1
|
Wang Z, Tao W, Zhao H. Extractor-attention-predictor network for quantitative photoacoustic tomography. PHOTOACOUSTICS 2024; 38:100609. [PMID: 38745884 PMCID: PMC11091525 DOI: 10.1016/j.pacs.2024.100609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 04/18/2024] [Accepted: 04/20/2024] [Indexed: 05/16/2024]
Abstract
Quantitative photoacoustic tomography (qPAT) holds great potential in estimating chromophore concentrations, whereas the involved optical inverse problem, aiming to recover absorption coefficient distributions from photoacoustic images, remains challenging. To address this problem, we propose an extractor-attention-predictor network architecture (EAPNet), which employs a contracting-expanding structure to capture contextual information alongside a multilayer perceptron to enhance nonlinear modeling capability. A spatial attention module is introduced to facilitate the utilization of important information. We also use a balanced loss function to prevent network parameter updates from being biased towards specific regions. Our method obtains satisfactory quantitative metrics in simulated and real-world validations. Moreover, it demonstrates superior robustness to target properties and yields reliable results for targets with small size, deep location, or relatively low absorption intensity, indicating its broader applicability. The EAPNet, compared to the conventional UNet, exhibits improved efficiency, which significantly enhances performance while maintaining similar network size and computational complexity.
Collapse
Affiliation(s)
- Zeqi Wang
- School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Wei Tao
- School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Hui Zhao
- School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| |
Collapse
|
2
|
Amendola C, Maffeis G, Farina A, Spinelli L, Torricelli A, Pifferi A, Sassaroli A, Fanelli D, Tommasi F, Martelli F. Application limits of the scaling relations for Monte Carlo simulations in diffuse optics. Part 1: theory. OPTICS EXPRESS 2024; 32:125-150. [PMID: 38175044 DOI: 10.1364/oe.507646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024]
Abstract
Monte Carlo (MC) is a powerful tool to study photon migration in scattering media, yet quite time-consuming to solve inverse problems. To speed up MC-simulations, scaling relations can be applied to an existing initial MC-simulation to generate a new data-set with different optical properties. We named this approach trajectory-based since it uses the knowledge of the detected photon trajectories of the initial MC-simulation, in opposition to the slower photon-based approach, where a novel MC-simulation is rerun with new optical properties. We investigated the convergence and applicability limits of the scaling relations, both related to the likelihood that the sample of trajectories considered is representative also for the new optical properties. For absorption, the scaling relation contains smoothly converging Lambert-Beer factors, whereas for scattering it is the product of two quickly diverging factors, whose ratio, for NIRS cases, can easily reach ten orders of magnitude. We investigated such instability by studying the probability-distribution for the number of scattering events in trajectories of given length. We propose a convergence test of the scattering scaling relation based on the minimum-maximum number of scattering events in recorded trajectories. We also studied the dependence of MC-simulations on optical properties, most critical in inverse problems, finding that scattering derivatives are ascribed to small deviations in the distribution of scattering events from a Poisson distribution. This paper, which can also serve as a tutorial, helps to understand the physics of the scaling relations with the causes of their limitations and devise new strategies to deal with them.
Collapse
|
3
|
Yang X, Xu G, Liu X, Zhou G, Zhang B, Wang F, Wang L, Li B, Li L. Carbon nanomaterial-involved EMT and CSC in cancer. REVIEWS ON ENVIRONMENTAL HEALTH 2023; 38:1-13. [PMID: 34619029 DOI: 10.1515/reveh-2021-0082] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 09/26/2021] [Indexed: 06/13/2023]
Abstract
Carbon nanomaterials (CNMs) are ubiquitous in our daily lives because of the outstanding physicochemical properties. CNMs play curial parts in industrial and medical fields, however, the risks of CNMs exposure to human health are still not fully understood. In view of, it is becoming extremely difficult to ignore the existence of the toxicity of CNMs. With the increasing exploitation of CNMs, it's necessary to evaluate the potential impact of these materials on human health. In recent years, more and more researches have shown that CNMs are contributed to the cancer formation and metastasis after long-term exposure through epithelial-mesenchymal transition (EMT) and cancer stem cells (CSCs) which is associated with cancer progression and invasion. This review discusses CNMs properties and applications in industrial and medical fields, adverse effects on human health, especially the induction of tumor initiation and metastasis through EMT and CSCs procedure.
Collapse
Affiliation(s)
- Xiaotong Yang
- Tianjin Medical University General Hospital, Tianjin, China
| | - Gongquan Xu
- Tianjin Medical University General Hospital, Tianjin, China
| | - Xiaolong Liu
- Tianjin Medical University General Hospital, Tianjin, China
| | - Guiming Zhou
- Tianjin Medical University General Hospital, Tianjin, China
| | - Bing Zhang
- Rushan Hospital of Traditional Chinese Medicine, Weihai, China
| | - Fan Wang
- Tianjin Medical University General Hospital, Tianjin, China
| | - Lingjuan Wang
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Hubei, China
| | - Bin Li
- Tianjin Medical University General Hospital, Tianjin, China
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
| | - Liming Li
- Tianjin Medical University General Hospital, Tianjin, China
| |
Collapse
|
4
|
Zheng S, Yingsa H, Meichen S, Qi M. Quantitative photoacoustic tomography with light fluence compensation based on radiance Monte Carlo model. Phys Med Biol 2023; 68. [PMID: 36821863 DOI: 10.1088/1361-6560/acbe90] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 02/23/2023] [Indexed: 02/25/2023]
Abstract
Objective. Photoacoustic tomography (PAT) is a rapidly evolving imaging modality that provides images with high contrast and spatial resolution showing the optical properties of biological tissues. The photoacoustic pressure is proportional to the product of the optical absorption coefficient and the local light fluence. The essential challenge in reconstructing quantitative images representing spatially varying absorption coefficients is the unknown light fluence. In addition, optical attenuation induces spatial variations in the light fluence, and the heterogeneity of the fluence determines the limits of reconstruction quality and depth.Approach.In this work, a reconstruction enhancement scheme is proposed to compensate for the variation in the light fluence in the absorption coefficient recovery. The inverse problem of the radiance Monte Carlo model describing light transport through the tissue is solved by using an alternating optimization strategy. In the iteration, the absorption coefficients and photon weights are alternately updated.Main results.The method provides highly accurate quantitative images of absorption coefficients in simulations, phantoms, andin vivostudies. The results show that the method has great potential for improving the accuracy of absorption coefficient recovery compared to conventional reconstruction methods that ignore light fluence variations. Comparison with state-of-the-art fluence compensation methods shows significant improvements in root mean square error, normalized mean square absolute distance, and structural similarity metrics.Significance.This method achieves high precision quantitative imaging by compensating for nonuniform light fluence without increasing the complexity and operation of the imaging system.
Collapse
Affiliation(s)
- Sun Zheng
- Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, Hebei, People's Republic of China
- Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding 071003, Hebei, People's Republic of China
| | - Hou Yingsa
- Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, Hebei, People's Republic of China
| | - Sun Meichen
- Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, Hebei, People's Republic of China
| | - Meng Qi
- Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, Hebei, People's Republic of China
| |
Collapse
|
5
|
Zuo H, Cui M, Wang X, Ma C. Spectral crosstalk in photoacoustic computed tomography. PHOTOACOUSTICS 2022; 26:100356. [PMID: 35574185 PMCID: PMC9095891 DOI: 10.1016/j.pacs.2022.100356] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 04/04/2022] [Accepted: 04/11/2022] [Indexed: 06/15/2023]
Abstract
Multispectral photoacoustic (PA) imaging faces two major challenges: the spectral coloring effect, which has been studied extensively as an optical inversion problem, and the spectral crosstalk, which is basically a result of non-ideal acoustic inversion. So far, there is no systematic work to analyze the spectral crosstalk because acoustic inversion and spectroscopic measurement are always treated as decoupled. In this work, we theorize and demonstrate through a series of simulations and experiments how imperfect acoustic inversion induces inaccurate PA spectrum measurement. We provide detailed analysis to elucidate how different factors, including limited bandwidth, limited view, light attenuation, out-of-plane signal, and image reconstruction schemes, conspire to render the measured PA spectrum inaccurate. We found that the model-based reconstruction outperforms universal back-projection in suppressing the spectral crosstalk in some cases.
Collapse
Affiliation(s)
- Hongzhi Zuo
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
| | - Manxiu Cui
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
| | - Xuanhao Wang
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
| | - Cheng Ma
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
- Center for Clinical Big Data Research, Institute of Precision Medicine, Tsinghua University, Beijing 100084, China
- Photomedicine Laboratory, Institute of Precision Medicine, Tsinghua University, Beijing 100084, China
| |
Collapse
|
6
|
Hänninen N, Pulkkinen A, Arridge S, Tarvainen T. Adaptive stochastic Gauss-Newton method with optical Monte Carlo for quantitative photoacoustic tomography. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:083013. [PMID: 35396833 PMCID: PMC8993421 DOI: 10.1117/1.jbo.27.8.083013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 03/18/2022] [Indexed: 06/14/2023]
Abstract
SIGNIFICANCE The image reconstruction problem in quantitative photoacoustic tomography (QPAT) is an ill-posed inverse problem. Monte Carlo method for light transport can be utilized in solving this image reconstruction problem. AIM The aim was to develop an adaptive image reconstruction method where the number of photon packets in Monte Carlo simulation is varied to achieve a sufficient accuracy with reduced computational burden. APPROACH The image reconstruction problem was formulated as a minimization problem. An adaptive stochastic Gauss-Newton (A-SGN) method combined with Monte Carlo method for light transport was developed. In the algorithm, the number of photon packets used on Gauss-Newton (GN) iteration was varied utilizing a so-called norm test. RESULTS The approach was evaluated with numerical simulations. With the proposed approach, the number of photon packets needed for solving the inverse problem was significantly smaller than in a conventional approach where the number of photon packets was fixed for each GN iteration. CONCLUSIONS The A-SGN method with a norm test can be utilized in QPAT to provide accurate and computationally efficient solutions.
Collapse
Affiliation(s)
- Niko Hänninen
- University of Eastern Finland, Department of Applied Physics, Kuopio, Finland
| | - Aki Pulkkinen
- University of Eastern Finland, Department of Applied Physics, Kuopio, Finland
| | - Simon Arridge
- University College London, Department of Computer Science, London, United Kingdom
| | - Tanja Tarvainen
- University of Eastern Finland, Department of Applied Physics, Kuopio, Finland
- University College London, Department of Computer Science, London, United Kingdom
| |
Collapse
|
7
|
Jafari CZ, Mihelic SA, Engelmann S, Dunn AK. High-resolution three-dimensional blood flow tomography in the subdiffuse regime using laser speckle contrast imaging. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-210364SSR. [PMID: 35362273 PMCID: PMC8968074 DOI: 10.1117/1.jbo.27.8.083011] [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: 11/15/2021] [Accepted: 03/04/2022] [Indexed: 06/14/2023]
Abstract
SIGNIFICANCE Visualizing high-resolution hemodynamics in cerebral tissue over a large field of view (FOV), provides important information in studying disease states affecting the brain. Current state-of-the-art optical blood flow imaging techniques either lack spatial resolution or are too slow to provide high temporal resolution reconstruction of flow map over a large FOV. AIM We present a high spatial resolution computational optical imaging technique based on principles of laser speckle contrast imaging (LSCI) for reconstructing the blood flow maps in complex tissue over a large FOV provided that the three-dimensional (3D) vascular structure is known or assumed. APPROACH Our proposed method uses a perturbation Monte Carlo simulation of the high-resolution 3D geometry for both accurately deriving the speckle contrast forward model and calculating the Jacobian matrix used in our reconstruction algorithm to achieve high resolution. Given the convex nature of our highly nonlinear problem, we implemented a mini-batch gradient descent with an adaptive learning rate optimization method to iteratively reconstruct the blood flow map. Specifically, we implemented advanced optimization techniques combined with efficient parallelization and vectorization of the forward and derivative calculations to make reconstruction of the blood flow map feasible with reconstruction times on the order of tens of minutes. RESULTS We tested our reconstruction algorithm through simulation of both a flow phantom model as well as an anatomically correct murine cerebral tissue and vasculature captured via two-photon microscopy. Additionally, we performed a noise study, examining the robustness of our inverse model in presence of 0.1% and 1% additive noise. In all cases, the blood flow reconstruction error was <2 % for most of the vasculature, except for the peripheral vasculature which suffered from insufficient photon sampling. Descending vasculature and deeper structures showed slightly higher sensitivity to noise compared with vasculature with a horizontal orientation at the more superficial layers. Our results show high-resolution reconstruction of the blood flow map in tissue down to 500 μm and beyond. CONCLUSIONS We have demonstrated a high-resolution computational imaging technique for visualizing blood flow map in complex tissue over a large FOV. Once a high-resolution structural image is captured, our reconstruction algorithm only requires a few LSCI images captured through a camera to reconstruct the blood flow map computationally at a high resolution. We note that the combination of high temporal and spatial resolution of our reconstruction algorithm makes the solution well-suited for applications involving fast monitoring of flow dynamics over a large FOV, such as in functional neural imaging.
Collapse
Affiliation(s)
- Chakameh Z. Jafari
- The University of Texas at Austin, Department of Electrical and Computer Engineering, Austin, Texas, United States
| | - Samuel A. Mihelic
- The University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas, United States
| | - Shaun Engelmann
- The University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas, United States
| | - Andrew K. Dunn
- The University of Texas at Austin, Department of Electrical and Computer Engineering, Austin, Texas, United States
- The University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas, United States
| |
Collapse
|
8
|
Sun Z, Sun H. Image reconstruction for endoscopic photoacoustic tomography including effects of detector responses. Exp Biol Med (Maywood) 2022; 247:881-897. [PMID: 35232296 DOI: 10.1177/15353702221079570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
In photoacoustic tomography (PAT), conventional image reconstruction methods are generally based on the assumption of an ideal point-like ultrasonic detector. This assumption is appropriate when the receiving surface of the detector is sufficiently small and/or the distance between the imaged object and the detector is large enough. However, it does not hold in endoscopic applications of PAT. In this study, we propose a model-based image reconstruction method for endoscopic photoacoustic tomography (EPAT), considering the effect of detector responses on image quality. We construct a forward model to physically describe the imaging process of EPAT, including the generation of the initial pressure due to optical absorption and thermoelastic expansion, the propagation of photoacoustic waves in tissues, and the acoustic measurement. The model outputs the theoretical sampling voltage signal, which is the response of the ultrasonic detector to the acoustic pressure reaching its receiving surface. The images representing the distribution map of the optical absorption energy density on cross-sections of the imaged luminal structures are reconstructed from the sampling voltage signals output by the detector through iterative inversion of the forward model. Compared with the conventional approaches based on back-projection and other imaging models, our method improved the quality and spatial resolution of the resulting images.
Collapse
Affiliation(s)
- Zheng Sun
- Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, China.,Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding 071003, China
| | - Huifeng Sun
- Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, China.,Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding 071003, China
| |
Collapse
|
9
|
Macdonald CM, Arridge S, Powell S. Efficient inversion strategies for estimating optical properties with Monte Carlo radiative transport models. JOURNAL OF BIOMEDICAL OPTICS 2020; 25:JBO-200101R. [PMID: 32798354 PMCID: PMC7426481 DOI: 10.1117/1.jbo.25.8.085002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 07/23/2020] [Indexed: 06/11/2023]
Abstract
SIGNIFICANCE Indirect imaging problems in biomedical optics generally require repeated evaluation of forward models of radiative transport, for which Monte Carlo is accurate yet computationally costly. We develop an approach to reduce this bottleneck, which has significant implications for quantitative tomographic imaging in a variety of medical and industrial applications. AIM Our aim is to enable computationally efficient image reconstruction in (hybrid) diffuse optical modalities using stochastic forward models. APPROACH Using Monte Carlo, we compute a fully stochastic gradient of an objective function for a given imaging problem. Leveraging techniques from the machine learning community, we then adaptively control the accuracy of this gradient throughout the iterative inversion scheme to substantially reduce computational resources at each step. RESULTS For example problems of quantitative photoacoustic tomography and ultrasound-modulated optical tomography, we demonstrate that solutions are attainable using a total computational expense that is comparable to (or less than) that which is required for a single high-accuracy forward run of the same Monte Carlo model. CONCLUSIONS This approach demonstrates significant computational savings when approaching the full nonlinear inverse problem of optical property estimation using stochastic methods.
Collapse
Affiliation(s)
- Callum M. Macdonald
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Simon Arridge
- University College London, Department of Computer Science, London, United Kingdom
| | - Samuel Powell
- University of Nottingham, Faculty of Engineering, Nottingham, United Kingdom
| |
Collapse
|