Inter-scanner and inter-patient variability for large-scale MRI studies
Award Number
2594488Award Type
StudentshipStatus / Stage
ActiveDates
26 September 2021 -29 September 2025
Duration (calculated)
04 years 00 monthsFunder(s)
EPSRC (UKRI)Funding Amount
£0.00Funder/Grant study page
EPSRCContracted Centre
University College LondonPrincipal Investigator
Agnieszka SierhejPI Contact
agnieszka.sierhej.20@ucl.ac.ukWHO Catergories
Risk reduction interventionDisease Type
Dementia (Unspecified)CPEC Review Info
| Reference ID | 763 |
|---|---|
| Researcher | Reside Team |
| Published | 24/07/2023 |
Data
| Award Number | 2594488 |
|---|---|
| Status / Stage | Active |
| Start Date | 20210926 |
| End Date | 20250929 |
| Duration (calculated) | 04 years 00 months |
| Funder/Grant study page | EPSRC |
| Contracted Centre | University College London |
| Funding Amount | £0.00 |
Plain English Summary
MRI is used across the NHS and other health services in the diagnosis and monitoring of every major disease including Cancer, Heart Disease, Dementia, and Stroke (the four largest killers in the UK). Clinical MRI data, however, is typically qualitative. This leads to considerable variability in images acquired on different scanners and between different points in time which is not due to the underlying pathology. This means that important biological variability or the effect of a new therapy is obscured by differences between scanners – a major confound for drug trials and AI-based inference. Inter-scanner variability greatly increases the amount of data required, increasing study costs and complexity and in some cases needlessly eliminating promising approaches and therapies altogether.
This project aims to separate inter-scanner variability from inter-patient variability, characterising the effect of each using large databases of MRI images and QA data from UCL and Alliance Medical. We will perform process control analysis to establish time-dependent calibration for the images and use this to correct for the effects of scanner variability. We will then perform advanced clustering and variability analysis on the images to identify the remaining drivers of change in the patient population. This provides baselining of a large cohort of healthy individuals which can be used to underpin further work in standardising the pipeline of comparison between control and patient images.
Aims
This project aims to separate inter-scanner variability from inter-patient variability, characterising the effect of each using large databases of MRI images and QA data from UCL and Alliance Medical.