A Machine Learning Model to Harmonize Volumetric Brain MRI Data for Quantitative Neuroradiologic Assessment of
Damiano Archetti1, Vikram Venkatraghavan1, Béla Weiss1
1From the Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Via Pilastroni 4, Brescia 25125, Italy (D.A., A.R.); Alzheimer Centre Amsterdam, Neurology, Vrije Universiteit, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands (V.V., W.M.v.d.F., B.M.T.); Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands (V.V., W.M.v.d.F., B.M.T.); Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary (B.W., T.A., Z.V.); Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Óbuda University, Budapest, Hungary (B.W.); The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, Australia (P.B.); School of Psychology, University of Surrey, Guildford, United Kingdom (T.A.); Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France (S.D.); Department of Epidemiology and Data Science, Vrije Universiteit, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands (W.M.v.d.F.); Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands (F.B.); Queen Square Institute of Neurology, University College London, United Kingdom (F.B.); and UCL Hawkes Institute, Department of Medical Physics and Biomedical Engineering and Department of Computer Science, University College London, London, United Kingdom (F.B., D.C.A., A.A., N.P.O.).
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Purpose To extend a previously developed machine learning algorithm for harmonizing brain volumetric data of individuals undergoing neuroradiologic assessment of Alzheimer disease not encountered during model training. Materials and Methods Neuroharmony is a recently developed method that uses image quality metrics as predictors to remove scanner-related effects in brain-volumetric data using random forest regression. To account for the interactions between Alzheimer disease pathology and image quality metrics during harmonization, the authors developed a multiclass extension of Neuroharmony for individuals with and without cognitive impairment. Cross-validation experiments were performed to benchmark performance against other available strategies using data from 20 864 participants with and without cognitive impairment, spanning 11 prospective and retrospective cohorts and 43 scanners. Evaluation metrics assessed the ability to remove scanner-related variations in brain volumes (marker concordance between scanner pairs) while retaining the ability to delineate different diagnostic groups (preserving disease-related signal). Results For each strategy, marker concordances between scanners were significantly better (P < .001) compared with preharmonized data. The proposed multiclass model achieved significantly higher concordance (mean, 0.75 ± 0.09 [SD]) than the Neuroharmony model trained on individuals without cognitive impairment (mean, 0.70 ± 0.11) and preserved disease-related signal (∆AUC [area under the receiver operating characteristic curve] = -0.006 ± 0.027) better than the Neuroharmony model trained on individuals with and without cognitive impairment that did not use the proposed extension (∆AUC = -0.091 ± 0.036). The marker concordance was better in scanners seen during training (concordance > 0.97) than unseen (concordance < 0.79), independent of cognitive status. Conclusion In a large-scale multicenter dataset, the proposed multiclass Neuroharmony model outperformed other available strategies for harmonizing brain volumetric data from unseen scanners in a clinical setting. Keywords: Image Postprocessing, MR Imaging, Dementia, Random Forest Supplemental material is available for this article. Published under a CC BY 4.0 license See also commentary by Haller in this issue.
