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Pian Q, Alfadhel M, Tang J, Lee GV, Li B, Fu B, Ayata Y, Yaseen MA, Boas DA, Secomb TW, Sakadzic S. Cortical microvascular blood flow velocity mapping by combining dynamic light scattering optical coherence tomography and two-photon microscopy. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:076003. [PMID: 37484973 PMCID: PMC10362155 DOI: 10.1117/1.jbo.28.7.076003] [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: 01/12/2023] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 07/25/2023]
Abstract
Significance The accurate large-scale mapping of cerebral microvascular blood flow velocity is crucial for a better understanding of cerebral blood flow (CBF) regulation. Although optical imaging techniques enable both high-resolution microvascular angiography and fast absolute CBF velocity measurements in the mouse cortex, they usually require different imaging techniques with independent system configurations to maximize their performances. Consequently, it is still a challenge to accurately combine functional and morphological measurements to co-register CBF speed distribution from hundreds of microvessels with high-resolution microvascular angiograms. Aim We propose a data acquisition and processing framework to co-register a large set of microvascular blood flow velocity measurements from dynamic light scattering optical coherence tomography (DLS-OCT) with the corresponding microvascular angiogram obtained using two-photon microscopy (2PM). Approach We used DLS-OCT to first rapidly acquire a large set of microvascular velocities through a sealed cranial window in mice and then to acquire high-resolution microvascular angiograms using 2PM. The acquired data were processed in three steps: (i) 2PM angiogram coregistration with the DLS-OCT angiogram, (ii) 2PM angiogram segmentation and graphing, and (iii) mapping of the CBF velocities to the graph representation of the 2PM angiogram. Results We implemented the developed framework on the three datasets acquired from the mice cortices to facilitate the coregistration of the large sets of DLS-OCT flow velocity measurements with 2PM angiograms. We retrieved the distributions of red blood cell velocities in arterioles, venules, and capillaries as a function of the branching order from precapillary arterioles and postcapillary venules from more than 1000 microvascular segments. Conclusions The proposed framework may serve as a useful tool for quantitative analysis of large microvascular datasets obtained by OCT and 2PM in studies involving normal brain functioning, progression of various diseases, and numerical modeling of the oxygen advection and diffusion in the realistic microvascular networks.
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Affiliation(s)
- Qi Pian
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
| | - Mohammed Alfadhel
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Jianbo Tang
- Southern University of Science and Technology, Department of Biomedical Engineering, Shenzhen, China
| | - Grace V. Lee
- University of Arizona, Program in Applied Mathematics, Tucson, Arizona, United States
| | - Baoqiang Li
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
- Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology, Brain Cognition and Brain Disease Institute; Shenzhen Fundamental Research Institutions, Shenzhen–Hong Kong Institute of Brain Science, Shenzhen, Guangdong, China
| | - Buyin Fu
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
| | - Yagmur Ayata
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
| | - Mohammad Abbas Yaseen
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - David A. Boas
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Timothy W. Secomb
- University of Arizona, Program in Applied Mathematics, Tucson, Arizona, United States
- University of Arizona, Department of Mathematics, Tucson, Arizona, United States
- University of Arizona, Department of Physiology, Tucson, Arizona, United States
| | - Sava Sakadzic
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
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Drees D, Scherzinger A, Hägerling R, Kiefer F, Jiang X. Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets. BMC Bioinformatics 2021; 22:346. [PMID: 34174827 PMCID: PMC8236169 DOI: 10.1186/s12859-021-04262-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 06/11/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Recent advances in 3D imaging technologies provide novel insights to researchers and reveal finer and more detail of examined specimen, especially in the biomedical domain, but also impose huge challenges regarding scalability for automated analysis algorithms due to rapidly increasing dataset sizes. In particular, existing research towards automated vessel network analysis does not always consider memory requirements of proposed algorithms and often generates a large number of spurious branches for structures consisting of many voxels. Additionally, very often these algorithms have further restrictions such as the limitation to tree topologies or relying on the properties of specific image modalities. RESULTS We propose a scalable iterative pipeline (in terms of computational cost, required main memory and robustness) that extracts an annotated abstract graph representation from the foreground segmentation of vessel networks of arbitrary topology and vessel shape. The novel iterative refinement process is controlled by a single, dimensionless, a-priori determinable parameter. CONCLUSIONS We are able to, for the first time, analyze the topology of volumes of roughly 1 TB on commodity hardware, using the proposed pipeline. We demonstrate improved robustness in terms of surface noise, vessel shape deviation and anisotropic resolution compared to the state of the art. An implementation of the presented pipeline is publicly available in version 5.1 of the volume rendering and processing engine Voreen.
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Affiliation(s)
- Dominik Drees
- Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany
| | | | | | | | - Xiaoyi Jiang
- Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany
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Damseh R, Delafontaine-Martel P, Pouliot P, Cheriet F, Lesage F. Laplacian Flow Dynamics on Geometric Graphs for Anatomical Modeling of Cerebrovascular Networks. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:381-394. [PMID: 32986549 DOI: 10.1109/tmi.2020.3027500] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Generating computational anatomical models of cerebrovascular networks is vital for improving clinical practice and understanding brain oxygen transport. This is achieved by extracting graph-based representations based on pre-mapping of vascular structures. Recent graphing methods can provide smooth vessels trajectories and well-connected vascular topology. However, they require water-tight surface meshes as inputs. Furthermore, adding vessels radii information on their graph compartments restricts their alignment along vascular centerlines. Here, we propose a novel graphing scheme that works with relaxed input requirements and intrinsically captures vessel radii information. The proposed approach is based on deforming geometric graphs constructed within vascular boundaries. Under a laplacian optimization framework, we assign affinity weights on the initial geometry that drives its iterative contraction toward vessels centerlines. We present a mechanism to decimate graph structure at each run and a convergence criterion to stop the process. A refinement technique is then introduced to obtain final vascular models. Our implementation is available on https://github.com/Damseh/VascularGraph. We benchmarked our results with that obtained using other efficient and state-of-the-art graphing schemes, validating on both synthetic and real angiograms acquired with different imaging modalities. The experiments indicate that the proposed scheme produces the lowest geometric and topological error rates on various angiograms. Furthermore, it surpasses other techniques in providing representative models that capture all anatomical aspects of vascular structures.
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Almasi S, Pratx G. High-Resolution Radioluminescence Microscopy Image Reconstruction via Ionization Track Analysis. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2019. [DOI: 10.1109/trpms.2019.2908219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Damseh R, Pouliot P, Gagnon L, Sakadzic S, Boas D, Cheriet F, Lesage F. Automatic Graph-Based Modeling of Brain Microvessels Captured With Two-Photon Microscopy. IEEE J Biomed Health Inform 2019; 23:2551-2562. [PMID: 30507542 PMCID: PMC6546554 DOI: 10.1109/jbhi.2018.2884678] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Graph models of cerebral vasculature derived from two-photon microscopy have shown to be relevant to study brain microphysiology. Automatic graphing of these microvessels remain problematic due to the vascular network complexity and two-photon sensitivity limitations with depth. In this paper, we propose a fully automatic processing pipeline to address this issue. The modeling scheme consists of a fully-convolution neural network to segment microvessels, a three-dimensional surface model generator, and a geometry contraction algorithm to produce graphical models with a single connected component. Based on a quantitative assessment using NetMets metrics, at a tolerance of 60 μm, false negative and false positive geometric error 19 rates are 3.8% and 4.2%, respectively, whereas false nega- 20 tive and false positive topological error rates are 6.1% and 4.5%, respectively. Our qualitative evaluation confirms the efficiency of our scheme in generating useful and accurate graphical models.
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Affiliation(s)
- Rafat Damseh
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montreal, QC, Canada
| | - Philippe Pouliot
- Department of Electrical Engineering, École Polytechnique de Montréal, Montreal, QC, Canada
- Research Centre, Montreal Hearth Institute, Montreal, QC, Canada
| | - Louis Gagnon
- Physics Department, Université Laval, Quebec, QC, Canada
| | - Sava Sakadzic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - David Boas
- Neurophotonics Center, Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Farida Cheriet
- Department of Computer and Software Engineering, École Polytechnique de Montréal, Montreal, QC, Canada
| | - Frederic Lesage
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montreal, QC, Canada
- Department of Electrical Engineering, École Polytechnique de Montréal, Montreal, QC, Canada
- Research Centre, Montreal Hearth Institute, Montreal, QC, Canada
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Vigneshwaran V, Sands GB, LeGrice IJ, Smaill BH, Smith NP. Reconstruction of coronary circulation networks: A review of methods. Microcirculation 2019; 26:e12542. [DOI: 10.1111/micc.12542] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 01/25/2019] [Accepted: 02/27/2019] [Indexed: 12/12/2022]
Affiliation(s)
- Vibujithan Vigneshwaran
- Auckland Bioengineering Institute University of Auckland Auckland New Zealand
- Faculty of Engineering University of Auckland Auckland New Zealand
| | - Gregory B. Sands
- Auckland Bioengineering Institute University of Auckland Auckland New Zealand
| | - Ian J. LeGrice
- Department of Physiology University of Auckland Auckland New Zealand
| | - Bruce H. Smaill
- Auckland Bioengineering Institute University of Auckland Auckland New Zealand
| | - Nicolas P. Smith
- Auckland Bioengineering Institute University of Auckland Auckland New Zealand
- Faculty of Engineering University of Auckland Auckland New Zealand
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Bates R, Irving B, Markelc B, Kaeppler J, Brown G, Muschel RJ, Brady SM, Grau V, Schnabel JA. Segmentation of Vasculature From Fluorescently Labeled Endothelial Cells in Multi-Photon Microscopy Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1-10. [PMID: 28796613 DOI: 10.1109/tmi.2017.2725639] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Vasculature is known to be of key biological significance, especially in the study of tumors. As such, considerable effort has been focused on the automated segmentation of vasculature in medical and pre-clinical images. The majority of vascular segmentation methods focus on bloodpool labeling methods; however, particularly, in the study of tumors, it is of particular interest to be able to visualize both the perfused and the non-perfused vasculature. Imaging vasculature by highlighting the endothelium provides a way to separate the morphology of vasculature from the potentially confounding factor of perfusion. Here, we present a method for the segmentation of tumor vasculature in 3D fluorescence microscopic images using signals from the endothelial and surrounding cells. We show that our method can provide complete and semantically meaningful segmentations of complex vasculature using a supervoxel-Markov random field approach. We show that in terms of extracting meaningful segmentations of the vasculature, our method outperforms both state-of-the-art method, specific to these data, as well as more classical vasculature segmentation methods.
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Almasi S, Ben-Zvi A, Lacoste B, Gu C, Miller EL, Xu X. Joint volumetric extraction and enhancement of vasculature from low-SNR 3-D fluorescence microscopy images. PATTERN RECOGNITION 2017; 63:710-718. [PMID: 28566796 PMCID: PMC5446895 DOI: 10.1016/j.patcog.2016.09.031] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
To simultaneously overcome the challenges imposed by the nature of optical imaging characterized by a range of artifacts including space-varying signal to noise ratio (SNR), scattered light, and non-uniform illumination, we developed a novel method that segments the 3-D vasculature directly from original fluorescence microscopy images eliminating the need for employing pre- and post-processing steps such as noise removal and segmentation refinement as used with the majority of segmentation techniques. Our method comprises two initialization and constrained recovery and enhancement stages. The initialization approach is fully automated using features derived from bi-scale statistical measures and produces seed points robust to non-uniform illumination, low SNR, and local structural variations. This algorithm achieves the goal of segmentation via design of an iterative approach that extracts the structure through voting of feature vectors formed by distance, local intensity gradient, and median measures. Qualitative and quantitative analysis of the experimental results obtained from synthetic and real data prove the effcacy of this method in comparison to the state-of-the-art enhancing-segmenting methods. The algorithmic simplicity, freedom from having a priori probabilistic information about the noise, and structural definition gives this algorithm a wide potential range of applications where i.e. structural complexity significantly complicates the segmentation problem.
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Affiliation(s)
- Sepideh Almasi
- Department of Electrical and Computer Engineering, Tufts University, Medford, MA, USA
| | - Ayal Ben-Zvi
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Department of Developmental Biology and Cancer Research, Institute for Medical Research IMRIC, Hebrew University of Jerusalem, Israel
| | - Baptiste Lacoste
- Department of Cellular and Molecular Medicine, University of Ottawa Brain and Mind Research Institute, The Ottawa Hospital Research Institute, Neuroscience Program, Ottawa, ON, Canada
| | - Chenghua Gu
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Eric L. Miller
- Department of Electrical and Computer Engineering, Tufts University, Medford, MA, USA
| | - Xiaoyin Xu
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
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