“The MR Facility aims to support world-class research in humans using state-of-the-art imaging tools and analysis methods.”
The MR Facility is a multi-disciplinary academic facility which hosts a community of scientists and clinicians using imaging to investigate genetic, cardiovascular, neurological and metabolic causes of disease. We have collaborations with numerous fields of basic science including physics, mathematics and bioinformatics.
The unit is equipped with a 3T Siemens Prisma (with 3D stimulus presentation) and a 1.5T Siemens Aera (including an exercise ergometer) with applications including fMRI, multinuclear spectroscopy, DTI, cardiovascular and whole body imaging. We perform translational and clinical imaging research using new imaging technologies, interventions and modelling techniques for proof of concept studies through to large-scale population cohorts. The unit is also equipped for dynamic contrast enhanced studies, adenosine stress MR and real-time exercise imaging. We have numerous collaborations with scientists in machine learning, biostatistics and genomics to support a variety of projects.
The facility is located within the main hospital allowing easy access for both patients and healthy volunteers. We also have an advanced image management system available to investigators as well as direct links to the Trust’s PACS and hospital information systems. Our team of radiographers and physicists are available for advice at any stage of project development.
Selvaraj S, Walker C, Arnone D, Cao B, Faulkner P, Cowen PJ, Roiser JP, Howes O. (2018). Effect of Citalopram on Emotion Processing in Humans: A Combined 5-HT(1A) [(11)C]CUMI-101 PET and Functional MRI Study. Neuropsychopharmacology, 43(3), 655-664.
Biffi C, de Marvao A, Attard MI, Dawes TJW, Whiffin N, Bai W, Shi W, Francis C, Meyer H, Buchan R, Cook SA, Rueckert D, O’Regan DP. (2018). Three-dimensional cardiovascular imaging-genetics: a mass univariate framework. Bioinformatics, 34(1), 97-103.
Whiffin N, Walsh R, Govind R, Edwards M, Ahmad M, Zhang X, Tayal U, Buchan R, Midwinter W, Wilk AE, Najgebauer H, Francis C, Wilkinson S, Monk T, Brett L, O’Regan DP, Prasad SK, Morris-Rosendahl DJ, Barton PJR, Edwards E, Ware JS, Cook SA. (2018). CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation. Genetics in Medicine.
Schafer S, de Marvao A, Adami E, Fiedler LR, Ng B, Khin E, Rackham OJ, van Heesch S, Pua CJ, Kui M, Walsh R, Tayal U, Prasad SK, Dawes TJ, Ko NS, Sim D, Chan LL, Chin CW, Mazzarotto F, Barton PJ, Kreuchwig F, de Kleijn DP, Totman T, Biffi C, Tee N, Rueckert D, Schneider V, Faber A, Regitz-Zagrosek V, Seidman JG, Seidman CE, Linke WA, Kovalik JP, O’Regan D, Ware JS, Hubner N and Cook SA. (2017). Titin-truncating variants affect heart function in disease cohorts and the general population. Nat Genet, 49(1), 46-53.
Dawes TJW, de Marvao A, Shi W, Fletcher T, Watson GMJ, Wharton J, Rhodes CJ, Howard L, Gibbs JSR, Rueckert D, Cook SA, Wilkins MR, O’Regan DP. (2017). Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study. Radiology, 283(2), 381-390.