Electronic Posters : Diffusion & Perfusion - Neuro
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Mapping Structural Anisotropy : Kurtosis

Monday May 9th
Exhibition Hall  14:00 - 16:00 Computer 84

14:00 4011.   Estimation of kurtosis in accelerated diffusion spectrum imaging using compressed sensing 
Jonathan Immanuel Sperl1, Ek Tsoon Tan2, Kedar Khare2, Kevin F King3, Xiaodong Tao2, Christopher J Hardy2, Luca Marinelli2, and Marion I Menzel1
1GE Global Research, Garching, Germany, 2GE Global Research, Niskayuna, NY, United States, 3GE Healthcare, Waukesha, WI, United States

Diffusion spectrum imaging provides radial information of diffusivity in the brain, such as diffusional kurtosis. This work presents a two step quadratic programming framework for fitting the diffusion and kurtosis tensors. Furthermore, the effects of using undersampled data and compressed sensing recon-structions are investigated. Various derived scalar measures for kurtosis are compared for a human brain data set. The results show the robustness of the fitting procedure as compared to standard linear fitting. Compressed sensing allows for either faster acquisitions or improved image quality, while providing some degree of denoising.

14:30 4012.   Do commonly used b-values yield accurate apparent kurtosis values? 
Tristan Anselm Kuder1, Bram Stieltjes2, Wolfhard Semmler1, and Frederik Bernd Laun1
1Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany, 2Quantitative Imaging-based Disease Characterization, German Cancer Research Center, Heidelberg, Germany

Diffusional kurtosis is usually measured by fitting to the expansion of the logarithmic signal in b terminated after the quadratic summand. Since it is not clear, if neglecting higher order terms is justified for b-values typically applied in clinical applications, in this work, the influence of higher order terms was investigated for different model geometries using computer simulations and in phantom experiments. For b-values typically used in vivo, the measured kurtosis strongly deviates from the correct value showing an important influence of the higher order summands.

15:00 4013.   Diffusion gradient correction in Diffusion Kurtosis Imaging 
Xiaowei Zou1, Jordan S. Muraskin1, Melvyn B. Ooi2, and Truman R. Brown3
1Biomedical Engineering, Columbia University, New York, NY, United States, 2Stanford University, 3Radiology, Columbia University

Diffusion gradient correction in DKI that resolves the inconsistence between gradient vectors and realigned images.

15:30 4014.   A novel Diffusion Kurtosis Imaging system using heteroscedastic multiple regression 
Xiaowei Zou1, and Truman R. Brown2
1Biomedical Engineering, Columbia University, New York, NY, United States, 2Radiology, Columbia University

A weighted global-fitting based Diffusion Kurtosis Imaging system that significantly improves reproducibility and robustness

Electronic Posters : Diffusion & Perfusion - Neuro
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Mapping Structural Anisotropy : Reconstruction & Morphometry

Tuesday May 10th
Exhibition Hall  13:30 - 15:30 Computer 85

13:30 4015.   Online Reconstruction and Motion Detection in HARDI 
Emmanuel Caruyer1, Iman Aganj2, Christophe Lenglet3, Guillermo Sapiro2, and Rachid Deriche1
1Athena Project-Team, INRIA Sophia Antipolis - Méditerranée, Sophia Antipolis, France, 2Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States, 3Department of Radiology - CMRR, University of Minnesota Medical School, Minneapolis, MN, United States

We demonstrate how Orientation Distribution Functions (ODFs) can be estimated online from High Angular Resolution Diffusion Imaging (HARDI) data and how this procedure can efficiently be used to detect head motion during acquisition.

14:00 4016.   Multiple Kernel Spherical Deconvolution 
Qiuyun Fan1,2, Xin Hong2, Nicole Davis3,4, Laurie E. Cutting3,5, and Adam W. Anderson1,2
1Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, 2Vanderbilt University Institute of Imaging Science, Nashville, TN, United States, 3Vanderbilt University Kennedy Center for Research on Human Development, Nashville, TN, United States, 4Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States, 5Department of Special Education, Vanderbilt Peabody, Nashville, TN, United States

DTI provides reproducible measures of fiber integrity, although it is unable to resolve crossing fibers. HARDI methods can resolve crossings, but do not provide estimates of the intrinsic anisotropy in each fiber. In this work, we propose an approach that resolves crossing fibers and estimates the intrinsic diffusion properties of each fiber. Multiple kernels are allowed in spherical deconvolution and multiple shells in q-space are sampled in order to estimate the kernel for each resolvable fiber in a voxel. The results can potentially provide more accurate analysis of the properties of fiber pathways in the brain.

14:30 4017.   Brain Atlas-based Study of the Interplay between Normal Tissue Microstructural MRI Parameters 
Indika S Walimuni1, and Khader M Hasan1
1Radiology, UTHSCH, Houston, Texas, United States

A comprehensive investigation of the interplay between microstructural MRI parameters such as T2 relaxation and diffusivites using standardized volume-based methods in both healthy gray and white matter has not been attempted before. Advances in multimodal MRI registration and segmentation methods have enabled the fusion of high resolution T1, diffusion tensor imaging and relaxation maps to a common native space provided by a high resolution T1-weighted volume. This volume can be anatomically labeled using atlases of white and gray matter regionn. In this work, we investigated the interplay between T2 relaxation and the radial diffusivity using 82 brain white matter (WM), gray matter (GM), cortical and subcortical structures. We report, a strong relation between the T2 relaxation and the radial diffusivity in a healthy population.

15:00 4018.   ODF-based morphometry and application to brain asymmetry 
Alvina Goh1, Neda Jahanshad2, Paul M Thompson2, and Christophe Lenglet3
1Department of Mathematics, National University of Singapore, Singapore, Singapore, 2Laboratory of Neuro Imaging, Department of Neurology, UCLA, Los Angeles, CA, United States, 3Department of Radiology - CMRR, University of Minnesota Medical School, Minneapolis, MN, United States

Orientation Distribution Functions (ODFs) are estimated from High Angular Resolution Diffusion Imaging (HARDI) data and provide a great amount of information on the structure of the white matter. We present an extension of voxel-based morphometry to ODFs and apply it to brain asymmetry.

Electronic Posters : Diffusion & Perfusion - Neuro
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Mapping Structural Anisotropy : Novel Contrast

Wednesday May 11th
Exhibition Hall  13:30 - 15:30 Computer 86

13:30 4019.   Diffusion properties of whole, post-mortem human brains 
Karla L Miller1, Charlotte J Stagg1, Saad Jbabdi1, Heidi Johansen-Berg1, and Jennifer A McNab2
1FMRIB Centre, University of Oxford, Oxford, Oxon, United Kingdom, 2A.A. Martinos Center, Massachusetts General Hospital, Boston, MA, United States

Although diffusion imaging is sensitive to tissue microstructure, biological interpretation of diffusion properties is lacking. Imaging of post-mortem brains would enable the comparison of diffusion indices (such as MD and FA) with histological samples. We present results of diffusion imaging in whole, post-mortem human brains. Diffusion properties are significantly different than in vivo and depend on post-mortem interval. However, anisotropy is partially preserved and tractography is possible.

14:00 4020.   White matter fiber orientation mapping based on T2* anisotropy 
Jongho Lee1,2, Peter van Gelderen1, Li-Wei Kuo1, Hellmut Merkle1, Afonso C. Silva3, and Jeff H. Duyn1
1Advanced MRI section/LFMI/NINDS, National Institutes of Health, Bethesda, MD, United States, 2Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States, 3CMU/LFMI/NINDS, National Institutes of Health, Bethesda, MD, United States

The dependence of T2* on fiber orientation relative to B0 was studied in post-mortem human brain tissue. We found an orientation dependence with sin2lower case Greek theta and sin4lower case Greek thetacomponents. This dependence could be accurately explained by a model of microscopic susceptibility variations together with significant susceptibility anisotropy. Based on this, we constructed a T2* orientation map that closely resembled a fiber orientation map derived from DTI.

14:30 4021.   Temporal Alterations in Brain Water Diffusivity in Acute Radiation Injury 
Richa Trivedi1, Hemanth Kumar Bhonsle Somu1, Senthil Veeramani1, Rajendra P Tripathi1, and Subash Khushu1
1Institute of Nuclear Medicine & Allied Sciences, Delhi, Delhi, India

Longitudinal diffusion tensor imaging (DTI) study was performed at baseline, 6 h, 1 day, 3 days and 5 days after a sub-lethal dose irradiation. Decreased MD values were observed in brain parenchyma at 3rd and 5th day compared to baseline study. Initial increase in FA values was observed on moving from 0 hour to 1 day in CC and Ctx followed by a sharp decrease in FA on 3rd and 5th day. No abnormalities were visible on anatomical images. Our results suggest the radiation-induced hypoxic changes in brain parenchyma during acute phase even before conventional MRI.

15:00 4022.   DTI Metrics Differentiate Chronic Infective from Chronic Inflammatory Knee Arthritis 
Rishi Awasthi1, Vikas Agarwal2, Deepak Tripathi2, Vinita Agarwal3, R KS Rathore4, and Rakesh K Gupta1
1Departments of Radiodiagnosis, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India, Lucknow, UP, India, 2Departments of Immunology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India, Lucknow, UP, India, 3Departments of Pathology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India, Lucknow, UP, India, 4Department of Mathematics & Statistics, Indian Institute of Technology, Kanpur, UP

A total of 17 patients (seven detected tubercular and 10 with inflammatory arthritis) were imaged using both conventional and DT MRI. It was found that DTI derived FA and CL of synovial membrane were significantly higher in tubercular arthritis as compared to the non-tubercular ones. In synovial fluid, apart from FA and CL, CP was also significantly higher in tubercular arthritis, while MD values were significantly lower than inflammatory arthritis. We conclude that DTI can non-invasively differentiate between chronic inflammatory arthritis from tubercular arthritis which is of importance in appropriate management of these patients.

Electronic Posters : Diffusion & Perfusion - Neuro
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Mapping Structural Anisotropy : Acquisition & Pipeline
Thursday May 12th
Exhibition Hall  13:30 - 15:30 Computer 87

13:30 4023.   Diffusion weighted MR nerve sheath imaging (DW-NSI) using diffusion-sensitized driven-equiliblium (DSDE) 
Makoto Obara1, Taro Takahara2, Masatoshi Honda3, Thomas Kwee4, Yutaka Imai3, and Marc Van Cauteren1
1Healthcare, Philips Electronics Japan, Minato-ku, Tokyo, Japan, 2Department of Biomedical Engineering, Tokai University School of Engineering, Hiratsuka, Kanagawa, Japan, 3Department of Radiology, Tokai University Hospital, Isehara, Kanagawa, Japan, 4University Medical Center Utrecht, Utrecht, Netherlands

The utility of motion compensation diffusion-sensitized driven-equilibrium (MC-DSDE) for diffusion-weighted nerve sheath imaging (DW-NSI) from the C1 nerve to the T1 nerve was assessed and compared to EPI-DWI and conventional DSDE sequences, in human volunteers, at 3.0T. MC-DSDS was apparently superior to the other sequences, with less distortion and more smoothness, making it an appropriate method for DW-NSI.

14:00 4024.   A Novel Interlaced Sampling Scheme for Multi-Shell q-space Magnetic Resonance Microscopy 
Sharon Portnoy1, Wenxing Ye2, Alireza Entezari2, Stephen J Blackband1,3, and Baba C Vemuri2
1Department of Neuroscience, University of Florida, Gainesville, Florida, United States, 2CISE department, University of Florida, Gainesville, Florida, United States,3National High Magnetic Field Laboratory, Tallahassee, Florida, United States

We propose a novel interlaced multi-shell q-space sampling scheme, which uses two different polyhedra, the icosidodecahedron and rhombic triacontahedron, to determine the sample distributions on odd and even q-shells. Comparison of simulated and acquired MR data shows that the interlaced scheme provides greater accuracy in the reconstruction of the 3D diffusion propagator relative to standard multi-shell schemes. Accuracy of the interlaced method is improved even further by interpolation of q-space samples onto a body-centered-cubic (rather than Cartesian) sampling grid. These techniques provide a significant improvement in our ability to resolve the complex fibre architectures within biological tissues.

14:30 4025.   Development and Evaluation of A Robust and Efficient Computational Pipeline for Track Density Imaging for Use in a Clinical Research Environment 
Cornelius von Morze1, Duan Xu1, and Christopher P Hess1
1Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States

Track density imaging is an exciting new diffusion post-processing method allowing visualization of white matter tracks with super-resolution by counting the number of tractographic tracks traversing each voxel on a fine spatial grid. We have developed a robust and efficient processing pipeline for production of TDI images in a clinical research environment. We have examined the initial results from TDI in a set of normal volunteers and patients at 3T.

15:00 4026.   Gaussian dephasing due to finite gradients in q-space imaging 
Frank Peeters1
1Université Catholique de Louvain, Brussels, Brussels, Belgium

In q-space imaging (QSI), the averaged propagator of the diffusion process can be obtained in terms of a Fourier transform of the MR-signal. However, a fundamental assumption in QSI is the so-called short gradient approximation (SGA) which can not be satisfied on clinical scanners. We present a new formalism for QSI that is also valid for long duration diffusion encoding gradients. It starts from the basic equations for Brownian motion and magnetic field gradient encoding. The general solution of the resulting Fokker-Planck equation shows that the effect of finite gradients is to make the propagator more Gaussian.

Electronic Posters : Diffusion & Perfusion - Neuro
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Integrated Software Packages

Tuesday May 10th
Exhibition Hall  13:30 - 15:30 Computer 88

13:30 4027.   Accelerating Diffusion Tensor Estimation Using General-Purpose Graphics Processing Unit 
Lin-Ching Chang1, and Mikhail A Gorbachev1
1Department of Electrical Engineering and Computer Science, The Catholic University of America, Washington, DC, United States

A general-purpose graphics processing unit (GPU) offers a powerful processing platform to accelerate non-graphics applications such as tensor estimation in Diffusion Tensor Imaging (DTI). Diffusion tensor maps are computed on a voxel-by-voxel basis by fitting the signal intensities of diffusion weighted images as a function of their corresponding b-matrices. This computation can be significantly accelerated by using the GPU. This study presents the application of using GPU hardware in diffusion tensor estimation by accelerating the weighted multivariate linear regression. The results are tested in simulated 3D brain dataset and show faster computation time against the CPU. The proposed GPU framework can accelerate DTI simulation and can be readily applied to quantitative assessment of the DTI using bootstrap analysis.

14:00 4028.   Diffusion Imaging in the Medical Imaging Interaction Toolkit (MITK) 
Klaus Hermann Fritzsche1, Marco Nolden1, Hans-Peter Meinzer1, and Bram Stieltjes1
1German Cancer Research Center, Heidelberg, Baden Württemberg, Germany

Q-ball imaging provides insights into aspects of brain structure in living humans that could not be studied previously. The lack of standardized and extensible software tools for I/O, reconstruction, interactive visualization, and statistics impedes development and sustainable evaluation. The diffusion imaging component of the Medical Imaging Interaction Framework (MITK-DI) aims at supporting cutting edge diffusion imaging techniques and, in contrast to most other frameworks, addresses all aspects of application design including full integration into an application platform and fluent workflows. MITK-DI therefore allows covering the complete cycle from raw-data to post processing and statistics.

14:30 4029.   Extendable Multimodality Imaging Framework with specific illustration of DTI 
Divya Kishore Singh Rathore1, Sanjay K Verma2, RKS Rathore2, and Rakesh K Gupta3
1Imaging R&D, ADISL, Kanpur, UP, India, 2Mathematics and Statistics, Indian Institute of Technology, Kanpur, UP, India, 3Departments of Radiodiagnosis, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, UP, India

A worldwide interest in research and development in MRI and associated protocols over last 2 decades has generated a need for tools and frameworks that allow rapid development of research grade software. We propose a plugin based architecture that allows developers to add extended support for various file-formats, post-processing algorithms, re-use of existing libraries etc. As an illustration, we are describing a plugin for post-processing of Diffusion Tensor MRI data.

15:00 4030.   DTI Processing and Analysis with MedINRIA 
Pierre Fillard1, and Nicolas Toussaint2
1Parietal Reseach Team, INRIA Saclay Île-de-France, Gif/Yvette, France, 2Imaging Sciences, King's College London, London, United Kingdom

Although the MedINRIA software has been out for three years, it has never been formally presented to ISMRM. The objective of this work is to expose the rationale for the development of MedINRIA, a clinically oriented medical image processing software, and to present its DTI functionalities. We present the processing pipeline going from DICOM to fiber tracts. Notably, it comprises a unique anisotropic smoothing procedure enhancing the tensor quality of datasets of moderate SNR typical of clinical data. We further describe the interactive tract-of-interest selection tool and show an example of fiber bundle extraction and analysis with MedINRIA.