HARDI/QSpace Imaging  




Time 
Prog # 

11:00  33. 
QSpace Diffusion Weighted MRI Analyzed with Maximizing Rician
Likelihood Improves Reliability and Tissue Contrast Bennett Allan Landman^{1}, Jonathan Andrew David Farrell^{1},^{ 2}, Seth A. Smith^{2},^{ 3}, Peter C. van Zijl,^{ 23}, Jerry L. Prince^{1} ^{1}Johns Hopkins University, Baltimore, Maryland, USA; ^{2}Kennedy Krieger Institute, Baltimore, Maryland, USA; ^{3}Johns Hopkins University School of Medicine, Baltimore, Maryland, USA Qspace imaging is an emerging diffusion weighted MR imaging technique that shows great promise in classifying changes in tissue microstructure. Unlike diffusion tensor imaging, qspace imaging identifies the molecular diffusion probability density function without the need to assume a Gaussian distribution. Traditional analysis techniques use limited diffusion models (e.g., biexponential) to regularize noisy data and do not properly account for the properties of Rician noise. We present a robust Mestimator for diffusion probability density functions based on maximum likelihood. In simulation and in vivo spinal cord, the method improves reliability of the estimated probability functions and increases tissue contrast. 

11:12  34. 
Computation of Diffusion Function Measures in QSpace Using Magnetic
Resonance Hybrid Diffusion Imaging YuChien Wu^{1},^{ 2}, Aaron S. Field,^{ 23}, Andrew L. Alexander,^{ 23} ^{1}University of WisconsinMadison, Madison , Wisconsin, USA; ^{2}Madison, Wisconsin, USA; ^{3}University of WisconsinMadison, Madison, Wisconsin, USA The distribution of water diffusion in biological tissues may be estimated by a 3D Fourier Transform (FT) of diffusionweighted measurements in qspace. In this study, methods for estimating diffusion spectrum measures (the zerodisplacement probability, the meansquared displacement, and the orientation distribution function) directly from the qspace signals are described. These methods were evaluated using both computer simulations and hybrid diffusion imaging (HYDI) measurements on a human brain. This new direct computation approach reduces HYDI data processing time and image artifacts arising from 3D FT and regridding interpolation. In addition, it is less sensitive to the noise and qspace truncation effects. 

11:24 
35. 
Simple Harmonic Oscillator Based Estimation and Reconstruction for
OneDimensional QSpace MR Evren Ozarslan^{1}, Cheng Guan Koay, Peter J. Basser ^{1}National Institutes of Health, Bethesda, Maryland, USA Dependence of the diffusionweighted MR signal on the diffusion gradient strength was represented as a series of eigenfunctions of the simple harmonic oscillator Hamiltonian. This formulation made it possible to rapidly estimate the ensemble average propagator and potentially useful descriptors such as its moments and returntoorigin probability. The proposed technique has significant advantages over the previously employed biexponential curve fitting and cumulant expansion methods. Unlike these approaches, the simple harmonic oscillator based method is linear, and capable of approximating complicated signal profiles such as nonmonotonic patterns due to diffractionlike effects. 

11:36 
36. 
Analysis of High BValue Diffusion Images Using Fractional Order
Calculus Xiaohong Joe Zhou^{1},^{ 2}, Osama Abdullah^{2}, Dumitru Baleanu^{3}, Richard L. Magin^{2} ^{1}University of Illinois Medical Center, Chicago, Illinois, USA; ^{2}University of Illinois at Chicago, Chicago, Illinois, USA; ^{3}Cankaya University, Ankara, Turkey It is well known that diffusioninduced MR signal loss deviates from monoexponential decay, especially at high bvalues. We demonstrate that the anomalous diffusion behavior can be characterized by extending the BlochTorrey equation through application of the operators of fractional calculus. Theoretical analyses and experimental results have shown that a model for anomalous diffusion can be established by directly solving the fractionalized BlochTorrey equation in time and space. Using this mathematical tool, we may extend the applications of diffusion imaging beyond simply evaluating apparent diffusion coefficients and stretched exponential constant α, and eventually reveal new parameters related to tissue microenvironment. 

11:48  37. 
Towards Better Understanding of Brain Tissue Using Directional
Kurtoses by Orthogonal Transformation of Diffusion Kurtosis Tensor D (K_{D}T) Edward Sai Kam Hui^{1},^{ 2}, Liqun Qi^{2}, Matthew M. Cheung^{1}, Kyle H. Cheng^{1}, Joseph A. Helpern^{3}, Jens H. Jensen^{3}, Ed X. Wu^{1} ^{1}The University of Hong Kong, Pokfulam, Hong Kong; ^{2}The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong; ^{3}New York University School of Medicine, New York, USA Orthogonal transformation of diffusion kurtosis tensor is implemented to obtain axial (K_{//}) and radial kurtosis (K_{^}) which measures kurtosis parallel and perpendicular, respectively, to principal diffusion direction, which could potentially yield extra information regarding diffusion environment in brain tissue. In vivo and ex vivo experiments were performed on normal adult SD rat brains. It was found that fixation has more effect on the intrinsic structure parallel than perpendicular to nerves as depicted by more increase in K_{//} than increase in K_{^} suggesting that directional kurtoses has excellent sensitivity to changes in diffusion environments, and gains superiority to mean kurtosis (MK). 

12:00  38. 
In Vivo HigherOrder Contrast Measured with Generalized
Diffusion Tensor Imaging Using HigherOrder Tensors Chunlei Liu^{1}, Sarah Charlotte Mang^{2}, Michael E. Moseley^{1} ^{1}Stanford University, Stanford, California , USA; ^{2}University of Tuebingen, Tuebingen, Germany The higher order tensor (HOT) model by Liu et al proposes to quantitatively characterize general diffusion processes using a series of diffusion tensors with increasing orders. Each order of these tensors offers a well defined physical interpretation, with the second order tensor meaning the second order cumulant (covariance matrix), the third order tensor meaning the third order cumulant (skewness tensor), and the fourth order tensor meaning the fourth order cumulant (kurtosis tensor). We present a very first in vivo implementation of generalized diffusion tensor imaging (GDTI) with HOT and propose a set of techniques to generate higher order diffusion contrast. 

12:12  39. 
Effect of Diffusion Gradient Pulse Duration on Fibre Orientation
Estimation ChunHung Yeh^{1}, JacquesDonald Tournier^{2},^{ 3}, KuanHung Cho^{1}, ChingPo Lin^{1}, Fernando Calamante^{2},^{ 3}, Alan Connelly^{2},^{ 3} ^{1}Institute of Neuroscience, National YangMing University, Taipei, Taiwan; ^{2}Brain Research Institute, Melbourne, Australia; ^{3}Department of Medicine, University of Melbourne, Melbourne, Australia In the qspace formalism, the short gradient pulse approximation is preferable to determine spin displacement. However, we propose that the application of a longer £_ which is clinically achievable is theoretically beneficial for resolving fibre orientations, as it enhances both the DW signal and the contrast between the DW directions. We thus investigate the relationship between £_ and the DW signal measured as a function of orientation using both simulated and experimental data. The results show that prolonging £_ preferentially enhances the transverse DW signal. This effect is advantageous for estimating fiber orientations and hence for fibertracking applications. 

12:24  40. 
A Recursive Algorithm to Decompose Orientation Distribution Function
and Resolve IntraVoxel Fiber Directions FangCheng Yeh^{1}, Van J. Wedeen^{2}, WenYih Isaac Tseng,^{ 13} ^{1}National Taiwan University College of Medicine, Taipei, Taiwan; ^{2}Harvard Medical School, Charlestown, Massachusetts, USA; ^{3}National Taiwan University Hospital, Taipei, Taiwan A recursive decomposition algorithm for resolving intravoxel fiber directions in diffusion spectrum imaging (DSI) and qball imaging (QBI) is presented. The proposed algorithm recursively decomposes orientation distribution function (ODF) and obtains fiber directions. 

12:36  41. 
Regularized SuperResolution for Diffusion MRI Shahrum NedjatiGilani^{1}, Geoff J M Parker^{2}, Daniel C. Alexander^{1} ^{1}University College London, London, UK; ^{2}University of Manchester, Manchester, UK We present an algorithm that uses regularized superresolution to find fibre populations and their respective orientations and volume fractions accurately from a subvoxel scale in a 3D diffusion MRI acquisition. We can use our method to recover fibre configurations such as bending, fanning, and partial volume effects. 

12:48  42. 
Spatial Normalization of the Fiber Orientation
Distribution Based on High Angular Resolution Diffusion Imaging Data Xin Hong^{1}, Lori Rose Arlinghaus^{1}, Adam W. Anderson^{1} ^{1}Vanderbilt University, Nashville, Tennessee, USA In this study an algorithm is developed to transform the Fiber Orientation Distribution (FOD) function based on HARDI data, which takes into account not only translation, but also rotation, scaling, and shearing effects of the spatial transformation. The algorithm is tested using both numerical phantom and in vivo human data. This technique makes it possible to compare the intravoxel fiber distribution between subjects, which will be helpful in various clinical studies of white matter diseases. 
