Abstract #4617

An Automated Processing Pipeline to Assess and Improve Data Quality for Multicentre Pediatric Dynamic Susceptibility Contrast Imaging

Stephen Powell^1,2, Stephanie Withey^2,3,4, Yu Sun^2,5, James Grist², Lesley MacPherson⁶, Laurence Abernathy⁷, Barry Pizer⁸, Richard Grundy⁹, Simon Bailey¹⁰, Dipayan Mitra¹¹, Dorothee Auer¹², Shivaram Avula⁷, Theodoros N. Arvanitis^2,3,13, and Andrew Peet^2,3

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, Liverpool, 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

Obtaining robust perfusion measures from pediatric Dynamic Susceptibility Contrast (DSC-) MRI, such as cerebral blood volume (CBV), is challenging due to variability in acquisition protocols between centres and a heterogeneous patient population. Quality control (QC) is currently carried out by expert qualitative review. An automated QC pipeline was developed which used denoising to salvage data, and assessed data quality using logistic regression classification, with signal-to-noise ratio (SNR) and root mean square error (RMSE) in a gamma variate fit to the first pass as predictors. SNR was the key factor in data quality and denoising is important in assuring appropriate analysis.

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