Relevant bibliographies by topics / Diffusion tendor imaging

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Diffusion tendor imaging'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Diffusion tendor imaging"

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Soni, PK, and Saurabh Kumar Sahu. "Fractional Anisotropy and Apparent Diffusion Coefficient values on Diffusor Tensor Imaging in Parkinson’s Disease: A case-control study." International Journal of Neurology and Neurosurgery 11, no. 3 (2019): 223–28. http://dx.doi.org/10.21088/ijnns.0975.0223.11319.9.

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Im, In-Chul, Eun-Hoe Goo, and Jae-Seung Lee. "Evaluation of the Neural Fiber Tractography Associated with Aging in the Normal Corpus Callosum Using the Diffusion Tensor Imaging (DTI)." Journal of the Korean Society of Radiology 5, no. 4 (August 30, 2011): 189–94. http://dx.doi.org/10.7742/jksr.2011.5.4.189.

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Mori, Harushi. "Diffusion tensor imaging." Rinsho Shinkeigaku 48, no. 11 (2008): 945–46. http://dx.doi.org/10.5692/clinicalneurol.48.945.

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Sener, Süleyman, Wim Van Hecke, Bart F. E. Feyen, Gregory Van der Steen, Pim Pullens, Luc Van de Hauwe, Tomas Menovsky, Paul M. Parizel, Philippe G. Jorens, and Andrew I. R. Maas. "Diffusion Tensor Imaging." Neurosurgery 79, no. 6 (June 26, 2016): 786–93. http://dx.doi.org/10.1227/neu.0000000000001325.

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Abstract BACKGROUND: A great need exists in traumatic brain injury (TBI) and aneurysmal subarachnoid hemorrhage (aSAH) for objective biomarkers to better characterize the disease process and to serve as early endpoints in clinical studies. Diffusion tensor imaging (DTI) has shown promise in TBI, but much less is known about aSAH. OBJECTIVE: To explore the use of whole-brain DTI tractography in TBI and aSAH as a biomarker and early endpoint. METHODS: Of a cohort of 43 patients with severe TBI (n = 20) or aSAH (n = 23) enrolled in a prospective, observational, multimodality monitoring study, DTI data were acquired at approximately day 12 (median, 12 days; interquartile range, 12-14 days) after injury in 22 patients (TBI, n = 12; aSAH, n = 10). Whole-brain DTI tractography was performed, and the following parameters quantified: average fractional anisotropy, mean diffusivity, tract length, and the total number of reconstructed fiber tracts. These were compared between TBI and aSAH patients and correlated with mortality and functional outcome assessed at 6 months by the Glasgow Outcome Scale Extended. RESULTS: Significant differences were found for fractional anisotropy values (P = .01), total number of tracts (P = .03), and average tract length (P = .002) between survivors and nonsurvivors. A sensitivity analysis showed consistency of results between the TBI and aSAH patients for the various DTI measures. CONCLUSION: DTI parameters, assessed at approximately day 12 after injury, correlated with mortality at 6 months in patients with severe TBI or aSAH. Similar patterns were found for both TBI and aSAH patients. This supports a potential role of DTI as early endpoint for clinical studies and a predictor of late mortality.
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CASCIO, CARISSA J., GUIDO GERIG, and JOSEPH PIVEN. "Diffusion Tensor Imaging." Journal of the American Academy of Child & Adolescent Psychiatry 46, no. 2 (February 2007): 213–23. http://dx.doi.org/10.1097/01.chi.0000246064.93200.e8.

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Rovaris, Marco, Federica Agosta, Elisabetta Pagani, and Massimo Filippi. "Diffusion Tensor MR Imaging." Neuroimaging Clinics of North America 19, no. 1 (February 2009): 37–43. http://dx.doi.org/10.1016/j.nic.2008.08.001.

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Ulmer, John L., Andrew P. Klein, Wade M. Mueller, Edgar A. DeYoe, and Leighton P. Mark. "Preoperative Diffusion Tensor Imaging." Neuroimaging Clinics of North America 24, no. 4 (November 2014): 599–617. http://dx.doi.org/10.1016/j.nic.2014.08.002.

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Topgaard, Daniel. "Diffusion tensor distribution imaging." NMR in Biomedicine 32, no. 5 (February 7, 2019): e4066. http://dx.doi.org/10.1002/nbm.4066.

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Diehl, Beate, Mark R. Symms, Philip A. Boulby, Tuuli Salmenpera, Claudia A. M. Wheeler-Kingshott, Gareth J. Barker, and John S. Duncan. "Postictal diffusion tensor imaging." Epilepsy Research 65, no. 3 (July 2005): 137–46. http://dx.doi.org/10.1016/j.eplepsyres.2005.05.007.

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Glasier, Charles M. "Pediatric diffusion and diffusion tensor imaging." Pediatric Radiology 37, no. 8 (June 28, 2007): 733. http://dx.doi.org/10.1007/s00247-007-0466-5.

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Dissertations / Theses on the topic "Diffusion tendor imaging"

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Inano, Rika. "Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading." Kyoto University, 2016. http://hdl.handle.net/2433/215442.

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Heim, Susanne. "Statistical Diffusion Tensor Imaging." Diss., lmu, 2007. http://nbn-resolving.de/urn:nbn:de:bvb:19-72610.

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Shen, Litao. "Diffusion tensor imaging application." Thesis, Purdue University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1602902.

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Central nervous system (CNS) related conditions and diseases like mild traumatic brain injury (mTBI) and multiple sclerosis (MS) affect people’s life quality, yet there is no single test for the diagnosis of these diseases or conditions. Patients may need to wait for years until they are diagnosed correctly to get the correct treatment, which is often too late. Thus, there is a strong need to develop some techniques to aid the diagnosis of CNS-related conditions and diseases. The conventional MRI can reveal the structure of the brain but cannot detect the difference between the healthy tissue and the anomalies. Diffusion tensor imaging (DTI) has been used for detecting white matter integrity and demyelination for the past decade in experiments and has been proven to have the ability to depict the problem effectively. In the past decade, many techniques were found based on DTI data, and these techniques improved pre-processing, processing, and post-processing.

Though there are many software and APIs that can provide functions for DTI file input/output (IO), visualization and other DTI related topics, there is no general software or API that is dedicated to covering the whole processing procedure of DTI that at the same time can be extended easily by the user. This thesis is dedicated to developing a software that can be used to aid in the diagnosis of CNS-related conditions and diseases while at the same time trying to cover as many topics as possible. Another purpose is to make the software highly extensible.

This thesis work first introduces the background of CNS-related disease and uses MS as an example to introduce the process of demyelination and the white matter integrity problem, which are involved in these CNS-related diseases and conditions. Then the diffusion process and the technique that can detect the diffusion signal (DTI) is presented. After this, concepts and meaning of the secondary metrics are discussed. Then, current existing software and APIs and their advantages and disadvantages are outlined. After these points, the techniques that are discussed in this thesis as well as their advantages are outlined. This part is followed by the charts and code samples which can illustrate the process and structure of this software. Then different modules and their results are explained.

In this software, the results are represented by images and 3D models. There are color images, pseudo color images with different schemes and gray scale images. Images are mainly included to represent the FA and MD data. In this software, streamlines are generated from the eigenvalue and eigenvector. Then a bundled result for the streamline is also realized in this software. The streamline and bundled results are 3D models. For 3D models, there are mainly two ways to display the real 3D model. One is the naked eye 3D which doesn’t require the user to wear glasses but has less stereoscopic characteristics. As the stereoscopic monitors and glasses are more and more popular and easily accessible, this software also provides stereoscopic views for 3D models, and the user can choose red & blue, interlaced techniques with proper glasses.

This thesis work ends with the discussion of the results and limitations of DTI. Finally, there is a discussion about the future work that can improve the performance of this software and topics that need to be covered.

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Rane, Swati. "Diffusion tensor imaging at long diffusion time." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29708.

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Thesis (Ph.D)--Biomedical Engineering, Georgia Institute of Technology, 2009.
Committee Chair: Hu, Xiaoping; Committee Member: Brummer, Marijn; Committee Member: Duong, Tim; Committee Member: Keilholz, Shella; Committee Member: Schumacher, Eric. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Campbell, Jennifer 1975. "Magnetic resonance diffusion tensor imaging." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=30809.

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Magnetic resonance imaging (MRI) can be used to image diffusion in liquids, such as water in brain structures. Molecular diffusion can be isotropic or anisotropic, depending on the fluid's environment, and can therefore be characterized by a scalar, D, or by a tensor, D, in the respective cases. For anisotropic environments, the eigenvector of D corresponding to the largest eigenvalue indicates the preferred direction of diffusion.
This thesis describes the design and implementation of diffusion tensor imaging on a clinical MRI system. An acquisition sequence was designed and post-processing software developed to create diffusion trace images, scalar anisotropy maps, and anisotropy vector maps. A number of practical imaging problems were addressed and solved, including optimization of sequence parameters, accounting for flow effects, and dealing with eddy currents, patient motion, and ghosting. Experimental validation of the sequence was performed by calculating the trace of the diffusion tensor measured in various isotropic liquids. The results agreed very well with the quantitative values found in the literature, and the scalar anisotropy index was also found to be correct in isotropic phantoms. Anisotropy maps, showing the preferred direction of diffusion, were generated in human brain in vivo. These showed the expected white matter tracts in the corpus callosum.
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Prem, Martin [Verfasser], and Dieter [Akademischer Betreuer] Riemann. "Diffusion-Tensor-Imaging bei primärer Insomnie." Freiburg : Universität, 2012. http://d-nb.info/1123469768/34.

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Zhou, Diwei. "Statistical analysis of diffusion tensor imaging." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/11430/.

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This thesis considers the statistical analysis of diffusion tensor imaging (DTI). DTI is an advanced magnetic resonance imaging (MRI) method that provides a unique insight into biological microstructure \textit{in vivo} by directionally describing the water molecular diffusion. We firstly develop a Bayesian multi-tensor model with reparameterisation for capturing water diffusion at voxels with one or more distinct fibre orientations. Our model substantially alleviates the non-identifiability issue present in the standard multi-tensor model. A Markov chain Monte Carlo (MCMC) algorithm is then developed to study the uncertainty of the model parameters based on the posterior distribution. We apply the Bayesian method to Monte Carlo (MC) simulated datasets as well as a healthy human brain dataset. A region containing crossing fibre bundles is investigated using our multi-tensor model with automatic model selection. A diffusion tensor, a covariance matrix related to the molecular displacement at a particular voxel in the brain, is in the non-Euclidean space of 3x3 positive semidefinite symmetric matrices. We define the sample mean of tensor data to be the Fréchet mean. We carry out the non-Euclidean statistical analysis of diffusion tensor data. The primary focus is on the use of Procrustes size-and-shape space. Comparisons are made with other non-Euclidean techniques, including the log-Euclidean, Riemannian, Cholesky, root Euclidean and power Euclidean methods. The weighted generalised Procrustes analysis has been developed to efficiently interpolate and smooth an arbitrary number of tensors with the flexibility of controlling individual contributions. A new anisotropy measure, Procrustes Anisotropy is defined and compared with other widely used anisotropy measures. All methods are illustrated through synthetic examples as well as white matter tractography of a healthy human brain. Finally, we use Giné’s statistic to design uniformly distributed diffusion gradient direction schemes with different numbers of directions. MC simulation studies are carried out to compare effects of Giné’s and widely used Jones' schemes on tensor estimation. We conclude by discussing potential areas for further research.
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Errangi, Bhargav Kumar. "A diffusion tensor imaging study of." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28156.

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Thesis (M. S.)--Biomedical Engineering, Georgia Institute of Technology, 2009.
Committee Chair: James K. Rilling; Committee Chair: Xiaoping Hu; Committee Member: Shella Keilholz; Committee Member: Todd M. Preuss.
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Edalati, Ahmadsaraei Masoud. "Diffusion Tensor Imaging: Application to Cardiovascular Magnetic Resonance Imaging." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1470754609.

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Wang, Zhizhou. "Diffusion tensor field restoration and segmentation." [Gainesville, Fla.] : University of Florida, 2004. http://purl.fcla.edu/fcla/etd/UFE0006046.

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Books on the topic "Diffusion tendor imaging"

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Stieltjes, Bram, Romuald M. Brunner, Klaus H. Fritzsche, and Frederik B. Laun. Diffusion Tensor Imaging. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-20456-2.

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Van Hecke, Wim, Louise Emsell, and Stefan Sunaert, eds. Diffusion Tensor Imaging. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3118-7.

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Introduction to diffusion tensor imaging. Amsterdam: Elsevier, 2006.

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Wilde, Elisabeth A., Kareem W. Ayoub, and Asim F. Choudhri. Diffusion Tensor Imaging. Edited by Andrew C. Papanicolaou. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199764228.013.10.

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Diffusion tensor imaging (DTI) is a method of specifying and visualizing the functional integrity of white matter tracts that contribute to the functional and structural connectivity among different brain regions through the examination of water diffusion through tissue. It has gained rapid popularity in the past two decades, particularly for elucidating the process of normal white matter development and the effects of aging on it, as well as providing some insights into the possible neuroanatomical correlates of numerous psychiatric and neurologic disorders. This chapter outlines the instrumentation and the procedures employed in deriving estimates of the functional integrity of anatomical connections in the brain, and issues regarding the reliability and validity of the different DTI procedures are systematically addressed.
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Mori, S. Introduction to Diffusion Tensor Imaging. Elsevier Science, 2007.

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Introduction to Diffusion Tensor Imaging. Elsevier, 2014. http://dx.doi.org/10.1016/c2011-0-07607-x.

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Introduction to Diffusion Tensor Imaging. Elsevier, 2007. http://dx.doi.org/10.1016/b978-0-444-52828-5.x5014-5.

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Sunaert, Stefan, Wim Van Hecke, and Louise Emsell. Diffusion Tensor Imaging: A Practical Handbook. Springer, 2015.

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Fritzsche, Klaus, Bram Stieltjes, Romuald M. Brunner, and Frederik Laun. Diffusion Tensor Imaging: Introduction and Atlas. Springer, 2012.

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Fritzsche, Klaus, Bram Stieltjes, Romuald M. Brunner, and Frederik Laun. Diffusion Tensor Imaging: Introduction and Atlas. Springer, 2017.

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Book chapters on the topic "Diffusion tendor imaging"

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Veraart, Jelle, and Jan Sijbers. "Diffusion Kurtosis Imaging." In Diffusion Tensor Imaging, 407–18. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3118-7_21.

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Stieltjes, Bram, Romuald M. Brunner, Klaus H. Fritzsche, and Frederik B. Laun. "Introduction to Diffusion Imaging." In Diffusion Tensor Imaging, 5–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-20456-2_1.

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Dhollander, Thijs. "From Diffusion to the Diffusion Tensor." In Diffusion Tensor Imaging, 37–63. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3118-7_4.

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Stieltjes, Bram, Romuald M. Brunner, Klaus H. Fritzsche, and Frederik B. Laun. "Two-dimensional Brain Slices." In Diffusion Tensor Imaging, 43–280. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-20456-2_2.

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Stieltjes, Bram, Romuald M. Brunner, Klaus H. Fritzsche, and Frederik B. Laun. "Three-dimensional Fiber Tracking." In Diffusion Tensor Imaging, 281–376. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-20456-2_3.

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Emsell, Louise. "How to Use this Book." In Diffusion Tensor Imaging, 3–5. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3118-7_1.

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Van Hecke, Wim, Alexander Leemans, and Louise Emsell. "DTI Analysis Methods: Voxel-Based Analysis." In Diffusion Tensor Imaging, 183–203. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3118-7_10.

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Caan, Matthan W. A. "DTI Analysis Methods: Fibre Tracking and Connectivity." In Diffusion Tensor Imaging, 205–28. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3118-7_11.

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Stieltjes, Bram. "Normal Diffusion Tensor Imaging-Based White Matter Anatomy." In Diffusion Tensor Imaging, 231–71. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3118-7_12.

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Emsell, Louise, and Stefan Sunaert. "DTI in Clinical Practice: Opportunities and Considerations." In Diffusion Tensor Imaging, 275–90. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3118-7_13.

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Conference papers on the topic "Diffusion tendor imaging"

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Chen, Bin, and John Moreland. "Human Brain Diffusion Tensor Imaging Visualization With Virtual Reality." In ASME 2010 World Conference on Innovative Virtual Reality. ASMEDC, 2010. http://dx.doi.org/10.1115/winvr2010-3761.

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Magnetic resonance diffusion tensor imaging (DTI) is sensitive to the anisotropic diffusion of water exerted by its macromolecular environment and has been shown useful in characterizing structures of ordered tissues such as the brain white matter, myocardium, and cartilage. The water diffusivity inside of biological tissues is characterized by the diffusion tensor, a rank-2 symmetrical 3×3 matrix, which consists of six independent variables. The diffusion tensor contains much information of diffusion anisotropy. However, it is difficult to perceive the characteristics of diffusion tensors by looking at the tensor elements even with the aid of traditional three dimensional visualization techniques. There is a need to fully explore the important characteristics of diffusion tensors in a straightforward and quantitative way. In this study, a virtual reality (VR) based MR DTI visualization with high resolution anatomical image segmentation and registration, ROI definition and neuronal white matter fiber tractography visualization and fMRI activation map integration is proposed. The VR application will utilize brain image visualization techniques including surface, volume, streamline and streamtube rendering, and use head tracking and wand for navigation and interaction, the application will allow the user to switch between different modalities and visualization techniques, as well making point and choose queries. The main purpose of the application is for basic research and clinical applications with quantitative and accurate measurements to depict the diffusivity or the degree of anisotropy derived from the diffusion tensor.
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Nielsen, Jon F., Ashok Panigrahy, and Stephan G. Erberich. "Diffusion tensor imaging in newborns." In Medical Imaging 2004, edited by Amir A. Amini and Armando Manduca. SPIE, 2004. http://dx.doi.org/10.1117/12.536240.

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Lee, Tin Man, and Usha Sinha. "Denoising diffusion tensor images: preprocessing for automated detection of subtle diffusion tensor abnormalities between populations." In Medical Imaging, edited by Joseph M. Reinhardt and Josien P. W. Pluim. SPIE, 2006. http://dx.doi.org/10.1117/12.654264.

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Descoteaux, Maxime, Christophe Lenglet, and Rachid Deriche. "Diffusion tensor sharpening improves white matter tractography." In Medical Imaging, edited by Josien P. W. Pluim and Joseph M. Reinhardt. SPIE, 2007. http://dx.doi.org/10.1117/12.708988.

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Pu, Lingling, Theodore P. Trouard, Lee Ryan, Chuan Huang, Maria I. Altbach, and Ali Bilgin. "Model-based compressive diffusion tensor imaging." In 2011 8th IEEE International Symposium on Biomedical Imaging (ISBI 2011). IEEE, 2011. http://dx.doi.org/10.1109/isbi.2011.5872400.

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Curran, Kathleen M., and Daniel C. Alexander. "Diffusion Tensor Orientation Matching for Image Registration." In Medical Imaging 2003, edited by Milan Sonka and J. Michael Fitzpatrick. SPIE, 2003. http://dx.doi.org/10.1117/12.481130.

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Li, Yimei, Hongtu Zhu, Yasheng Chen, Joseph G. Ibrahim, Hongyu An, Weili Lin, Colin Hall, and Dinggang Shen. "RADTI: regression analyses of diffusion tensor images." In SPIE Medical Imaging, edited by Josien P. W. Pluim and Benoit M. Dawant. SPIE, 2009. http://dx.doi.org/10.1117/12.812328.

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Ebrahimi, Behzad, Siamak P. Nejad Davarani, GuangLiang Ding, Quan Jiang, and Timothy E. Chupp. "A microfabricated phantom for diffusion tensor imaging." In SPIE Medical Imaging, edited by Robert C. Molthen and John B. Weaver. SPIE, 2010. http://dx.doi.org/10.1117/12.844460.

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Beg, Mirza Faisal, Ryan Dickie, Gregory Golds, and Laurent Younes. "Consistent realignment of 3D diffusion tensor MRI eigenvectors." In Medical Imaging, edited by Josien P. W. Pluim and Joseph M. Reinhardt. SPIE, 2007. http://dx.doi.org/10.1117/12.710369.

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Cisternas, J., T. Asahi, M. Galvez, and G. Rojas. "Regularization of diffusion tensor images." In 2008 5th IEEE International Symposium on Biomedical Imaging (ISBI 2008). IEEE, 2008. http://dx.doi.org/10.1109/isbi.2008.4541151.

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Reports on the topic "Diffusion tendor imaging"

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Little, Deborah M. High Resolution Diffusion Tensor Imaging of Cortical-Subcortical White Matter Tracts in TBI. Fort Belvoir, VA: Defense Technical Information Center, October 2009. http://dx.doi.org/10.21236/ada513063.

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Little, Deborah M. High Resolution Diffusion Tensor Imaging of Cortical-Subcortical White Matter Tracts in TBI. Fort Belvoir, VA: Defense Technical Information Center, October 2010. http://dx.doi.org/10.21236/ada549548.

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Yin, Xiaoqin, Meijun Liu, Yang Liao, Qizheng Li, Xiaolin Hou, Dongdong Yang, Xin Chu, Chan Zhu, and Shuoguo Jin. The diagnostic value of Diffusion tensor imaging（DTI） in Parkinson's disease: a meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, July 2020. http://dx.doi.org/10.37766/inplasy2020.7.0098.

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Li, Yiming, and Jiahe Guo. Diffusion Tensor Imaging and Intraoperative Subcortical Stimulation: Comparative Meta Analysis of Subcortical Functional Areas. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2021. http://dx.doi.org/10.37766/inplasy2021.8.0013.

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Brody, David L. Radiological-Pathological Correlations Following Blast-Related Traumatic Brain Injury in the Whole Human Brain Using ex Vivo Diffusion Tensor Imaging. Fort Belvoir, VA: Defense Technical Information Center, January 2014. http://dx.doi.org/10.21236/ada597888.

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