Search · four archives
Search · four archives
12 papers · ranked by Valyu relevance
Alexandru V. Avram, Kadharbatcha S. Saleem, Peter J. Basser
Diffusion MRI studies with resolutions of a few hundred micrometers have consistently shown that in the cortex water diffusion occurs preferentially along radial and tangential orientations with respect to the cortical surface, in agreement with histology. These dominant orientations do not change significantly even if…
Filip Szczepankiewicz, Malwina Molendowska, Samo Lasič, Marcella E Safi + 7 more
Tensor-valued diffusion encoding employs gradient waveforms that enable unique sensitivity to microstructural features of tissue, but the interpretation of signal and parameters may be confounded by diffusion-time dependence. We introduce a framework for restriction-weighted q-space trajectory imaging (ResQ) that…
Maxime Yon, Omar Narvaez, Jan Martin, Hong Jiang + 5 more
Time- or frequency-dependent (“restricted”) diffusion potentially provides useful information about cellular-scale structures in the brain but is challenging to interpret because of the intravoxel tissue heterogeneity. Frequency-dependence was recently incorporated in a multidimensional diffusion-relaxation correlation…
Mingyao Liang, Jiangyu Yuan, Chaogang Tang, Pingfu Wang + 7 more
Tensor-valued diffusion MRI (dMRI) enables the separation of total kurtosis into isotropic and anisotropic components, offering improved specificity over conventional Diffusion Kurtosis Imaging (DKI). In this study, we introduce a novel framework for detecting anisotropic-isotropic kurtosis mismatch and evaluate its…
Yiang Pan, Yuanjing Feng, Jianzhong He, William Consagra + 3 more
Diffusion MRI (dMRI) enables noninvasive characterization of white-matter fiber orientations and tissue microstructure, but widely used approaches, such as constrained spherical deconvolution (CSD) and parametric multicompartment models, typically address these features separately. The diffusion tensor distribution…
Maxime Yon, Omar Narvaez, Daniel Topgaard, Alejandra Sierra
Massively Multidimensional Diffusion MRI combines tensor-valued encoding, oscillating gradients, and diffusion-relaxation correlation to provide multicomponent sub-voxel parameters depicting the tissue microstructure. This method was successfully implemented ex vivo in micro-imaging systems and in clinical conditions…
Kulam Najmudeen Magdoom, Alexandru V. Avram, Joelle E. Sarlls, Gasbarra Dario + 1 more
Neural tissue microstructure plays an important role in developmental, physiological and pathophysiological processes. Diffusion tensor distribution (DTD) MRI helps probe heterogeneity at the mesoscopic length scale, orders of magnitude smaller than the nominal MRI voxel size, by describing water diffusion within a…
Maryam Afzali, Sam Coveney, Lars Mueller, Sarah Jones + 7 more
Diffusion tensor imaging (DTI) is commonly used in cardiac diffusion magnetic resonance imaging (dMRI). However, the tissue’s microstructure (cells, membranes, etc.) restricts the movement of the water molecules, making the spin displacements deviate from Gaussian behaviour. This effect may be observed with diffusion…
Uzair Hussain, Ali R. Khan
Diffusion MRI (dMRI) is an imaging technique widely used in neuroimaging research, where the signal carries directional information of underlying neuronal fibres, based on the diffusivity of water molecules. One of the shortcomings of dMRI is that numerous images, sampled at gradient directions on a sphere, must be…
Brandon Wong, Brokoslaw Laschowski
Neural decoding can be viewed as a representation learning problem in which neural activity is mapped into an intermediate representation before downstream reconstruction. The choice of intermediate representation influences both performance and learning difficulty. Here we developed a novel framework for studying how…
Tamoghna Chattopadhyay, Chirag Jagad, Rudransh Kush, Vraj Dharmesh Desai + 5 more
Diffusion tensor imaging (DTI) is a key neuroimaging modality for assessing brain tissue microstructure, yet high-quality acquisitions are costly, time-intensive, and prone to artifacts. To address data scarcity and privacy concerns – and to augment the available data for training deep learning methods – synthetic DTI…
Xinyu Ye, Xiaodong Ma, Ziyi Pan, Zhe Zhang + 3 more
to propose a two-step non-local principal component analysis (PCA) method and demonstrate its utility for denoising diffusion tensor MRI (DTI) with a few diffusion directions. A two-step denoising pipeline was implemented to ensure accurate patch selection even with high noise levels and was coupled with data…