Abstract #5007

Automated analysis of the UK Biobank MRI data for the assessment of multi-organ involvement in disease

Eleanor F Cox^1,2, Zhendi Gong³, Martin Craig^1,2, Ali-Reza Mohammadi-Nejad^1,2,4, Guruprasad Padur Aithal^2,5, Iain D Stewart⁶, Louise V Wain^7,8, Gisli Jenkins⁶, Dorothee P Auer^1,2,4, Stamatios N Sotiropoulos^1,2,4, Xin Chen^2,3, Susan T Francis^1,2, and The DEMISTIFI Consortium⁹

¹Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom, ²NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, United Kingdom, ³School of Computer Science, University of Nottingham, Nottingham, United Kingdom, ⁴Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom, ⁵Nottingham Digestive Diseases Centre, Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom, ⁶National Heart & Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom, ⁷Department of Population Health Sciences, University of Leicester, Leicester, United Kingdom, ⁸NIHR Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom, ⁹Lead Research Organisation: Imperial College London, London, United Kingdom

Synopsis

Keywords: Kidney, Kidney

Motivation: To understand organ changes in multimorbidity (fibrosis in two or more organs).

Goal(s): To use the MRI data in the UK Biobank (UKBB) to study multi-organ changes.

Approach: An automated pipeline to analyse the UKBB kidney MRI data, including deep learning for kidney cortex and medulla segmentation from T₁ maps, alongside segmentation of the liver, spleen and pancreas to assess their T₁. Analysis of 500 healthy volunteers and 235 participants with kidney, pancreas and liver disease.

Results: Multi-organ changes in addition to the primary diseased organ. For example, elevation in cortical T₁ in kidney disease together with increased pancreatic and liver T₁.

Impact: The automated multi-organ analysis of abdominal MRI data to study multi-organ fibrosis. In the future, this will allow investigations related to the epidemiology, risk factors (genetic and environmental) and natural history of fibrotic multimorbidity.

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