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CMIC Seminar: Microstructure imaging in practice: Dominique Drai

October 16, 2013 @ 12:00 pm - 1:00 pm

I will be describing 3 mains projects and a quick overview of some side projects:

1. Axon radius estimation with Oscillating Gradient Spin Echo (OGSE) Diffusion MRI.

The estimation of axon radius provides insights into brain function and could provide progression and classification biomarkers for a number of white matter diseases. A recent in silico study has shown that optimised gradient waveforms (GEN) and oscillating gradient waveform spin echo (OGSE) have increased sensitivity to small axon radius compared to pulsed gradient spin echo (PGSE) diffusion MR sequences. In a follow-up study, experiments with glass capillaries show the practical feasibility of GEN sequences and verify improved pore-size estimates. Here, we compare PGSE with sine, sine with arbitrary phase, and square wave OGSE (SNOGSE, SPOGSE, SWOGSE, respectively) for axon radius mapping in the corpus callosum of a rat, ex-vivo. Our results suggest improvements in pore size estimates from OGSE over PGSE, with greatest improvement from SWOGSE, supporting theoretical results from and other studies.

2. Measuring tissue membrane permeability using diffusion-diffusion exchange spectroscopic imaging.

The permeability of membranes within tissue microstructure is abnormal in a number of pathologies, for example in cancer and stroke. We demonstrate the use of diffusion-diffusion exchange spectroscopy in yeast and diffusion-diffusion exchange spectroscopic imaging (DEXSI) in an ex-vivo rat brain on a 9.4T Agilent small bore scanner.

We adapt the Diffusion-diffusion Exchange Spectroscopy (DEXSY) NMR technique for MRI of biological samples to quantify permeability. Previous studies that estimate permeability have adapted the Karger framework using biophysical models of tissue. An alternative, phenomenological approach, Filter Exchange Imaging (FEXI) has been used to quantify permeability in the human brain. The FEXI study assumed a two site system and that the rate of exchange between sites was mono-exponential. A recent study suggests white matter can be better described by at least three compartments. Diffusion-diffusion exchange techniques are inherently able to detect multiple diffusivities and thus the exchange of water between them. Future work will decrease total acquisition time by fast imaging and optimising the diffusion protocol.

3. In vivo neurite orientation dispersion and density imaging (NODDI) MRI in mouse brain.

Monitoring the progress of neuronal reorganization in vivo is a key requirement in understanding the progression of disease and determining therapeutic efficacy in neurological disorders. Current understanding of neuronal reorganization is primarily obtained from invasive tissue measurements using histological and immunohistological methods, which are restricted to single time point analysis and therefore do not allow dynamic assessment of tissue remodeling in vivo. Diffusion tensor imaging (DTI) has previously been shown to detect variations in fractional anisotropy (FA) due to pathology. However, due to the assumption of Gaussian diffusion inherent to the tensor model, regions of reduced FA values may not fully reflect true brain pathology. In this study, we investigate the feasibility of MRI measurement of neurite density in wildtype mice using NODDI . NODDI adopts a tissue model that distinguishes three types of microstructural environment: intra-neurite, extra-neurite, and CSF compartments. The unique diffusion properties of water in each compartment provides a separate component of the MR signal for each environment. The NODDI analysis generates microstructural parameter maps, including orientation dispersion index (ODI) and neurite density index (NDI), and isotropic volume fraction (Iso) which has reported to reflect neurite structures.

Details

Date:
October 16, 2013
Time:
12:00 pm - 1:00 pm

Venue

Department of Computer Science, UCL
Gower Street, London, WC1E 6BT‎ United Kingdom
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Organizer

Department of Computer Science, UCL
Phone:
+44 (0)20 7679 7214
Website:
http://www.cs.ucl.ac.uk/

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