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Kebiri H, Gholipour A, Lin R, Vasung L, Calixto C, Krsnik Ž, Karimi D, Bach Cuadra M. Deep learning microstructure estimation of developing brains from diffusion MRI: A newborn and fetal study. Med Image Anal 2024; 95:103186. [PMID: 38701657 DOI: 10.1016/j.media.2024.103186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 02/06/2024] [Accepted: 04/22/2024] [Indexed: 05/05/2024]
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with standard FOD computation methods, accurate estimation requires a large number of measurements that usually cannot be acquired for newborns and fetuses. We propose to overcome this limitation by using a deep learning method to map as few as six diffusion-weighted measurements to the target FOD. To train the model, we use the FODs computed using multi-shell high angular resolution measurements as target. Extensive quantitative evaluations show that the new deep learning method, using significantly fewer measurements, achieves comparable or superior results than standard methods such as Constrained Spherical Deconvolution and two state-of-the-art deep learning methods. For voxels with one and two fibers, respectively, our method shows an agreement rate in terms of the number of fibers of 77.5% and 22.2%, which is 3% and 5.4% higher than other deep learning methods, and an angular error of 10° and 20°, which is 6° and 5° lower than other deep learning methods. To determine baselines for assessing the performance of our method, we compute agreement metrics using densely sampled newborn data. Moreover, we demonstrate the generalizability of the new deep learning method across scanners, acquisition protocols, and anatomy on two clinical external datasets of newborns and fetuses. We validate fetal FODs, successfully estimated for the first time with deep learning, using post-mortem histological data. Our results show the advantage of deep learning in computing the fiber orientation density for the developing brain from in-vivo dMRI measurements that are often very limited due to constrained acquisition times. Our findings also highlight the intrinsic limitations of dMRI for probing the developing brain microstructure.
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Affiliation(s)
- Hamza Kebiri
- CIBM Center for Biomedical Imaging, Switzerland; Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland; Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Rizhong Lin
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland; Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Lana Vasung
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Camilo Calixto
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Željka Krsnik
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Davood Karimi
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Meritxell Bach Cuadra
- CIBM Center for Biomedical Imaging, Switzerland; Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
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Li J, Ai L, Yao R. NVAM-Net: deep learning networks for reconstructing high-quality fiber orientation distributions. Neuroradiology 2024:10.1007/s00234-024-03341-y. [PMID: 38563964 DOI: 10.1007/s00234-024-03341-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 03/19/2024] [Indexed: 04/04/2024]
Abstract
PURPOSE Diffusion magnetic resonance imaging (dMRI) is a widely used non-invasive method for investigating brain anatomical structures. Conventional techniques for estimating fiber orientation distribution (FOD) from dMRI data often neglect voxel-level spatial relationships, leading to ambiguous associations between target voxels and their neighbors, which, in turn, adversely impacts FOD accuracy. This study aims to address this issue by introducing a novel neural network, the neighboring voxel attention mechanism network (NVAM-Net), designed to reconstruct high-quality FOD images. METHODS The NVAM-Net leverages a Transformer architecture and incorporates two innovative attention mechanisms: voxel attention and surface attention. These mechanisms are specifically designed to capture overlooked features among neighboring voxels. The processed features are subsequently passed through two fully connected layers, further enhancing FOD estimation accuracy by separately estimating spherical harmonics (SH) coefficients of varying orders. RESULTS The experimental findings, based on the Human Connectome Project (HCP) dataset, reveal that the reconstructed super-resolution FOD images achieve results comparable to those obtained through more advanced dMRI acquisition protocols. These results underscore the NVAM-Net's robust performance in reconstructing multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD). CONCLUSION In summary, this research underscores the NVAM-Net's advantages and practical feasibility in reconstructing high-quality FOD images. It provides a reliable reference point for clinical applications in the field of diffusion magnetic resonance imaging.
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Affiliation(s)
- Jiahao Li
- School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China
| | - Lingmei Ai
- School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China.
| | - Ruoxia Yao
- School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China
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Liang Z, Arefin TM, Lee CH, Zhang J. Using mesoscopic tract-tracing data to guide the estimation of fiber orientation distributions in the mouse brain from diffusion MRI. Neuroimage 2023; 270:119999. [PMID: 36871795 PMCID: PMC10052941 DOI: 10.1016/j.neuroimage.2023.119999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/21/2023] [Accepted: 02/28/2023] [Indexed: 03/07/2023] Open
Abstract
Diffusion MRI (dMRI) tractography is the only tool for non-invasive mapping of macroscopic structural connectivity over the entire brain. Although it has been successfully used to reconstruct large white matter tracts in the human and animal brains, the sensitivity and specificity of dMRI tractography remained limited. In particular, the fiber orientation distributions (FODs) estimated from dMRI signals, key to tractography, may deviate from histologically measured fiber orientation in crossing fibers and gray matter regions. In this study, we demonstrated that a deep learning network, trained using mesoscopic tract-tracing data from the Allen Mouse Brain Connectivity Atlas, was able to improve the estimation of FODs from mouse brain dMRI data. Tractography results based on the network generated FODs showed improved specificity while maintaining sensitivity comparable to results based on FOD estimated using a conventional spherical deconvolution method. Our result is a proof-of-concept of how mesoscale tract-tracing data can guide dMRI tractography and enhance our ability to characterize brain connectivity.
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Affiliation(s)
- Zifei Liang
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 First Ave, New York, NY 10016, USA
| | - Tanzil Mahmud Arefin
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 First Ave, New York, NY 10016, USA
| | - Choong H Lee
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 First Ave, New York, NY 10016, USA
| | - Jiangyang Zhang
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 First Ave, New York, NY 10016, USA.
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Munisso MC, Saito S, Tsuge I, Morimoto N. Three-dimensional analysis of load-dependent changes in the orientation of dermal collagen fibers in human skin: A pilot study. J Mech Behav Biomed Mater 2023; 138:105585. [PMID: 36435035 DOI: 10.1016/j.jmbbm.2022.105585] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 08/29/2022] [Accepted: 11/17/2022] [Indexed: 11/23/2022]
Abstract
The availability of quantitative structural data on the orientation of collagen fibers is of crucial importance for understanding the behavior of connective tissues. These fibers can be visualized using a variety of imaging techniques, including second harmonic generation (SHG) microscopy. However, characterization of the collagen network requires the accurate extraction of parameters from imaging data. To this end, several automated processes have been developed to identify the preferred orientation of collagen fibers. Common methods include fast Fourier transforms and curvelet transforms, but these tools are mostly used to infer a single preferred orientation. The purpose of this pilot study was to develop an easy procedure for comprehensively comparing multiple human skin samples with the goal of analyzing load-dependent changes via SHG microscopy. We created a 3D model based upon 2D image stacks that provide fiber orientation data perpendicular and parallel to the plane of the epidermis. The SHG images were analyzed by CurveAlign to obtain angle histogram plots containing information about the multiple fiber orientations in each single image. Subsequently, contour plots of the angle histogram intensities were created to provide a useful visual plotting method to clearly show the anomalies in the angle histograms in all samples. Our results provided additional details on how the collagen network carries a load. In fact, analysis of SHG images indicated that increased stretch was accompanied by an increase in the alignment of fibers in the loading direction. Moreover, these images demonstrated that more than one type of preferred orientation is present. In particular, the 3D network of fibers appears to have two preferred orientations in the planes both perpendicular and parallel to the plane of the epidermis.
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Affiliation(s)
- Maria Chiara Munisso
- Department of Plastic and Reconstructive Surgery, Graduate School of Medicine and Faculty of Medicine, Kyoto University, Kyoto, Japan.
| | - Susumu Saito
- Department of Plastic and Reconstructive Surgery, Graduate School of Medicine and Faculty of Medicine, Kyoto University, Kyoto, Japan.
| | - Itaru Tsuge
- Department of Plastic and Reconstructive Surgery, Graduate School of Medicine and Faculty of Medicine, Kyoto University, Kyoto, Japan
| | - Naoki Morimoto
- Department of Plastic and Reconstructive Surgery, Graduate School of Medicine and Faculty of Medicine, Kyoto University, Kyoto, Japan
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Kang X, Yoon BC, Shah S, Adamson MM. Fixel-based analysis of the diffusion properties of the patients with brain injury and chronic health symptoms. Neurosci Res 2023:S0168-0102(23)00009-3. [PMID: 36682692 DOI: 10.1016/j.neures.2023.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/28/2022] [Accepted: 01/17/2023] [Indexed: 01/21/2023]
Abstract
The diffusion properties from diffusion tensor imaging (DTI) are sensitive to white matter (WM) abnormalities and could serve as indicators of diffuse axonal damages incurred during a traumatic brain injury (TBI). Analyses of diffusion metrics in the regions of interest (ROIs) were used to compare the differences in the 18 major fiber tracts in 46 participants, between TBI participants with (n = 17) or without (n = 16) chronic symptoms (CS) and a control group (CG, n = 13). In addition to the widely used diffusion metrics, such as fractional anisotropy (FA), mean (MD), axial (AD) and radial (RD) diffusivities, apparent fiber density (AFD), complexity (CX) and fixel number (FN) derived from Mrtrix3 software package were used to characterize WM tracts and compare between participant groups in the ROIs defined by the fixel numbers. Significant differences were found in FA, AFD, MD, RD and CX in ROIs with different FNs in the corpus callosum forceps minor, left and right inferior longitudinal fasciculus, and left and right uncinate fasciculus for both TBI groups compared to controls. Diffusion properties in ROIs with different FNs can serve as detailed biomarkers of WM abnormalities, especially for individuals with chronic TBI related symptoms.
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Zeng R, Lv J, Wang H, Zhou L, Barnett M, Calamante F, Wang C. FOD-Net: A deep learning method for fiber orientation distribution angular super resolution. Med Image Anal 2022; 79:102431. [PMID: 35397471 DOI: 10.1016/j.media.2022.102431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 03/16/2022] [Accepted: 03/21/2022] [Indexed: 10/18/2022]
Abstract
Mapping the human connectome using fiber-tracking permits the study of brain connectivity and yields new insights into neuroscience. However, reliable connectome reconstruction using diffusion magnetic resonance imaging (dMRI) data acquired by widely available clinical protocols remains challenging, thus limiting the connectome/tractography clinical applications. Here we develop fiber orientation distribution (FOD) network (FOD-Net), a deep-learning-based framework for FOD angular super-resolution. Our method enhances the angular resolution of FOD images computed from common clinical-quality dMRI data, to obtain FODs with quality comparable to those produced from advanced research scanners. Super-resolved FOD images enable superior tractography and structural connectome reconstruction from clinical protocols. The method was trained and tested with high-quality data from the Human Connectome Project (HCP) and further validated with a local clinical 3.0T scanner as well as with another public available multicenter-multiscanner dataset. Using this method, we improve the angular resolution of FOD images acquired with typical single-shell low-angular-resolution dMRI data (e.g., 32 directions, b=1000s/mm2) to approximate the quality of FODs derived from time-consuming, multi-shell high-angular-resolution dMRI research protocols. We also demonstrate tractography improvement, removing spurious connections and bridging missing connections. We further demonstrate that connectomes reconstructed by super-resolved FODs achieve comparable results to those obtained with more advanced dMRI acquisition protocols, on both HCP and clinical 3.0T data. Advances in deep-learning approaches used in FOD-Net facilitate the generation of high quality tractography/connectome analysis from existing clinical MRI environments. Our code is freely available at https://github.com/ruizengalways/FOD-Net.
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Affiliation(s)
- Rui Zeng
- School of Biomedical Engineering, The University of Sydney, Sydney 2050, Australia; Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia
| | - Jinglei Lv
- School of Biomedical Engineering, The University of Sydney, Sydney 2050, Australia; Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia
| | - He Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China; Human Phenome Institute, Fudan University, Shanghai, China
| | - Luping Zhou
- School of Computer Science, The University of Sydney, Sydney 2050, Australia
| | - Michael Barnett
- Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia; Sydney Neuroimaging Analysis Centre, Sydney 2050, Australia
| | - Fernando Calamante
- School of Biomedical Engineering, The University of Sydney, Sydney 2050, Australia; Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia; Sydney Imaging, The University of Sydney, Sydney 2050, Australia
| | - Chenyu Wang
- Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia; Sydney Neuroimaging Analysis Centre, Sydney 2050, Australia.
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Karimi D, Vasung L, Jaimes C, Machado-Rivas F, Khan S, Warfield SK, Gholipour A. A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging. Med Image Anal 2021; 72:102129. [PMID: 34182203 PMCID: PMC8320341 DOI: 10.1016/j.media.2021.102129] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 05/26/2021] [Accepted: 05/28/2021] [Indexed: 12/29/2022]
Abstract
Accurate modeling of diffusion-weighted magnetic resonance imaging measurements is necessary for accurate brain connectivity analysis. Existing methods for estimating the number and orientations of fascicles in an imaging voxel either depend on non-convex optimization techniques that are sensitive to initialization and measurement noise, or are prone to predicting spurious fascicles. In this paper, we propose a machine learning-based technique that can accurately estimate the number and orientations of fascicles in a voxel. Our method can be trained with either simulated or real diffusion-weighted imaging data. Our method estimates the angle to the closest fascicle for each direction in a set of discrete directions uniformly spread on the unit sphere. This information is then processed to extract the number and orientations of fascicles in a voxel. On realistic simulated phantom data with known ground truth, our method predicts the number and orientations of crossing fascicles more accurately than several classical and machine learning methods. It also leads to more accurate tractography. On real data, our method is better than or compares favorably with other methods in terms of robustness to measurement down-sampling and also in terms of expert quality assessment of tractography results.
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Affiliation(s)
- Davood Karimi
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Lana Vasung
- Department of Pediatrics at Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Camilo Jaimes
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Fedel Machado-Rivas
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shadab Khan
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ali Gholipour
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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8
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Shishehbor M, Son H, Nuruddin M, Youngblood JP, Davis C, Zavattieri PD. Influence of alignment and microstructure features on the mechanical properties and failure mechanisms of cellulose nanocrystals (CNC) films. J Mech Behav Biomed Mater 2021; 118:104399. [PMID: 33662741 DOI: 10.1016/j.jmbbm.2021.104399] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 01/17/2021] [Accepted: 02/10/2021] [Indexed: 11/29/2022]
Abstract
The mechanical properties of cellulose nanocrystal (CNC) films critically depend on many microstructural parameters such as fiber length distribution (FLD), fiber orientation distribution (FOD), and the strength of the interactions between the fibers. In this paper, we use our coarse-grained molecular model of CNC to study the effect of length and orientation distribution and attractions between CNCs on the mechanical properties of neat CNCs. The effect of misalignment of a 2D staggered structure of CNC with respect to the loading direction was studied with simulations and analytical solutions and then verified with experiments. To understand the effect of FLD and FOD on the mechanical performance, various 3D microstructures representing different case studies such as highly aligned, randomly distributed, short length CNCs and long length CNCs were generated and simulated. According to the misalignment study, three different failure modes: sliding mode, mixed mode, and normal mode were defined. Also, comparing the effects of FOD, FLD, and CNC interaction strength, shows that the adhesion strength is the only parameter that can significantly improve the mechanical properties, regardless of loading direction or FOD of CNCs.
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Affiliation(s)
- Mehdi Shishehbor
- Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Hyeyoung Son
- School of Materials Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Md Nuruddin
- School of Materials Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Jeffrey P Youngblood
- School of Materials Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Chelsea Davis
- School of Materials Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Pablo D Zavattieri
- Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, 47907, USA.
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9
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Salo RA, Belevich I, Jokitalo E, Gröhn O, Sierra A. Assessment of the structural complexity of diffusion MRI voxels using 3D electron microscopy in the rat brain. Neuroimage 2020; 225:117529. [PMID: 33147507 DOI: 10.1016/j.neuroimage.2020.117529] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 10/09/2020] [Accepted: 10/27/2020] [Indexed: 10/23/2022] Open
Abstract
Validation and interpretation of diffusion magnetic resonance imaging (dMRI) requires detailed understanding of the actual microstructure restricting the diffusion of water molecules. In this study, we used serial block-face scanning electron microscopy (SBEM), a three-dimensional electron microscopy (3D-EM) technique, to image seven white and grey matter volumes in the rat brain. SBEM shows excellent contrast of cellular membranes, which are the major components restricting the diffusion of water in tissue. Additionally, we performed 3D structure tensor (3D-ST) analysis on the SBEM volumes and parameterised the resulting orientation distributions using Watson and angular central Gaussian (ACG) probability distributions as well as spherical harmonic (SH) decomposition. We analysed how these parameterisations described the underlying orientation distributions and compared their orientation and dispersion with corresponding parameters from two dMRI methods, neurite orientation dispersion and density imaging (NODDI) and constrained spherical deconvolution (CSD). Watson and ACG parameterisations and SH decomposition captured well the 3D-ST orientation distributions, but ACG and SH better represented the distributions due to its ability to model asymmetric dispersion. The dMRI parameters corresponded well with the 3D-ST parameters in the white matter volumes, but the correspondence was less evident in the more complex grey matter. SBEM imaging and 3D-ST analysis also revealed that the orientation distributions were often not axially symmetric, a property neatly captured by the ACG distribution. Overall, the ability of SBEM to image diffusion barriers in intricate detail, combined with 3D-ST analysis and parameterisation, provides a step forward toward interpreting and validating the dMRI signals in complex brain tissue microstructure.
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Affiliation(s)
- Raimo A Salo
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, PO Box 1627, FI-70211 Kuopio, Finland
| | - Ilya Belevich
- Electron Microscopy Unit, Institute of Biotechnology, University of Helsinki, PO Box 56, FI-00014 Helsinki, Finland
| | - Eija Jokitalo
- Electron Microscopy Unit, Institute of Biotechnology, University of Helsinki, PO Box 56, FI-00014 Helsinki, Finland
| | - Olli Gröhn
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, PO Box 1627, FI-70211 Kuopio, Finland
| | - Alejandra Sierra
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, PO Box 1627, FI-70211 Kuopio, Finland.
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Alimi A, Deslauriers-Gauthier S, Matuschke F, Müller A, Muenzing SEA, Axer M, Deriche R. Analytical and fast Fiber Orientation Distribution reconstruction in 3D-Polarized Light Imaging. Med Image Anal 2020; 65:101760. [PMID: 32629230 DOI: 10.1016/j.media.2020.101760] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 04/13/2020] [Accepted: 06/18/2020] [Indexed: 12/24/2022]
Abstract
Three dimensional Polarized Light Imaging (3D-PLI) is an optical technique which allows mapping the spatial fiber architecture of fibrous postmortem tissues, at sub-millimeter resolutions. Here, we propose an analytical and fast approach to compute the fiber orientation distribution (FOD) from high-resolution vector data provided by 3D-PLI. The FOD is modeled as a sum of K orientations/Diracs on the unit sphere, described on a spherical harmonics basis and analytically computed using the spherical Fourier transform. Experiments are performed on rich synthetic data which simulate the geometry of the neuronal fibers and on human brain data. Results indicate the analytical FOD is computationally efficient and very fast, and has high angular precision and angular resolution. Furthermore, investigations on the right occipital lobe illustrate that our strategy of FOD computation enables the bridging of spatial scales from microscopic 3D-PLI information to macro- or mesoscopic dimensions of diffusion Magnetic Resonance Imaging (MRI), while being a means to evaluate prospective resolution limits for diffusion MRI to reconstruct region-specific white matter tracts. These results demonstrate the interest and great potential of our analytical approach.
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Affiliation(s)
- Abib Alimi
- Athena Project-Team, Inria Sophia Antipolis-Méditerranée, Université Côte d'Azur, France.
| | | | - Felix Matuschke
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Germany
| | - Andreas Müller
- Simulation Lab Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, JARA, Research Center Jülich, Germany
| | - Sascha E A Muenzing
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Germany
| | - Markus Axer
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Germany
| | - Rachid Deriche
- Athena Project-Team, Inria Sophia Antipolis-Méditerranée, Université Côte d'Azur, France
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Lee HH, Yaros K, Veraart J, Pathan JL, Liang FX, Kim SG, Novikov DS, Fieremans E. Along-axon diameter variation and axonal orientation dispersion revealed with 3D electron microscopy: implications for quantifying brain white matter microstructure with histology and diffusion MRI. Brain Struct Funct 2019; 224:1469-88. [PMID: 30790073 DOI: 10.1007/s00429-019-01844-6] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 02/01/2019] [Indexed: 10/27/2022]
Abstract
Tissue microstructure modeling of diffusion MRI signal is an active research area striving to bridge the gap between macroscopic MRI resolution and cellular-level tissue architecture. Such modeling in neuronal tissue relies on a number of assumptions about the microstructural features of axonal fiber bundles, such as the axonal shape (e.g., perfect cylinders) and the fiber orientation dispersion. However, these assumptions have not yet been validated by sufficiently high-resolution 3-dimensional histology. Here, we reconstructed sequential scanning electron microscopy images in mouse brain corpus callosum, and introduced a random-walker (RaW)-based algorithm to rapidly segment individual intra-axonal spaces and myelin sheaths of myelinated axons. Confirmed by a segmentation based on human annotations initiated with conventional machine-learning-based carving, our semi-automatic algorithm is reliable and less time-consuming. Based on the segmentation, we calculated MRI-relevant estimates of size-related parameters (inner axonal diameter, its distribution, along-axon variation, and myelin g-ratio), and orientation-related parameters (fiber orientation distribution and its rotational invariants; dispersion angle). The reported dispersion angle is consistent with previous 2-dimensional histology studies and diffusion MRI measurements, while the reported diameter exceeds those in other mouse brain studies. Furthermore, we calculated how these quantities would evolve in actual diffusion MRI experiments as a function of diffusion time, thereby providing a coarse-graining window on the microstructure, and showed that the orientation-related metrics have negligible diffusion time-dependence over clinical and pre-clinical diffusion time ranges. However, the MRI-measured inner axonal diameters, dominated by the widest cross sections, effectively decrease with diffusion time by ~ 17% due to the coarse-graining over axonal caliber variations. Furthermore, our 3d measurement showed that there is significant variation of the diameter along the axon. Hence, fiber orientation dispersion estimated from MRI should be relatively stable, while the "apparent" inner axonal diameters are sensitive to experimental settings, and cannot be modeled by perfectly cylindrical axons.
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Schilling KG, Janve V, Gao Y, Stepniewska I, Landman BA, Anderson AW. Histological validation of diffusion MRI fiber orientation distributions and dispersion. Neuroimage 2017; 165:200-221. [PMID: 29074279 DOI: 10.1016/j.neuroimage.2017.10.046] [Citation(s) in RCA: 118] [Impact Index Per Article: 16.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Revised: 10/04/2017] [Accepted: 10/21/2017] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) is widely used to probe tissue microstructure, and is currently the only non-invasive way to measure the brain's fiber architecture. While a large number of approaches to recover the intra-voxel fiber structure have been utilized in the scientific community, a direct, 3D, quantitative validation of these methods against relevant histological fiber geometries is lacking. In this study, we investigate how well different high angular resolution diffusion imaging (HARDI) models and reconstruction methods predict the ground-truth histologically defined fiber orientation distribution (FOD), as well as investigate their behavior over a range of physical and experimental conditions. The dMRI methods tested include constrained spherical deconvolution (CSD), Q-ball imaging (QBI), diffusion orientation transform (DOT), persistent angular structure (PAS), and neurite orientation dispersion and density imaging (NODDI) methods. Evaluation criteria focus on overall agreement in FOD shape, correct assessment of the number of fiber populations, and angular accuracy in orientation. In addition, we make comparisons of the histological orientation dispersion with the fiber spread determined from the dMRI methods. As a general result, no HARDI method outperformed others in all quality criteria, with many showing tradeoffs in reconstruction accuracy. All reconstruction techniques describe the overall continuous angular structure of the histological FOD quite well, with good to moderate correlation (median angular correlation coefficient > 0.70) in both single- and multiple-fiber voxels. However, no method is consistently successful at extracting discrete measures of the number and orientations of FOD peaks. The major inaccuracies of all techniques tend to be in extracting local maxima of the FOD, resulting in either false positive or false negative peaks. Median angular errors are ∼10° for the primary fiber direction and ∼20° for the secondary fiber, if present. For most methods, these results did not vary strongly over a wide range of acquisition parameters (number of diffusion weighting directions and b value). Regardless of acquisition parameters, all methods show improved successes at resolving multiple fiber compartments in a voxel when fiber populations cross at near-orthogonal angles, with no method adequately capturing low to moderate angle (<60°) crossing fibers. Finally, most methods are limited in their ability to capture orientation dispersion, resulting in low to moderate, yet statistically significant, correlation with histologically-derived dispersion with both HARDI and NODDI methodologies. Together, these results provide quantitative measures of the reliability and limitations of dMRI reconstruction methods and can be used to identify relative advantages of competing approaches as well as potential strategies for improving accuracy.
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Affiliation(s)
- Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Vaibhav Janve
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | | | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States
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Yu X, Wang Y, Zhang Y. Transmural variation in elastin fiber orientation distribution in the arterial wall. J Mech Behav Biomed Mater 2017; 77:745-753. [PMID: 28838859 DOI: 10.1016/j.jmbbm.2017.08.002] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Revised: 08/01/2017] [Accepted: 08/02/2017] [Indexed: 12/17/2022]
Abstract
The complex three-dimensional elastin network is a major load-bearing extracellular matrix (ECM) component of an artery. Despite the reported anisotropic behavior of arterial elastin network, it is usually treated as an isotropic material in constitutive models. Our recent multiphoton microscopy study reported a relatively uniform elastin fiber orientation distribution in porcine thoracic aorta when imaging from the intima side (Chow et al., 2014). However it is questionable whether the fiber orientation distribution obtained from a small depth is representative of the elastin network structure in the arterial wall, especially when developing structure-based constitutive models. To date, the structural basis for the anisotropic mechanical behavior of elastin is still not fully understood. In this study, we examined the transmural variation in elastin fiber orientation distribution in porcine thoracic aorta and its association with elastin anisotropy. Using multi-photon microscopy, we observed that the elastin fibers orientation changes from a relatively uniform distribution in regions close to the luminal surface to a more circumferential distribution in regions that dominate the media, then to a longitudinal distribution in regions close to the outer media. Planar biaxial tensile test was performed to characterize the anisotropic behavior of elastin network. A new structure-based constitutive model of elastin network was developed to incorporate the transmural variation in fiber orientation distribution. The new model well captures the anisotropic mechanical behavior of elastin network under both equi- and nonequi-biaxial loading and showed improvements in both fitting and predicting capabilities when compared to a model that only considers the fiber orientation distribution from the intima side. We submit that the transmural variation in fiber orientation distribution is important in characterizing the anisotropic mechanical behavior of elastin network and should be considered in constitutive modeling of an artery.
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Affiliation(s)
- Xunjie Yu
- Department of Mechanical Engineering, Boston University, Boston, MA, United States
| | - Yunjie Wang
- Department of Mechanical Engineering, Boston University, Boston, MA, United States
| | - Yanhang Zhang
- Department of Mechanical Engineering, Boston University, Boston, MA, United States; Department of Biomedical Engineering, Boston University, Boston, MA, United States.
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Morrill EE, Tulepbergenov AN, Stender CJ, Lamichhane R, Brown RJ, Lujan TJ. A validated software application to measure fiber organization in soft tissue. Biomech Model Mechanobiol 2016; 15:1467-78. [PMID: 26946162 DOI: 10.1007/s10237-016-0776-3] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Accepted: 02/19/2016] [Indexed: 10/22/2022]
Abstract
The mechanical behavior of soft connective tissue is governed by a dense network of fibrillar proteins in the extracellular matrix. Characterization of this fibrous network requires the accurate extraction of descriptive structural parameters from imaging data, including fiber dispersion and mean fiber orientation. Common methods to quantify fiber parameters include fast Fourier transforms (FFT) and structure tensors; however, information is limited on the accuracy of these methods. In this study, we compared these two methods using test images of fiber networks with varying topology. The FFT method with a band-pass filter was the most accurate, with an error of [Formula: see text] in measuring mean fiber orientation and an error of [Formula: see text] in measuring fiber dispersion in the test images. The accuracy of the structure tensor method was approximately five times worse than the FFT band-pass method when measuring fiber dispersion. A free software application, FiberFit, was then developed that utilizes an FFT band-pass filter to fit fiber orientations to a semicircular von Mises distribution. FiberFit was used to measure collagen fibril organization in confocal images of bovine ligament at magnifications of [Formula: see text] and [Formula: see text]. Grayscale conversion prior to FFT analysis gave the most accurate results, with errors of [Formula: see text] for mean fiber orientation and [Formula: see text] for fiber dispersion when measuring confocal images at [Formula: see text]. By developing and validating a software application that facilitates the automated analysis of fiber organization, this study can help advance a mechanistic understanding of collagen networks and help clarify the mechanobiology of soft tissue remodeling and repair.
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Schilling K, Janve V, Gao Y, Stepniewska I, Landman BA, Anderson AW. Comparison of 3D orientation distribution functions measured with confocal microscopy and diffusion MRI. Neuroimage 2016; 129:185-197. [PMID: 26804781 DOI: 10.1016/j.neuroimage.2016.01.022] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Revised: 01/05/2016] [Accepted: 01/11/2016] [Indexed: 01/30/2023] Open
Abstract
The ability of diffusion MRI (dMRI) fiber tractography to non-invasively map three-dimensional (3D) anatomical networks in the human brain has made it a valuable tool in both clinical and research settings. However, there are many assumptions inherent to any tractography algorithm that can limit the accuracy of the reconstructed fiber tracts. Among them is the assumption that the diffusion-weighted images accurately reflect the underlying fiber orientation distribution (FOD) in the MRI voxel. Consequently, validating dMRI's ability to assess the underlying fiber orientation in each voxel is critical for its use as a biomedical tool. Here, using post-mortem histology and confocal microscopy, we present a method to perform histological validation of orientation functions in 3D, which has previously been limited to two-dimensional analysis of tissue sections. We demonstrate the ability to extract the 3D FOD from confocal z-stacks, and quantify the agreement between the MRI estimates of orientation information obtained using constrained spherical deconvolution (CSD) and the true geometry of the fibers. We find an orientation error of approximately 6° in voxels containing nearly parallel fibers, and 10-11° in crossing fiber regions, and note that CSD was unable to resolve fibers crossing at angles below 60° in our dataset. This is the first time that the 3D white matter orientation distribution is calculated from histology and compared to dMRI. Thus, this technique serves as a gold standard for dMRI validation studies - providing the ability to determine the extent to which the dMRI signal is consistent with the histological FOD, and to establish how well different dMRI models can predict the ground truth FOD.
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Affiliation(s)
- Kurt Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Vaibhav Janve
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | | | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
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16
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Feng Y, Wu Y, Rathi Y, Westin CF. Sparse deconvolution of higher order tensor for fiber orientation distribution estimation. Artif Intell Med 2015; 65:229-38. [PMID: 26428956 DOI: 10.1016/j.artmed.2015.09.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2015] [Revised: 08/10/2015] [Accepted: 09/02/2015] [Indexed: 11/27/2022]
Abstract
PURPOSE Higher order tensor (HOT) imaging approaches based on the spherical deconvolution framework have attracted much interest for their effectiveness in estimating fiber orientation distribution (FOD). However, sparse regularization techniques are still needed to obtain stable FOD in solving the deconvolution problem, particularly in very high orders. Our goal is to adequately characterize the actual sparsity lying in the FOD domain to develop accurate estimation approach for fiber orientation in HOT framework. MATERIALS AND METHODS We propose a sparse HOT regularization model by enforcing the sparse constraint directly on the representation of FOD instead of imposing it on coefficients of basis function. Then, we incorporate both the stabilizing effect of the l2 penalty and the sparsity encouraging effect of the l1 penalty in the sparse model to adequately characterize the actual sparsity lying in the FOD domain. Furthermore, a weighted regularization scheme is developed to iteratively solve the deconvolution problem. The deconvolution technique is compared against existing methods using l2 or l1 regularizer and tested on synthetic data and real human brain. RESULTS Experiments were conducted on synthetic data and real human brain data. The synthetic experimental results indicate that crossing fibers are more easily detected and the angular resolution limit is improved by our method by approximately 20°-30° compared to existing HOT method. The detection accuracy is considerably improved compared with that of spherical deconvolution approaches using the l2 regularizer and the reweighted l1 scheme. CONCLUSIONS Results of testing the deconvolution technique demonstrate that it allows HOTs to obtain increasingly clean and sharp FOD, which in turn significantly increases the angular resolution of current HOT methods. With sparsity on FOD domain, this method efficiently improves the ability of HOT in resolving crossing fibers.
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Affiliation(s)
- Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou, Zhejiang Province 310023, China.
| | - Ye Wu
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou, Zhejiang Province 310023, China
| | - Yogesh Rathi
- Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, 1249 Boylston Street, Boston, MA 02215, United States
| | - Carl-Fredrik Westin
- Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, 1249 Boylston Street, Boston, MA 02215, United States
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Varentsova A, Zhang S, Arfanakis K. Development of a high angular resolution diffusion imaging human brain template. Neuroimage 2014; 91:177-86. [PMID: 24440528 DOI: 10.1016/j.neuroimage.2014.01.009] [Citation(s) in RCA: 94] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2013] [Revised: 11/05/2013] [Accepted: 01/07/2014] [Indexed: 01/23/2023] Open
Abstract
Brain diffusion templates contain rich information about the microstructure of the brain, and are used as references in spatial normalization or in the development of brain atlases. The accuracy of diffusion templates constructed based on the diffusion tensor (DT) model is limited in regions with complex neuronal micro-architecture. High angular resolution diffusion imaging (HARDI) overcomes limitations of the DT model and is capable of resolving intravoxel heterogeneity. However, when HARDI is combined with multiple-shot sequences to minimize image artifacts, the scan time becomes inappropriate for human brain imaging. In this work, an artifact-free HARDI template of the human brain was developed from low angular resolution multiple-shot diffusion data. The resulting HARDI template was produced in ICBM-152 space based on Turboprop diffusion data, was shown to resolve complex neuronal micro-architecture in regions with intravoxel heterogeneity, and contained fiber orientation information consistent with known human brain anatomy.
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Affiliation(s)
- Anna Varentsova
- Department of Physics, Illinois Institute of Technology, Chicago, IL, USA
| | - Shengwei Zhang
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA; Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL, USA.
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18
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Kurniawan ND, Richards KL, Yang Z, She D, Ullmann JFP, Moldrich RX, Liu S, Yaksic JU, Leanage G, Kharatishvili I, Wimmer V, Calamante F, Galloway GJ, Petrou S, Reutens DC. Visualization of mouse barrel cortex using ex-vivo track density imaging. Neuroimage 2013; 87:465-75. [PMID: 24060319 DOI: 10.1016/j.neuroimage.2013.09.030] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2013] [Revised: 09/06/2013] [Accepted: 09/13/2013] [Indexed: 02/04/2023] Open
Abstract
We describe the visualization of the barrel cortex of the primary somatosensory area (S1) of ex vivo adult mouse brain with short-tracks track density imaging (stTDI). stTDI produced much higher definition of barrel structures than conventional fractional anisotropy (FA), directionally-encoded color FA maps, spin-echo T1- and T2-weighted imaging and gradient echo T1/T2*-weighted imaging. 3D high angular resolution diffusion imaging (HARDI) data were acquired at 48 micron isotropic resolution for a (3mm)(3) block of cortex containing the barrel field and reconstructed using stTDI at 10 micron isotropic resolution. HARDI data were also acquired at 100 micron isotropic resolution to image the whole brain and reconstructed using stTDI at 20 micron isotropic resolution. The 10 micron resolution stTDI maps showed exceptionally clear delineation of barrel structures. Individual barrels could also be distinguished in the 20 micron stTDI maps but the septa separating the individual barrels appeared thicker compared to the 10 micron maps, indicating that the ability of stTDI to produce high quality structural delineation is dependent upon acquisition resolution. Close homology was observed between the barrel structure delineated using stTDI and reconstructed histological data from the same samples. stTDI also detects barrel deletions in the posterior medial barrel sub-field in mice with infraorbital nerve cuts. The results demonstrate that stTDI is a novel imaging technique that enables three-dimensional characterization of complex structures such as the barrels in S1 and provides an important complementary non-invasive imaging tool for studying synaptic connectivity, development and plasticity of the sensory system.
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Affiliation(s)
- Nyoman D Kurniawan
- Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia.
| | - Kay L Richards
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
| | - Zhengyi Yang
- ITEE, University of Queensland, Brisbane, Queensland, Australia
| | - David She
- Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
| | - Jeremy F P Ullmann
- Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
| | - Randal X Moldrich
- Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
| | - Sha Liu
- Queensland Brain Institute, Brisbane, Queensland, Australia
| | - Javier Urriola Yaksic
- Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
| | - Gayeshika Leanage
- Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
| | - Irina Kharatishvili
- Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
| | - Verena Wimmer
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
| | - Fernando Calamante
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia; Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Graham J Galloway
- Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
| | - Steven Petrou
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
| | - David C Reutens
- Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
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