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A data-driven approach to extract connectivity structures from diffusion tensor imaging data

Yu Jin, Joseph F. JaJa, Rong Chen, Edward H. Herskovits
2015 2015 IEEE International Conference on Big Data (Big Data)  
Diffusion Tensor Imaging (DTI) is an effective tool for the analysis of structural brain connectivity in normal development and in a broad range of brain disorders.  ...  Our algorithm is based on a sparse representation of the whole brain connectivity matrix, which reduces the number of edges from around a half billion to a few million while incorporating the necessary  ...  This opens up new research opportunities to generate, explore and analyze complex brain networks derived from Diffusion Tensor Imaging (DTI) based structural connectivity information [1] , [2] .  ... 
doi:10.1109/bigdata.2015.7363843 dblp:conf/bigdataconf/JinJCH15 fatcat:3eqlwn67qjabndwvrdsycvsjyq

Analysis of MR diffusion weighted images

G J M Parker
2004 British Journal of Radiology  
Whilst some of this information may be appreciated visually in diffusion weighted images, much of it may be extracted only with the aid of data post-processing.  ...  This review summarizes the methods available for interpreting diffusion weighted imaging (DWI) information using the diffusion tensor and other models of the DWI signal.  ...  Acknowledgment The author is grateful to Dr Derek Jones for the provision of Figure 7 .  ... 
doi:10.1259/bjr/81090732 pmid:15677359 fatcat:j4mcqwim5vcd3kl7lq5epqdjam

Brain Mapping Using Neuroimaging

Woo-Suk Tae, Shin-Hyuk Kang, Byung-Joo Ham, Byung-Jo Kim, Sung-Bom Pyun
2016 Applied Microscopy  
This review will introduce common neuroimaging modalities, including structural magnetic resonance imaging (MRI), functional MRI (fMRI), diffusion tensor imaging, and other recent neuroimaging analyses  ...  Mapping brain structural and functional connections through the whole brain is essential for understanding brain mechanisms and the physiological bases of brain diseases.  ...  FA images were created by fitting a tensor model to the raw diffusion data using FMRIB's diffusion toolbox, and a brain image was extracted.  ... 
doi:10.9729/am.2016.46.4.179 fatcat:erx2aerd3veard77idz5lmcori

Inter-Scanner Harmonization of High Angular Resolution DW-MRI using Null Space Deep Learning

Vishwesh Nath, Prasanna Parvathaneni, Colin B Hansen, Allison E Hainline, Camilo Bermudez, Samuel Remedios, Justin A Blaber, Kurt G Schilling, Ilwoo Lyu, Vaibhav Janve, Yurui Gao, Iwona Stepniewska (+6 others)
2019 Lecture Notes-Monograph Series  
Diffusion-weighted magnetic resonance imaging (DW-MRI) allows for non-invasive imaging of the local fiber architecture of the human brain at a millimetric scale.  ...  Herein, we propose a data-driven technique using a neural network design which exploits two categories of data.  ...  It would be interesting to explore the impact of including data from diffusion phantoms to enhance the diversity of signals captured in a data-driven approach.  ... 
pmid:34456460 pmcid:PMC8388262 fatcat:355bowgxbjdnddm2f5xmnisube

Inter-Scanner Harmonization of High Angular Resolution DW-MRI using Null Space Deep Learning [article]

Vishwesh Nath, Prasanna Parvathaneni, Colin B. Hansen, Allison E. Hainline, Camilo Bermudez, Samuel Remedios, Justin A. Blaber, Kurt G. Schilling, Ilwoo Lyu, Vaibhav Janve, Yurui Gao, Iwona Stepniewska (+5 others)
2018 arXiv   pre-print
Diffusion-weighted magnetic resonance imaging (DW-MRI) allows for non-invasive imaging of the local fiber architecture of the human brain at a millimetric scale.  ...  Herein, we propose a data-driven tech-nique using a neural network design which exploits two categories of data.  ...  It would be interesting to explore the impact of including data from diffusion phantoms to enhance the diversity of signals captured in a data-driven approach.  ... 
arXiv:1810.04260v1 fatcat:6fcaknpb6fa7helwxfnhgvacxa

Riemannian Manifolds For Brain Extraction On Multi-Modal Resonance Magnetic Images

Mohamed Gouskir, Belaid Bouikhalene, Hicham Aissaoui, Benachir Elhadadi
2015 Zenodo  
tensor) to regularize the convergence contour and extract complex anatomical structures.  ...  We consider the image as residing in a Riemannian manifold, for developing a new method to brain edge detection and brain extraction.  ...  ACKNOWLEDGMENT We would like to thank deeply Professor Mohamed Naimi, Vice-Dean of Polydisciplinary Faculty of Beni-Mellal, for his precious help.  ... 
doi:10.5281/zenodo.1109803 fatcat:grsl3lc3tfhtrarjjt73fgmj2e

Visualization in Connectomics [article]

Hanspeter Pfister, Verena Kaynig, Charl P. Botha, Stefan Bruckner, Vincent J. Dercksen, Hans-Christian Hege, Jos B. T. M. Roerdink
2012 arXiv   pre-print
Such a representation is believed to increase our understanding of how functional brain states emerge from their underlying anatomical structure.  ...  Connectomics is a field of neuroscience that analyzes neuronal connections. A connectome is a complete map of a neuronal system, comprising all neuronal connections between its structures.  ...  Based on such data, multiple diffusion tensors can be fit to the data [94] , higher order tensors can be used [70] , or a model-free method such as Q-Ball imaging [93] can be applied.  ... 
arXiv:1206.1428v2 fatcat:xjayhfspffe7hloxan4drfkggy

A Kernel-Based Approach for User-Guided Fiber Bundling using Diffusion Tensor Data

Raul San Jose Estepar, Marek Kubicki, Martha Shenton, Carl-Fredrik Westin
2006 2006 International Conference of the IEEE Engineering in Medicine and Biology Society  
The method finds fibers, that passing through user-defined ROIs, still fit to the underlying data model given by the diffusion tensor.  ...  This paper describes a novel user-guided method for grouping fibers from diffusion tensor MRI tractography into bundles.  ...  Our method tries to overcome this dependency by presenting a method based on ROIs but driven by the diffusion tensor data.  ... 
doi:10.1109/iembs.2006.259829 pmid:17946126 pmcid:PMC2768065 fatcat:nzg5wer6mjhxnjr5335hqnjfru

A Kernel-Based Approach for User-Guided Fiber Bundling using Diffusion Tensor Data

Raul San Jose Estepar, Marek Kubicki, Martha Shenton, Carl-Fredrik Westin
2006 IEEE Engineering in Medicine and Biology Society. Conference Proceedings  
The method finds fibers, that passing through user-defined ROIs, still fit to the underlying data model given by the diffusion tensor.  ...  This paper describes a novel user-guided method for grouping fibers from diffusion tensor MRI tractography into bundles.  ...  Our method tries to overcome this dependency by presenting a method based on ROIs but driven by the diffusion tensor data.  ... 
doi:10.1109/iembs.2006.4397985 fatcat:7p6chgmzxvdfbbq3b2esizu26e

Visualization in Connectomics [chapter]

Hanspeter Pfister, Verena Kaynig, Charl P. Botha, Stefan Bruckner, Vincent J. Dercksen, Hans-Christian Hege, Jos B. T. M. Roerdink
2014 Mathematics and Visualization  
After a brief summary of the biological background and an overview of relevant imaging modalities, we review current techniques to extract connectivity H. Pfister (B) · V. Kaynig H.  ...  Connectomics is a branch of neuroscience that attempts to create a connectome, i.e., a complete map of the neuronal system and all connections between neuronal structures.  ...  Based on such data, multiple diffusion tensors can be fit to the data [95] , higher order tensors can be used [69] , or a model-free method such as Q-Ball imaging [96] can be applied.  ... 
doi:10.1007/978-1-4471-6497-5_21 fatcat:hlsaktgoofabnaa25zpykhlgpa

Multi-scale, multi-level, heterogeneous features extraction and classification of volumetric medical images

Shuai Li, Qinping Zhao, Shengfa Wang, Aimin Hao, Hong Qin
2013 2013 IEEE International Conference on Image Processing  
To tackle the challenge of complex volumetric inner structure and diverse feature forms, our technical solution hinges upon the integrated approach of locally-defined diffusion tensor (DT), DT-based anisotropic  ...  This paper articulates a novel method for the heterogeneous feature extraction and classification directly on volumetric images, which covers multi-scale point feature, multi-scale surface feature, multi-level  ...  structural feature extraction, classification, filtering and visualization, which requires no additional manual inputs except for a few threshold parameters. (2) We define a diffusion tensor to facilitate  ... 
doi:10.1109/icip.2013.6738291 dblp:conf/icip/LiZWHQ13 fatcat:bn3xro33k5f3xjrlnbpcijuywy

Novel image processing techniques to better understand white matter disruption in multiple sclerosis

Daniel Goldberg-Zimring, Simon K. Warfield
2006 Autoimmunity Reviews  
obtained from a set of subjects, and Tractographies which can aid in the delineation of WM fiber tracts by tracking connected diffusion tensors.  ...  The geometry of diffusion tensors can quantitatively characterize the local structure in tissues.  ...  The geometry of diffusion tensors can quantitatively characterize the local structure in tissues. • Diffusion tensor magnetic resonance imaging provides both orientation and anisotropy information regarding  ... 
doi:10.1016/j.autrev.2006.06.003 pmid:17027890 fatcat:uuq4adh3fzfzdpwd7qhfmpsm2a

Understanding Alterations in Brain Connectivity in Attention-Deficit/Hyperactivity Disorder Using Imaging Connectomics

Martha E. Shenton, Marek Kubicki, Nikos Makris
2014 Biological Psychiatry  
Hong et al. use diffusion tensor imaging and whole-brain tractography, the latter an unbiased, data-driven approach, as opposed to a hypothesis-driven, single-tract a priori approach, along with an imaging  ...  diffusion tractography, a means of extracting information about specific fiber bundles in the brain, and imaging connectomics techniques, a way to map neural connections in the brain at synaptic and macrostructural  ...  Hong et al. use diffusion tensor imaging and whole-brain tractography, the latter an unbiased, data-driven approach, as opposed to a hypothesis-driven, single-tract a priori approach, along with an imaging  ... 
doi:10.1016/j.biopsych.2014.08.018 pmid:25262232 fatcat:3yg3r2bjh5hkjlgehfiyiyev3i

Page 9790 of The Journal of Neuroscience Vol. 29, Issue 39 [page]

2008 The Journal of Neuroscience  
In addition, structural data on the density of coherency of white matter tracts were obtained using diffusion tensor imaging (DTI).  ...  nucleus (Aron et al., 2007), indicat- sponse selection using model-driven functional magnetic reso- nance imaging (fMRI) and diffusion tensor imaging (DTI).  ... 

A Fuzzy, Nonparametric Segmentation Framework for DTI and MRI Analysis: With Applications to DTI-Tract Extraction

S.P. Awate, Zhang Hui, J.C. Gee
2007 IEEE Transactions on Medical Imaging  
This paper presents a novel fuzzy-segmentation method for diffusion tensor (DT) and magnetic resonance (MR) images.  ...  By enhancing the nonparametric model to capture the spatial continuity and structure of the fiber bundle, we exploit the framework to extract the cingulum fiber bundle.  ...  ACKNOWLEDGMENT The authors would like to thank T. R. Franklin and A. R.  ... 
doi:10.1109/tmi.2007.907301 pmid:18041267 fatcat:r3gwamh3fbanxake3pcajt4u7u
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