Diffusion Analysis

Thursday 15 May 2014

Flavio Dell'Acqua^1,2, Luis Lacerda¹, Marco Catani³, and Andrew Simmons^1,2
1Dept of Neuroimaging, King's College London, Institute of Psychiatry, London, United Kingdom, ²NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia, King's College London, Institute of Psychiatry, London, United Kingdom, ³Dept of Forensics and Neurodevelopmental Sciences, King's College London, Institute of Psychiatry, London, United Kingdom

All the anisotropy information in a HARDI signal is captured by the spherical harmonic coefficients of even order l ≥ 2. By measuring the total power of these coefficients it is possible to obtain an absolute measure of how much anisotropy information is available in each HARDI signal or a measure of the total anisotropic power (AP). This index can be use to better quantify and discriminate low anisotropy regions from CSF regions or noise. This measure is entirely model independent, robust to noise and can be easily applied on a large number of existing datasets.

David Raffelt¹, Robert E Smith¹, Donald Tournier^1,2, David Vaughan^1,3, Graeme Jackson^1,3, and Alan Connelly^1,2
1Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia, ²Department of Medicine, University of Melbourne, Melbourne, VIC, Australia, ³Department of Neurology, Austin Health, Melbourne, VIC, Australia

When investigating morphometric white matter differences between populations, the direction of expansion or contraction relative to the underlying fibre tract is important. Expansion or contraction parallel to the fibre orientation implies a difference in axon length, while a change to the cross sectional area (CSA) in the perpendicular plane implies a difference in the number of axons and is therefore more relevant. We propose a method that uses Fibre Orientation Distributions to resolve multiple populations of fibres within each voxel (‘fixels’). We perform whole-brain statistical comparisons of the change in fixel CSA across groups in a method called fixel-based morphometry.

Neda Sadeghi¹, John H Gilmore², Weili Lin³, and Guido Gerig^1
1Scientific Computing and Imaging Institute, Salt Lake City, UT, United States, ²Department of Psychiatry, University of North Carolina, Chapel Hill, NC, United States, ³Department of Radiology, University of North Carolina, Chapel Hill, NC, United States

Early brain development is characterized by rapid organization and structuring of brain tissue. Magnetic Resonance diffusion tensor imaging (MR-DTI) can capture these changes non-invasively by following individuals longitudinally to better understand departures from normal brain development in neurological disorders or disease. We present analysis and modeling of neurodevelopmental growth trajectories from longitudinal infant DTI using recently developed image processing and statistical modeling tools. Comparing populations of healthy singleton and twin subjects, we find subtle group differences in axial diffusivity at birth, which disappear after 2-3 months. Color-coded 3D visualizations reveal large variability of these differences across white matter regions.

Huaiqiang Sun¹, Haoynag Xing¹, Chunlin Wang¹, Xianglong Li², Jin Li², and Qiyong Gong^1
1Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China, ²Regenerative Medicine Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China

An unbiased tensor-based image registration scheme was integrated into conventional Tract-Based Spatial Statistics to longitudinally monitor the dynamic of white matter reorganization in a primate permanent ischemic stroke model. Our findings suggest that white matter reorganization occurs in chronic phase of stroke and shows a dynamic pattern.

Eleftherios Garyfallidis¹, Demian Wassermann², and Maxime Descoteaux^1
1University of Sherbrooke, Sherbrooke, Quebec, Canada, ²Harvard Medical School, MA, United States

We created a novel method that allows to investigate bundles for detailed group comparisons using streamline distances. This method opens the door to calculating group statistics beyond standard templates and average brains.

Paulo Rodrigues¹, Alberto Prats-Galino², David Gallardo-Pujol³, Pablo Villoslada⁴, Carles Falcon⁵, and Vesna Prčkovska^4
1Mint Labs S.L., Barcelona, Spain, ²LSNA, Facultat de Medicina, University of Barcelona, Barcelona, Spain, ³Dept. of Personality, Faculty of Psychology, University of Barcelona, Barcelona, Spain, ⁴Center for Neuroimmunology, Department of Neurosciences, IDIBAPS, Hospital Clinic, Barcelona, Spain, ⁵GIB-UB, CIBER-BBN, Barcelona, Spain

Connectomics', studies have gained significant importance over the last years. Especially they are becoming popular in clinical studies where the connectivity matrices are used to classify among patients and healthy controls or predict the course of the diseases (neurodegenerative diseases). In functional connectomics, from RS-fMRI, the variability and reproducibility has been researched. In structural connectomics however, there are many different approaches for acquisition, modeling and choice for tractography algorithms and the variability of the structural connectome has not yet been properly investigated. These types of studies are very important pre-requisite for using these techniques when working with clinical data.

Christopher S Parker^1,2, Fani Deligianni², M. Jorge Cardoso¹, Pankaj Daga¹, Marc Modat¹, Chris A Clark², Sebastien Ourselin^1,3, and Jonathan D Clayden^2
1Centre for Medical Image Computing, UCL, London, United Kingdom, ²Imaging and Biophysics Unit, UCL, London, United Kingdom, ³Dementia Research Centre, UCL, London, United Kingdom

A variety of image processing pipelines have been used to reconstruct whole-brain structural connectivity networks from diffusion MRI data. The choice of reconstruction method can impact network topology measures. We assessed similarity in networks obtained using two alternative and independent state-of-the-art reconstruction pipelines in order to identify core connections emerging robustly in both. We found high convergence between group-averaged networks across a range of network densities and identified a ‘consensus network’, which had high convergence and anatomical plausibility. Future work will investigate convergence using finer node scale parcellations, allowing a more detailed analysis of the convergence structure.

Myung-Ho In¹, Oleg Posnansky¹, and Oliver Speck^1,2
1Biomedical Magnetic Resonance, Otto-von-Guericke University, Magdeburg, Germany, Magdeburg, Germany, ²Leibniz Institute for Neurobiology, Magdeburg, Germany, Magdeburg, Germany

A well-known problem in echo-planar imaging (EPI) is geometric distortion due to field inhomogeneities induced by susceptibility effects. In diffusion weighted EPI (DW-EPI), in addition, the distortions vary due to eddy-currents from diffusion gradients. To correct both susceptibility and eddy-current induced distortions, a comprehensive approach using the point spread function (PSF) mapping based methods is presented. An extended PSF method with a reversed gradient approach suggested recently was adapted for susceptibility-induced distortion correction, and a fast PSF-based eddy-current calibration method is newly suggested in this study. The combination of the methods thus allows distortion-free DW-EPIs without loss of spatial resolution.

Ria Mazumder^1,2, Bradley D. Clymer¹, Richard D. White^2,3, and Arunark Kolipaka^2,3
1Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, United States, ²Department of Radiology, The Ohio State University, Columbus, OH, United States, ³Department of Internal Medicine-Division of Cardiology, The Ohio State University, Columbus, OH, United States

The counter-directional helical structure of the heart plays an important role in regulating cardiac mechanics and can be quantified on the basis of the angle it subtends known as helical angle (HA). Non-invasive measurement of HA is possible using diffusion tensor imaging, the accuracy of which is dependent on increased diffusion encoding directions (DED) and high signal to noise ratio (i.e. increase in averages). The purpose this study is to robustly estimate HA using fewer averages and DED (thereby, reducing the acquisition time) by implementing a 3D adaptive anisotropic Gaussian post-processing filter.

Mei-Lan Chu^1,2, Hing-Chiu Chang², and Nan-Kuei Chen^2,3
1Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, Taiwan, ²Brain Imaging and Analysis Center, Duke University, Durham, North Carolina, United States, ³Department of Radiology, Duke University, Durham, North Carolina, United States

Diffusion-weighted imaging (DWI) data acquired with interleaved EPI sequence are highly susceptible to inter-segment phase variations and aliasing artifacts induced by motion in the presence of strong diffusion weighting gradients. Here we report a novel and efficient approach to remove aliasing artifacts in interleaved EPI based high-resolution DWI data through developing a MUSE algorithm based on projection onto convex set (POCS) 2. The new technique, termed POCSMUSE, can provide high-quality image without navigator echo, and can be generally applied to DWI data acquired with arbitrary k-space trajectory.