Applying machine learning classifiers to automate quality assessment of paediatric dynamic susceptibility contrast (DSC-) MRI data


OBJECTIVE: Investigate the performance of qualitative review (QR) for assessing dynamic susceptibility contrast (DSC-) MRI data quality in paediatric normal brain and develop an automated alternative to QR. METHODS: 1027 signal-time courses were assessed by Reviewer 1 using QR. 243 were additionally assessed by Reviewer 2 and % disagreements and Cohen's κ (κ) were calculated. The signal drop-to-noise ratio (SDNR), root mean square error (RMSE), full width half maximum (FWHM) and percentage signal recovery (PSR) were calculated for the 1027 signal-time courses. Data quality thresholds for each measure were determined using QR results. The measures and QR results trained machine learning classifiers. Sensitivity, specificity, precision, classification error and area under the curve from a receiver operating characteristic curve were calculated for each threshold and classifier. RESULTS: Comparing reviewers gave 7% disagreements and κ = 0.83. Data quality thresholds of: 7.6 for SDNR; 0.019 for RMSE; 3 s and 19 s for FWHM; and 42.9 and 130.4% for PSR were produced. SDNR gave the best sensitivity, specificity, precision, classification error and area under the curve values of 0.86, 0.86, 0.93, 14.2% and 0.83. Random forest was the best machine learning classifier, giving sensitivity, specificity, precision, classification error and area under the curve of 0.94, 0.83, 0.93, 9.3% and 0.89. CONCLUSION: The reviewers showed good agreement. Machine learning classifiers trained on signal-time course measures and QR can assess quality. Combining multiple measures reduces misclassification. ADVANCES IN KNOWLEDGE: A new automated quality control method was developed, which trained machine learning classifiers using QR results.

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Divisions: College of Health & Life Sciences > School of Psychology
College of Health & Life Sciences > Aston Institute of Health & Neurodevelopment (AIHN)
College of Health & Life Sciences
Additional Information: Copyright © 2023 The Authors. Published by the British Institute of Radiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 Unported License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited. Funding Information: This work was funded by EPSRC through a studentship from the Sci-Phy-4-Health CDT (EP/L016346/1) and the National Institute for Health Research (NIHR) via a research professorship (RP-R2-12-019). Also, the work has been partially funded by the Children’s Research Fund, Help Harry Help Others (HHHO), Cancer Research UK, NIHR, the Experimental Cancer Medicine Centre Pediatric Network (C8232/A25261), the Children’s Cancer Fund, the Little Princess Trust and HDR UK (HDR-3001). HDR UK is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome Trust.
Publication ISSN: 1748-880X
Last Modified: 16 May 2024 07:20
Date Deposited: 08 Mar 2023 10:30
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Related URLs: https://www.bir ... 59/bjr.20201465 (Publisher URL)
PURE Output Type: Article
Published Date: 2023-04-01
Published Online Date: 2023-02-20
Accepted Date: 2023-01-24
Authors: Powell, Stephen J
Withey, Stephanie B
Sun, Yu
Grist, James T
Novak, Jan (ORCID Profile 0000-0001-5173-3608)
MacPherson, Lesley
Abernethy, Laurence
Pizer, Barry
Grundy, Richard
Morgan, Paul S
Jaspan, Tim
Bailey, Simon
Mitra, Dipayan
Auer, Dorothee P
Avula, Shivaram
Arvanitis, Theodoros N
Peet, Andrew



Version: Published Version

License: Creative Commons Attribution

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