![]() # recommended basis orders are radial_order=6 and time_order=2. Qspace architecture zip#linspace ( tau_min, tau_max, 5 ) qtdmri_fits = msds = rtops = rtaps = rtpps = for i, ( data_, mask_, gtab_ ) in enumerate ( zip ( data, cc_masks, gtabs )): # select the corpus callsoum voxel for every dataset cc_voxels = data_ # initialize the qt-dMRI model. (although this should be done with caution). This including points beyond the dataset’s maximum \(q\tau\) value \(q\tau\)-space, and any \(q\tau\)-position can be freely recovered. The basis is fitted to the data, its coefficients describe the the entire Noted that qt-dMRI’s combined smoothness and sparsity regularization allowsįor smooth interpolation at any \(q\tau\) position. Time, so we estimate \(q\tau\) indices between the minimum and maximumĭiffusion times of the data at 5 equally spaced points. In this example we don’t extrapolate the data beyond the maximum diffusion Return-to-Plane Probability (RTOP, RTAP and RTPP), as well as the Mean Squared In particular, we estimate the Return-to-Original, Return-to-Axis and (q:math: tau-indices) for the masked voxels in the corpus callosum of each dataset. Next, we use qt-dMRI to estimate of time-dependent q-space indices savefig ( 'qt-dMRI_datasets_fa_with_ccmasks.png' ) imshow ( mask_template, origin = 'lower', interpolation = 'nearest' ) plt. imshow ( fa, cmap = 'Greys_r', origin = 'lower', interpolation = 'nearest' ) plt. title ( subplot_titles, fontsize = 15 ) plt. r_ # produce the FA images with corpus callosum masks. fa # set mask color to green with 0.5 opacity as overlay mask_template = np. fit ( data_middle_slice, data_middle_slice > 0 ) fa = tenfit. subplots ( nrows = 2, ncols = 2 ) for i, ( data_, mask_, gtab_ ) in enumerate ( zip ( data, cc_masks, gtabs )): # take the middle slice data_middle_slice = data_ mask_middle_slice = mask_ # estimate fractional anisotropy (FA) for this slice tenmod = dti. Time-dependent q-space indices from a \(q\tau\)-acquisition of a mouse. Qspace architecture how to#In this example we illustrate how to use the \(q\tau\)-dMRI to estimate Return To the Plane Probability (RTPP) are available, as well as the ![]() Alsoĭirectional indices such as the Return To the Axis Probability (RTAP) and Inverse Variance (QIV) and Return-To-Origin Probability (RTOP). Invariant quantities such as the Mean Squared Displacement (MSD), Q-space \(q\tau\)-dMRI can be seen as a time-dependent extension of the MAP-MRIįunctional basis, and all the previously proposed q-spaceĬan be estimated for any diffusion time. Specifically designed to provide open interpretation of the \(q\tau\)-dMRI is the first of its kind in being ( \(q\tau\)-indices), providing a new means for studying diffusion in \(q\tau\)-space signal and estimate time-dependent q-space indices ![]() The framework to – without making biophysical assumptions – represent the As the main contribution, \(q\tau\)-dMRI provides Number of diffusion-weighted images (DWIs) that is needed to represent the dMRI Imposing both signal smoothness and sparsity – to drastically reduce the Following recent terminology, we refer to our We propose a functionalīasis approach that is specifically designed to represent the dMRI signal in Varying over three-dimensional q-space and diffusion time – is a sought-afterĪnd still unsolved challenge in diffusion MRI (dMRI). Estimating diffusion time dependent q-space indices using qt-dMRIĮffective representation of the four-dimensional diffusion MRI signal – ![]()
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