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Details of Grant 

EPSRC Reference: EP/T013133/1
Title: Robust 3D functional imaging of the living, breathing brain
Principal Investigator: Chiew, Dr M
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Siemens Process Systems Engineering Ltd
Department: Clinical Neurosciences
Organisation: University of Oxford
Scheme: New Investigator Award
Starts: 01 May 2020 Ends: 30 April 2024 Value (£): 352,921
EPSRC Research Topic Classifications:
Image & Vision Computing Medical Imaging
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
17 Oct 2019 HT Investigator-led Panel Meeting - October 2019 Announced
Summary on Grant Application Form
Since the early 1990s, we have been able to use imaging methods such as functional MRI (fMRI) to look into the brain to see how it works. This non-invasive technology has transformed the way that doctors and neuroscientists can answer questions about how the brain is organised and how it processes information, in the way a healthy brain functions, or how it interacts with illness and disease. However, fMRI data can be susceptible to corruption due to motion and physiological fluctuations that reduce image quality, particularly as technological progress leads to imaging at higher spatial resolutions and higher magnetic field strengths, stretching the capabilities of our MRI systems.

Nearly everyone has had experience trying to capture images of moving objects in poor lighting conditions (e.g. people in a dimly lit room), often resulting in blurry and terrible looking photos. Now imagine trying to take pictures using a camera that operates quite slowly and indirectly (i.e. an MRI scanner), of a living, breathing human brain that won't sit still. Even for a head that is motionless, physiological factors like breathing and heart beats cause the brain inside to pulse, move and cause unwanted image corruption. This is particularly problematic in lower parts of the brain, like the brain-stem, which is involved in important physiological functions like processing pain and modulating blood pressure, for example. Coupled with the fact that the brain activity signals we want to extract are quite subtle, these physiological image corruptions can significantly impact the quality of the imaging data we can acquire in these clinically important brain regions.

There are two primary ways of dealing with this problem using existing methods. The first approach modifies the acquisition of data through a process referred to as "gating", which synchronises imaging with a certain part of the cardiac cycle. The second approach uses image post-processing to try and "correct" the corrupted images. However, gating is inefficient and image post-processing can be imperfect, presenting a large opportunity for significant improvement in the efficiency and quality of functional brain imaging data.

This proposal brings new developments in multi-dimensional ("tensor") signal processing to bear on this problem. Tensor-based methods allow us to represent and manipulate signals with higher dimensionality, allowing us to resolve more features in our data. For example, a black and white movie might have dimensions corresponding to space and time, but a colour movie has dimensions of space, time and colour, where the extra dimension allows us to capture more information about the signals of interest. For our physiological corruption problem, we use these new tools to represent our 3D brain images over not only time, but also across different points in the breathing and heart beat cycles, to effectively separate, rather than mix all of these signals contributions together.

To do this, we will combine new sophisticated methods for acquiring the raw MRI data with advances in image reconstruction to develop a technique for producing imaging data free of physiological corruption, in a time efficient way. This project brings together knowledge and resources across a broad spectrum of fields, ranging from hardware control of MRI systems to nonlinear signal processing and image analysis, to provide better tools for medical and neuroscientific study of the human brain-stem.

Key Findings
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Organisation Website: http://www.ox.ac.uk