuan Fang
- Hunan University, College of Electrical and Information Engineering, Changsha, China
| | - Hossein Rabbani
- Isfahan University of Medical Sciences, School of Advanced Technologies in Medicine, Medical Image a, Iran
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7
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Ding L, Dean-Ben XL, Razansky D. Real-Time Model-Based Inversion in Cross-Sectional Optoacoustic Tomography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:1883-1891. [PMID: 26955023 DOI: 10.1109/tmi.2016.2536779] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Analytical (closed-form) inversion schemes have been the standard approach for image reconstruction in optoacoustic tomography due to their fast reconstruction abilities and low memory requirements. Yet, the need for quantitative imaging and artifact reduction has led to the development of more accurate inversion approaches, which rely on accurate forward modeling of the optoacoustic wave generation and propagation. In this way, multiple experimental factors can be incorporated, such as the exact detection geometry, spatio-temporal response of the transducers, and acoustic heterogeneities. The model-based inversion commonly results in very large sparse matrix formulations that require computationally extensive and memory demanding regularization schemes for image reconstruction, hindering their effective implementation in real-time imaging applications. Herein, we introduce a new discretization procedure for efficient model-based reconstructions in two-dimensional optoacoustic tomography that allows for parallel implementation on a graphics processing unit (GPU) with a relatively low numerical complexity. By on-the-fly calculation of the model matrix in each iteration of the inversion procedure, the new approach results in imaging frame rates exceeding 10 Hz, thus enabling real-time image rendering using the model-based approach.
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8
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Meng J, Jiang Z, Wang LV, Park J, Kim C, Sun M, Zhang Y, Song L. High-speed, sparse-sampling three-dimensional photoacoustic computed tomography in vivo based on principal component analysis. JOURNAL OF BIOMEDICAL OPTICS 2016; 21:76007. [PMID: 27424604 DOI: 10.1117/1.jbo.21.7.076007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Accepted: 07/01/2016] [Indexed: 05/18/2023]
Abstract
Photoacoustic computed tomography (PACT) has emerged as a unique and promising technology for multiscale biomedical imaging. To fully realize its potential for various preclinical and clinical applications, development of systems with high imaging speed, reasonable cost, and manageable data flow are needed. Sparse-sampling PACT with advanced reconstruction algorithms, such as compressed-sensing reconstruction, has shown potential as a solution to this challenge. However, most such algorithms require iterative reconstruction and thus intense computation, which may lead to excessively long image reconstruction times. Here, we developed a principal component analysis (PCA)-based PACT (PCA-PACT) that can rapidly reconstruct high-quality, three-dimensional (3-D) PACT images with sparsely sampled data without requiring an iterative process. In vivo images of the vasculature of a human hand were obtained, thus validating the PCA-PACT method. The results showed that, compared with the back-projection (BP) method, PCA-PACT required ∼50% fewer measurements and ∼40% less time for image reconstruction, and the imaging quality was almost the same as that for BP with full sampling. In addition, compared with compressed sensing-based PACT, PCA-PACT had approximately sevenfold faster imaging speed with higher imaging accuracy. This work suggests a promising approach for low-cost, 3-D, rapid PACT for various biomedical applications.
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Affiliation(s)
- Jing Meng
- Qufu Normal University, School of Information Science and Engineering & Institute of Network Computing, 80 Yantai Road North, Rizhao 276826, China
| | - Zibo Jiang
- Qufu Normal University, School of Information Science and Engineering & Institute of Network Computing, 80 Yantai Road North, Rizhao 276826, China
| | - Lihong V Wang
- Washington University in St. Louis, Department of Biomedical Engineering, Optical Imaging Laboratory, One Brookings Drive, St. Louis, Missouri 63130, United States
| | - Jongin Park
- Pohang University of Science and Technology, Departments of Electrical Engineering and Creative IT Engineering, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk 790-784, Republic of Korea
| | - Chulhong Kim
- Pohang University of Science and Technology, Departments of Electrical Engineering and Creative IT Engineering, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk 790-784, Republic of Korea
| | - Mingjian Sun
- Harbin Institute of Technology, Department of Control Science and Engineering, 92 West Dazhi Street, Nan Gang District, Harbin 150001, China
| | - Yuanke Zhang
- Qufu Normal University, School of Information Science and Engineering & Institute of Network Computing, 80 Yantai Road North, Rizhao 276826, China
| | - Liang Song
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, Shenzhen 518055, China
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He H, Prakash J, Buehler A, Ntziachristos V. Optoacoustic Tomography Using Accelerated Sparse Recovery and Coherence Factor Weighting. Tomography 2016; 2:138-145. [PMID: 30042960 PMCID: PMC6024421 DOI: 10.18383/j.tom.2016.00148] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
Sparse recovery algorithms have shown great potential to accurately reconstruct images using limited-view optoacoustic (photoacoustic) tomography data sets, but these are computationally expensive. In this paper, we propose an improvement of the fast converging Split Augmented Lagrangian Shrinkage Algorithm method based on least square QR inversion for improving the reconstruction speed. We further show image fidelity improvement when using a coherence factor to weight the reconstruction result. Phantom and in vivo measurements show that the accelerated Split Augmented Lagrangian Shrinkage Algorithm method with coherence factor weighting offers images with reduced artifacts and provides faster convergence compared with existing sparse recovery algorithms.
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Affiliation(s)
- Hailong He
- Institute for Biological and Medical Imaging, Helmholtz Zentrum München, Oberschleißheim, Germany and.,Chair for Biological Imaging, Technische Universität München, München, Germany
| | - Jaya Prakash
- Institute for Biological and Medical Imaging, Helmholtz Zentrum München, Oberschleißheim, Germany and.,Chair for Biological Imaging, Technische Universität München, München, Germany
| | - Andreas Buehler
- Institute for Biological and Medical Imaging, Helmholtz Zentrum München, Oberschleißheim, Germany and.,Chair for Biological Imaging, Technische Universität München, München, Germany
| | - Vasilis Ntziachristos
- Institute for Biological and Medical Imaging, Helmholtz Zentrum München, Oberschleißheim, Germany and.,Chair for Biological Imaging, Technische Universität München, München, Germany
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10
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Wan H, Zhou Y, Ying L, Meng J, Song L, Xia J. Enabling high-speed wide-field dynamic imaging in multifocal photoacoustic computed microscopy: a simulation study. APPLIED OPTICS 2016; 55:3724-9. [PMID: 27168282 PMCID: PMC4877034 DOI: 10.1364/ao.55.003724] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Photoacoustic-computed microscopy (PACM) is an emerging technology that employs thousands of optical foci to provide wide-field high-resolution images of tissue optical absorption. A major limitation of PACM is the slow imaging speed, limiting its usage in dynamic imaging. In this study, we improved the speed through a two-step approach. First, we employed compressed sensing with partially known support to reduce the transducer element number, which subsequently improved the imaging speed at each optical scanning step. Second, we use the high-speed low-resolution image acquired without microlens array to inform dynamic changes in the high-resolution PACM image. Combining both approaches, we achieved high-resolution dynamic imaging over a wide field.
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Affiliation(s)
- Hongying Wan
- Department of Biomedical Engineering, University at Buffalo, the State University of New York, Buffalo, NY, 14260, USA
| | - Yihang Zhou
- Department of Biomedical Engineering, University at Buffalo, the State University of New York, Buffalo, NY, 14260, USA
| | - Leslie Ying
- Department of Biomedical Engineering, University at Buffalo, the State University of New York, Buffalo, NY, 14260, USA
| | - Jing Meng
- College of Information Science and Engineering, Qufu Normal University, 80 Yantai Road North, Rizhao, Shandong 276826, China
| | - Liang Song
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, Shenzhen 518055, China
| | - Jun Xia
- Department of Biomedical Engineering, University at Buffalo, the State University of New York, Buffalo, NY, 14260, USA
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Lutzweiler C, Tzoumas S, Rosenthal A, Ntziachristos V, Razansky D. High-Throughput Sparsity-Based Inversion Scheme for Optoacoustic Tomography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:674-684. [PMID: 26469127 DOI: 10.1109/tmi.2015.2490799] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The concept of sparsity is extensively exploited in the fields of data acquisition and image processing, contributing to better signal-to-noise and spatio-temporal performance of the various imaging methods. In the field of optoacoustic tomography, the image reconstruction problem is often characterized by computationally extensive inversion of very large datasets, for instance when acquiring volumetric multispectral data with high temporal resolution. In this article we seek to accelerate accurate model-based optoacoustic inversions by identifying various sources of sparsity in the forward and inverse models as well as in the single- and multi-frame representation of the projection data. These sources of sparsity are revealed through appropriate transformations in the signal, model and image domains and are subsequently exploited for expediting image reconstruction. The sparsity-based inversion scheme was tested with experimental data, offering reconstruction speed enhancement by a factor of 40 to 700 times as compared with the conventional iterative model-based inversions while preserving similar image quality. The demonstrated results pave the way for achieving real-time performance of model-based reconstruction in multi-dimensional optoacoustic imaging.
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12
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Kim J, Lee D, Jung U, Kim C. Photoacoustic imaging platforms for multimodal imaging. Ultrasonography 2015; 34:88-97. [PMID: 25754364 PMCID: PMC4372714 DOI: 10.14366/usg.14062] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2014] [Revised: 02/09/2015] [Accepted: 02/16/2015] [Indexed: 11/22/2022] Open
Abstract
Photoacoustic (PA) imaging is a hybrid biomedical imaging method that exploits both acoustical Epub ahead of print and optical properties and can provide both functional and structural information. Therefore, PA imaging can complement other imaging methods, such as ultrasound imaging, fluorescence imaging, optical coherence tomography, and multi-photon microscopy. This article reviews techniques that integrate PA with the above imaging methods and describes their applications.
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Affiliation(s)
- Jeesu Kim
- Departments of Electrical Engineering, Pohang University of Science and Technology, Pohang, Korea
| | - Donghyun Lee
- Departments of Creative IT Engineering, Pohang University of Science and Technology, Pohang, Korea
| | - Unsang Jung
- Departments of Future IT Innovation Laboratory, Pohang University of Science and Technology, Pohang, Korea
| | - Chulhong Kim
- Departments of Electrical Engineering, Pohang University of Science and Technology, Pohang, Korea ; Departments of Creative IT Engineering, Pohang University of Science and Technology, Pohang, Korea
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