Robust 3D functional imaging of the living, breathing brain

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.

Modern healthcare has become increasingly reliant on the development of medical technologies, for diagnosis, therapy and research. Rapid progress is being made in MRI as a healthcare technology, and new developments in functional MRI (fMRI), for example, have been invaluable in making big-data projects like the UK Biobank (which aims to scan 100,000 people) feasible, transforming the scope of medical and population health research. As this proposal focuses on a new solution to the physiological "noise" that plague functional brain imaging in regions like the brain-stem, it will provide increased imaging efficiency by improving data quality without the need for an increase in scan time or cost.

The brain-stem is important because it plays a critical role in human health, which includes mediating our perception of pain, controlling autonomic processes in our body, and regulating our breathing. When things go wrong with any of these functions, fMRI can be an incredibly useful tool for clinicians to non-invasively look at brain activity in the brain-stem. By providing better imaging tools for doctors and neuroscientists, we enable them to be more effective at their jobs of understanding how disease mechanisms work and how to diagnose and treat them.

From an economic standpoint, providing improved fMRI capabilities has impact on multiple levels. At a large scale, facilitating the development of more effective diagnostics or therapeutic interventions for disorders affecting the brain-stem can improve patient throughput and reduce the cost burden on the NHS, which is becoming an increasingly relevant as the NHS struggles with funding and staffing issues. At the scale of individuals, improving fMRI image quality could help shorten scan times, and therefore the cost associated with MRI scanning, by reducing the amount of data needed for statistically significant measurements.

This research will also contribute to the global efforts to understand the human brain, which include the US's BRAIN Initiative, the EU's Human Brain Project, Japan's Brain/MINDS, and China's Brain Project. These programs exist to push the frontiers of neuroscience in part through supporting the development of new brain imaging technologies. As the World Health Organisation reports that neurological disorders affect nearly a billion people worldwide, improving the tools we have to map the functional organisation of the human brain will help us understand how and why these disorders occur, and ultimately inform treatment and prevention of these conditions.