• Graduate students in Physics, Chemistry, Mathematics, Computing, or related subjects

Magnetic resonance imaging developments for neuroscience applications

[Opportunities at the Brain Research Institute

Preferred areas of expertise[

Magnetic resonance imaging (MRI) development work at the Brain Research Institute takes place in a highly multi-disciplinary environment. The MRI development research group works in conjunction with expert colleagues from a number of disciplines, including in particular neurology, neuropsychology, and neuroinformatics, as part of a larger neurosciences effort directed at improving the understanding and treatment of epilepsy and stroke, as well as the functional consequences of these conditions.

As part of this multi-disciplinary team, students would have the opportunity to further develop techniques designed to image not only the structure of the brain, but also various aspects of the brain’s function. For example, the current principal interests of the MR development group are in improving the imaging of:

(1) the diffusion properties of water within brain tissue, and (2) the blood flow at the capillary level (brain perfusion)

Perfusion Imaging[

MRI can be sensitised to the random microscopic movement (Brownian motion) of water within brain tissue. This technique has already provided important new information in the investigation of several neurological conditions, including epilepsy, stroke, and Alzheimer’s disease.

The white matter of the brain consists of densely packed bundles of fibres, which connect the cell bodies both to other regions of the brain and to other parts of the body. As a result, water diffusivity within the white matter has been found to be highly anisotropic (ie directionally dependent).

The mathematical model most commonly used to describe this process is a diffusion tensor, with its principal eigenvector oriented along the direction of the fibres. This information can in principle be used to identify connectivity pathways within the central nervous system (CNS), which has important implications for many aspects of neuroscience.

However, there are many regions within the CNS where the tensor model is inadequate. In particular, when an image pixel contains more than one fibre orientation, the single direction determined from the tensor model will not in general correspond to any of the true fibre directions.

Our group has been working on developing an improved framework to circumvent the limitations of the tensor model. This is based on the concept of spherical deconvolution, and provides an estimate of the distribution of fibre orientations.

Perfusion of the brain (i.e. blood flow at the capillary level where exchange of oxygen and nutrients takes place) is essential for the viability of brain tissue.

MRI can be used to quantify perfusion in a number of approaches, each of which requires the blood flowing into the brain to be labelled in some way. The two main approaches to this involve (a) magnetically labelling the blood itself (arterial spin labelling), or (b) the injection of a bolus of a paramagnetic contrast agent (bolus tracking). These techniques have again been shown to be useful both for clinical applications (such as in patients with stroke), and to study brain function.

We have worked on aspects of both techniques for many years, in improving both the image acquisition and the modelling required for perfusion quantification. In particular, bolus tracking requires an estimate of the time dependent blood supply to each pixel in the image (the arterial input function or AIF). For a number of practical reasons, the AIF is commonly estimated from a pixel within a major artery distant from the tissue of interest. Our work has shown that this is an important source of error in quantifying perfusion. To address this issue, we have been developing a method to calculate a local AIF, using independent component analysis (ICA).

Brain Research Institute

Introduction[

Diffusion Weighted Imaging (DWI)[

Enquiries[If you are interested in these or other projects, please contact:

Prof Alan ConnellyBrain Research Institute and The University of Melbourne.Address: Brain Research Institute, Ground Floor, Neurosciences Building, Austin Health, Banksia Street, Heidelberg Heights, Victoria 3081, Australia.Telephone: (+61 3) 9496-4389 Email: a.connelly@brain.org.au

Magnetic resonance research projects at the BRI [The Brain Research Institute offers students the opportunity to work within a team of experienced magnetic resonance development physicists in a highly multi-disciplinary environment dedicated to the study of neuroscience. Post-graduate research projects in areas similar to those described above require suitably qualified graduate students to contribute to the development of techniques that will be used to further our programme of neuroscience research. This environment is rare in being able to offer expert supervision in a number of areas (including MRI physics, neuroinformatics, neurology, neuropsychology) that are essential to the production of high quality research work directed towards the technical developments of immediate relevance to clinical and neuroscientific problems.

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R(θ) F(θ,φ) = S(θ,φ)

Illustration of the spherical deconvolution method. The response function R(θ) describes the diffusion signal measured for a typical fibre bundle aligned with the z-axis. The function F(θ,φ) describes the distribution of fibre orientations within the pixel. The diffusion signal S(θ,φ) measured during the experiment is given by the spherical convolution of R(θ) with F(θ,φ). F(θ,φ) can therefore be estimated by performing the spherical deconvolution of R(θ) from S(θ,φ).

Results obtained using spherical deconvolution in the brainstem, as highlighted in the insert. The two main fibre bundles in this region can readily be identified. The corticospinal tract (blue lobes) connects the brain motor regions to the rest of the body and runs down to the spine. The cross-pontine fibres (red lobes) establish connections between the two sides of the cerebellum, and feed into the middle cerebellar peduncles (the green lobes on either sides of the image).

highprobability

lowprobability

The fibre orientation distribution provided by spherical deconvolution can be used to ‘track’ the path of white matter fibre bundles. This is commonly done by following the direction of the fibres in 3D. Shown here are connectivity maps for the start-point highlighted by the arrow, placed within the corticospinal tract. The probability of connection from this point is indicated by the colour, overlaid on 16 coronal slices.

Sequential images during the passage of a bolus of contrast agent; the bolus of contrast agent induces a transient drop in signal intensity. The time dependent contrast agent concentration can be calculated from the signal intensity changes, and is proportional to brain perfusion and the AIF. Perfusion can be calculated by solving the ill-conditioned inverse problem.

Middle Cerebral

Artery

Middle Cerebral

Artery

Internal CarotidArtery

Internal CarotidArtery

VertebralArteries

Image pixel AIF t =AIF est t ⊗VTF t

Left: model of the brain vasculature showing the major cerebral arteries (in red), and their corresponding arterial trees. The yellow cube indicates a typical image pixel. The AIF should be measured in the input to that pixel but is commonly estimated (AIFest) from a major artery. The true AIF in the pixel can be expressed as the mathematical convolution of the estimated arterial input function and the vascular transport function (VTF: probability distribution of vascular transit time).

Perfusion image using the local AIF(new approach)

Perfusion image using the global AIF

(traditional approach) Errors in perfusion quantification. The use of an erroneous AIF in patients with vascular abnormalities (as in the example in the figure) leads to a severe overestimation of the perfusion abnormality. This can have dangerous consequences in the management of patients with stroke. Perfusion image calculated using (left) the novel local AIF method, and (right) using the traditional AIF method (estimated from a major artery).