Inter-scanner and inter-patient variability for large-scale MRI studies

MRI在NHS和其他医疗服务中被用于诊断和监测每一种主要疾病，包括癌症、心脏病、痴呆和中风（英国的四大杀手）。然而，临床MRI数据通常是定性的。这导致在不同扫描仪上和不同时间点之间获得的图像有相当大的差异，这不是由于潜在的病理。这意味着重要的生物变异或新疗法的效果被扫描仪之间的差异所掩盖——这是药物试验和基于人工智能的推断的主要混淆。扫描仪间的可变性大大增加了所需的数据量，增加了研究成本和复杂性，在某些情况下，不必要地完全消除了有前途的方法和治疗方法。该项目旨在将扫描仪之间的可变性与患者之间的可变性分开，使用来自UCL和Alliance Medical的MRI图像和QA数据的大型数据库来描述每种影响。我们将进行过程控制分析，以建立与时间相关的图像校准，并使用它来纠正扫描仪可变性的影响。然后，我们将对图像进行高级聚类和变异性分析，以确定患者群体变化的剩余驱动因素。这提供了大量健康个体的基线，可用于支持进一步标准化对照和患者图像之间比较管道的工作。

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.