Tutorials

This example shows how you can create gradient tables and sphere objects using DIPY.

We show how to extract brain information and mask from a b0 image using DIPY’s segment.mask module.

Computing the Signal-to-Noise-Ratio (SNR) of DW images is still an open question, as SNR depends on the white matter structure of interest as well as the gradient direction corresponding to each DWI.

Often in imaging it is common to reslice images in different resolutions. Especially in dMRI we usually want images with isotropic voxel size as they facilitate most tractography algorithms. In this example we show how you can reslice a dMRI dataset to have isotropic voxel size.

During a dMRI acquisition, the subject motion inevitable. This motion implies a misalignment between N volumes on a dMRI dataset. A common way to solve this issue is to perform a registration on each acquired volume to a reference b = 0. [JenkinsonSmith01]

This example shows how to use Constrained Spherical Deconvolution (CSD) introduced by Tournier et al. [Tournier2007].

This example shows how to use Multi-Shell Multi-Tissue Constrained Spherical Deconvolution (MSMT-CSD) introduced by Tournier et al. [Jeurissen2014]. This tutorial goes through the steps involved in implementing the method.

We show how to obtain a voxel specific response function in the form of axially symmetric tensor and the fODF using the FORECAST model from [Anderson2005] , [Kaden2016] and [Zucchelli2017].

We show how to model the diffusion signal as a linear combination of continuous functions from the SHORE basis [Merlet2013], [Özarslan2008], [Özarslan2009]. We also compute the analytical Orientation Distribution Function (ODF).

We show how to calculate two SHORE-based scalar maps: return to origin probability (RTOP) [Descoteaux2011] and mean square displacement (MSD) [Wu2007], [Wu2008] on your data. SHORE can be used with any multiple b-value dataset like multi-shell or DSI.

We show how to model the diffusion signal as a linear combination of continuous functions from the MAP-MRI basis [Ozarslan2013]. This continuous representation allows for the computation of many properties of both the signal and diffusion propagator.

Effective representation of the four-dimensional diffusion MRI signal – varying over three-dimensional q-space and diffusion time – is a sought-after and still unsolved challenge in diffusion MRI (dMRI). We propose a functional basis approach that is specifically designed to represent the dMRI signal in this qtau-space [Fick2017]. Following recent terminology, we refer to our qtau-functional basis as \(q\tau\)-dMRI. We use GraphNet regularization – imposing both signal smoothness and sparsity – to drastically reduce the number of diffusion-weighted images (DWIs) that is needed to represent the dMRI signal in the qtau-space. As the main contribution, \(q\tau\)-dMRI provides the framework to – without making biophysical assumptions – represent the \(q\tau\)-space signal and estimate time-dependent q-space indices (\(q\tau\)-indices), providing a new means for studying diffusion in nervous tissue. \(q\tau\)-dMRI is the first of its kind in being specifically designed to provide open interpretation of the \(q\tau\)-diffusion signal.

The diffusion tensor model is a model that describes the diffusion within a voxel. First proposed by Basser and colleagues [Basser1994], it has been very influential in demonstrating the utility of diffusion MRI in characterizing the micro-structure of white matter tissue and of the biophysical properties of tissue, inferred from local diffusion properties and it is still very commonly used.

The diffusion tensor model takes into account certain kinds of noise (thermal), but not other kinds, such as “physiological” noise. For example, if a subject moves during acquisition of one of the diffusion-weighted samples, this might have a substantial effect on the parameters of the tensor fit calculated in all voxels in the brain for that subject. One of the pernicious consequences of this is that it can lead to wrong interpretation of group differences. For example, some groups of participants (e.g. young children, patient groups, etc.) are particularly prone to motion and differences in tensor parameters and derived statistics (such as FA) due to motion would be confounded with actual differences in the physical properties of the white matter. An example of this was shown in a paper by Yendiki et al. [Yendiki2013].

As shown previously (see Reconstruction of the diffusion signal with the Tensor model), the diffusion tensor model is a simple way to characterize diffusion anisotropy. However, in regions near the ventricles and parenchyma, anisotropy can be underestimated by partial volume effects of the cerebral spinal fluid (CSF). This free water contamination can particularly corrupt Diffusion Tensor Imaging analysis of microstructural changes when different groups of subjects show different brain morphology (e.g. brain ventricle enlargement associated with brain tissue atrophy that occurs in several brain pathologies and aging).

The diffusion kurtosis model is an expansion of the diffusion tensor model (see Reconstruction of the diffusion signal with the Tensor model). In addition to the diffusion tensor (DT), the diffusion kurtosis model quantifies the degree to which water diffusion in biological tissues is non-Gaussian using the kurtosis tensor (KT) [Jensen2005].

Diffusion Kurtosis Imaging (DKI) is one of the conventional ways to estimate the degree of non-Gaussian diffusion (see Reconstruction of the diffusion signal with the kurtosis tensor model) [Jensen2005]. However, a limitation of DKI is that its measures are highly sensitive to noise and image artefacts. For instance, due to the low radial diffusivities, standard kurtosis estimates in regions of well-aligned voxel may be corrupted by implausible low or even negative values.

DKI can also be used to derive concrete biophysical parameters by applying microstructural models to DT and KT estimated from DKI. For instance, Fieremans et al. [Fierem2011] showed that DKI can be used to estimate the contribution of hindered and restricted diffusion for well-aligned fibers - a model that was later referred to as the white matter tract integrity WMTI technique [Fierem2013]. The two tensors of WMTI can be also interpreted as the influences of intra- and extra-cellular compartments and can be used to estimate the axonal volume fraction and diffusion extra-cellular tortuosity. According to previous studies [Fierem2012] [Fierem2013], these latter measures can be used to distinguish processes of axonal loss from processes of myelin degeneration.

We show how to apply a Constant Solid Angle ODF (Q-Ball) model from Aganj et al. [Aganj2010] to your datasets.

We show how to apply Diffusion Spectrum Imaging [Wedeen08] to diffusion MRI datasets of Cartesian keyhole diffusion gradients.

We show how to calculate two DSI-based scalar maps: return to origin probability (RTOP) [Descoteaux2011] and mean square displacement (MSD) [Wu2007], [Wu2008] on your dataset.

We show how to apply Generalized Q-Sampling Imaging [Yeh2010] to diffusion MRI datasets. You can think of GQI as an analytical version of DSI orientation distribution function (ODF) (Garyfallidis, PhD thesis, 2012).

An alternative method to DSI is the method proposed by [Canales10] which is called DSI with Deconvolution. This algorithm is using Lucy-Richardson deconvolution in the diffusion propagator with the goal to create sharper ODFs with higher angular resolution.

In this example, we will use the Sparse Fascicle Model (SFM) [Rokem2015], to reconstruct the fiber Orientation Distribution Function (fODF) in every voxel.

The intravoxel incoherent motion (IVIM) model describes diffusion and perfusion in the signal acquired with a diffusion MRI sequence that contains multiple low b-values. The IVIM model can be understood as an adaptation of the work of Stejskal and Tanner [Stejskal65] in biological tissue, and was proposed by Le Bihan [LeBihan84]. The model assumes two compartments: a slow moving compartment, where particles diffuse in a Brownian fashion as a consequence of thermal energy, and a fast moving compartment (the vascular compartment), where blood moves as a consequence of a pressure gradient. In the first compartment, the diffusion coefficient is \(\mathbf{D}\) while in the second compartment, a pseudo diffusion term \(\mathbf{D^*}\) is introduced that describes the displacement of the blood elements in an assumed randomly laid out vascular network, at the macroscopic level. According to [LeBihan84], \(\mathbf{D^*}\) is greater than \(\mathbf{D}\).

Different models of diffusion MRI can be compared based on their accuracy in fitting the diffusion signal. Here, we demonstrate this by comparing two models: the diffusion tensor model (DTI) and Constrained Spherical Deconvolution (CSD). These models differ from each other substantially. DTI approximates the diffusion pattern as a 3D Gaussian distribution, and has only 6 free parameters. CSD, on the other hand, fits many more parameters. The models aare also not nested, so they cannot be compared using the log-likelihood ratio.

This example shows how you can use a spherical harmonics (SH) function to reconstruct any spherical function using DIPY. In order to generate a signal, we will need to generate an evenly distributed sphere. Let’s import some standard packages.

Q-space trajectory imaging (QTI) by Westin et al. [1] is a general framework for analyzing diffusion-weighted MRI data acquired with tensor-valued diffusion encoding. This tutorial briefly summarizes the theory and provides an example of how to estimate the diffusion and covariance tensors using DIPY.

Q-space trajectory imaging (QTI) [1] with applied positivity constraints (QTI+) is an estimation framework proposed by Herberthson et al. [2] which enforces necessary constraints during the estimation of the QTI model parameters.

This example shows how to use RUMBA-SD to reconstruct fiber orientation density functions (fODFs). This model was introduced by Canales-Rodriguez et al [CanalesRodriguez2015].

This example explains how to compute an affine transformation to register two 3D volumes by maximization of their Mutual Information [Mattes03]. The optimization strategy is similar to that implemented in ANTS [Avants11].

This example explains how to compute a transformation to register two 3D volumes by maximization of their Mutual Information [Mattes03]. The optimization strategy is similar to that implemented in ANTS [Avants11].

This example explains how to register 2D images using the Symmetric Normalization (SyN) algorithm proposed by Avants et al. [Avants09] (also implemented in the ANTs software [Avants11])

This example explains how to register 3D volumes using the Symmetric Normalization (SyN) algorithm proposed by Avants et al. [Avants09] (also implemented in the ANTs software [Avants11])

This example demonstrates registration of a binary and a fuzzy image. This could be seen as aligning a fuzzy (sensed) image to a binary (e.g., template) image.

This example explains how you can register two bundles from two different subjects directly in the space of streamlines [Garyfallidis15], [Garyfallidis14].

This example explains how to coregister a set of bundles to a common space that is not biased by any specific bundle. This is useful when we want to align all the bundles but do not have a target reference space defined by an atlas.

This example shows how to register streamlines into a template space by applying non-rigid deformations.

This example explains how we can use QuickBundles [Garyfallidis12] to simplify/cluster streamlines.

QuickBundles [Garyfallidis12] is a flexible algorithm that requires only a distance metric and an adjacency threshold to perform clustering. There is a wide variety of metrics that could be used to cluster streamlines.

This page lists available features that can be used by the tractography clustering framework. For every feature a brief description is provided explaining: what it does, when it’s useful and how to use it. If you are not familiar with the tractography clustering framework, read the Clustering framework first.

This page lists available metrics that can be used by the tractography clustering framework. For every metric a brief description is provided explaining: what it does, when it’s useful and how to use it. If you are not familiar with the tractography clustering framework, check this tutorial Clustering framework.

We show how to extract brain information and mask from a b0 image using DIPY’s segment.mask module.

This example explains how to segment a T1-weighted structural image by using Bayesian formulation. The observation model (likelihood term) is defined as a Gaussian distribution and a Markov Random Field (MRF) is used to model the a priori probability of context-dependent patterns of different tissue types of the brain. Expectation Maximization and Iterated Conditional Modes are used to find the optimal solution. Similar algorithms have been proposed by Zhang et al. [Zhang2001] and Avants et al. [Avants2011] available in FAST-FSL and ANTS-atropos, respectively.

This example explains how we can use RecoBundles [Garyfallidis17] to extract bundles from tractograms.