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Details of Grant 

EPSRC Reference: EP/Y002016/1
Title: Towards Motion-Robust and Efficient Functional MRI Using Implicit Function Learning
Principal Investigator: Qin, Dr C
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Siemens Healthineers Trinity College Dublin
Department: Electrical and Electronic Engineering
Organisation: Imperial College London
Scheme: Standard Research - NR1
Starts: 01 March 2024 Ends: 30 June 2025 Value (£): 162,813
EPSRC Research Topic Classifications:
Image & Vision Computing Med.Instrument.Device& Equip.
EPSRC Industrial Sector Classifications:
Healthcare Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
17 May 2023 ECR International Collaboration Grants Panel 1 Announced
Summary on Grant Application Form
Functional magnetic resonance imaging (fMRI) is a leading modality to measure brain activity and connectivity. Clinically, it is starting to be used in pre-surgical planning and in assessment of brain function in vegetative state patients. It also recently shows promise in infant cognition research, which holds the key to understanding the origins and functions of the human brain. However, one of the main challenges that constrain the clinical applications of fMRI is its sensitivity to motion, where head movement causes highly deleterious artefacts in fMRI data and can be a major source of error in functional connectivity analysis. This is particularly challenging on infants between the ages of 2 and 48 months, where in many cases half of the data are discarded due to head movement, leading to significant delays and cost for repeated scans. The sustained increase in demand for it would lead to further increased pressures on hospital resources and reduced efficiency of the imaging workflows. Therefore, it is urgent to have techniques and tools to eliminate or reduce the motion effects on fMRI scans.

Recently, machine learning (ML) and deep learning (DL) techniques have shown promise to alleviate motion corruption by learning from data to retrospectively correct the motion and artefacts. However, most of these learning-based methods do not specifically focus on fMRI and most existing motion correction approaches for static and structural MRI are not directly applicable to fMRI due to the high memory requirement and application-specific motion artefacts in fMRI. Therefore, there is still a lack of a robust and reliable technique for the problem. With the increasing need and availability of fMRI data and the growing cost for repeated scans due to motion, demand for motion-robust and efficient fMRI are becoming essential.

We aim to fill the gap in fMRI research in this project by proposing to investigate motion-robust and efficient fMRI based on novel implicit function learning techniques. The proposed research will integrate and advance state-of-the-art research in machine learning and medical imaging. We will particularly consider motion correction of infant motion trajectories in this study, as infant motion causes substantial data loss in fMRI and represents the most necessary and urgent need. However, as there is not much low-motion infant data available and as they also cannot easily provide motion-free control for validation, we propose to use adult fMRIs for the initial feasibility study, where more data of low motion and better 'ground truth' control can be obtained. The project will create a novel implicit function learning method to learn a prior space for resolution-agnostic motion-free fMRI, investigate integration of the data-driven prior with instance-specific slice-to-volume registration for fast and adaptable motion correction in motion scenarios mimicking infant movement, and evaluate and validate the created approach on data of adult brain fMRI scans with and without infant-like motion. This will conduce to creation of a novel method that enables high-precision, memory-efficient and robust fMRI motion correction with resolution-agnostic volumetric reconstruction. Future research will be extended to infant fMRI given a promising outcome of the project.

The project will contribute to knowledge in machine learning, medical imaging and computer vision, by advancing state-of-the-art in both the fundamental and applied research in the multi-disciplinary field. The project will also benefit clinicians and medical image processing researchers especially on fMRI and infant, offering them a fast and reliable motion correction tool that addresses the drawbacks of current techniques. The patients, the healthcare industry and the society will also benefit from the development in medical imaging technologies, with an improved healthcare system and economics resulting from it.
Key Findings
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Organisation Website: http://www.imperial.ac.uk