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Abstract #5031

An Algorithm for the Automated Quality Assessment and Perfusion Biomarker Determination of Multicentre Dynamic Susceptibility Contrast (DSC-) MRI

Stephen Powell1,2,3, Stephanie Withey2,3,4, Yu Sun2,3,5, Lesley Macpherson6, Laurence Abernathy7, Barry Pizer8, Richard Grundy9, Simon Bailey10, Dipayan Mitra11, Dorothee Auer12, Shivaram Avula7, Theodoros N. Arvanitis2,3,13, and Andrew Peet2,3

1Physical Sciences for Health CDT, University of Birmingham, Birmingham, United Kingdom, 2Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom, 3Department of Oncology, Birmingham Children's Hospital, Birmingham, United Kingdom, 4RRPPS, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom, 5School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China, 6Radiology, Birmingham Children's Hospital, Birmingham, United Kingdom, 7Radiology, Alder Hey Children's NHS Foundation Trust, Liverpool, United Kingdom, 8Oncology, Alder Hey Children's NHS Foundation Trust, Li, United Kingdom, 9The Children's Brain Tumour Research Centre, University Of Nottingham, Nottingham, United Kingdom, 10Sir James Spence Institute of Child Health, Royal Victoria Infirmary, Newcastle, United Kingdom, 11Neuroradiology, The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle, United Kingdom, 12Sir Peter Mansfield Imaging Centre, University Of Nottingham, Nottingham, United Kingdom, 13Institute of Digital Healthcare, University of Warwick, Coventry, United Kingdom

Dynamic Susceptibility Contrast (DSC-) MRI estimates biomarkers, such as cerebral blood volume (CBV). However, data quality varies between centres and quality control (QC) is carried out by qualitative review, which is time-consuming and subjective. An automated QC pipeline was developed and tested on 34 patient data sets. The pipeline analysed four slices from each patient, producing SNR, RMSE, relative CBV (rCBV), and quality maps for each slice, which were used to quantify QC. Average values for each parameter were produced for each centre, protocol and field strength, showing variability in data quality and providing a basis for multi-centre protocol optimisation.

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