Harmonization of diffusion MRI data sets with adaptive dictionary learning

• Published in: Human brain mapping Vol. 41; no. 16; pp. 4478 - 4499
• Main Authors: St‐Jean, Samuel, Viergever, Max A., Leemans, Alexander
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.11.2020
• Subjects: Adaptive algorithms; Akaike information criterion; Algorithms; Biological Variation, Population; Brain - diagnostic imaging; cross‐validation; Datasets; Dictionaries; dictionary learning; Diffusion; Diffusion Magnetic Resonance Imaging - methods; Diffusion Magnetic Resonance Imaging - standards; diffusion MRI; harmonization; Humans; Image Processing, Computer-Assisted - methods; Image Processing, Computer-Assisted - standards; Machine learning; Magnetic resonance imaging; Mapping; Multicenter Studies as Topic; Neuroimaging - methods; Neuroimaging - standards; Regularization; scanner variability; Scanners; scanner‐space; Spatial data; Spatial discrimination; Spatial resolution; Training; Variability; diffusion MRI; Akaike information criterion; cross-validation; scanner-space; dictionary learning; harmonization; scanner variability
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.25117

Diffusion magnetic resonance imaging can indirectly infer the microstructure of tissues and provide metrics subject to normal variability in a population. Potentially abnormal values may yield essential information to support analysis of controls and patients cohorts, but subtle confounds could be mistaken for purely biologically driven variations amongst subjects. In this work, we propose a new harmonization algorithm based on adaptive dictionary learning to mitigate the unwanted variability caused by different scanner hardware while preserving the natural biological variability of the data. Our harmonization algorithm does not require paired training data sets, nor spatial registration or matching spatial resolution. Overcomplete dictionaries are learned iteratively from all data sets at the same time with an adaptive regularization criterion, removing variability attributable to the scanners in the process. The obtained mapping is applied directly in the native space of each subject toward a scanner‐space. The method is evaluated with a public database which consists of two different protocols acquired on three different scanners. Results show that the effect size of the four studied diffusion metrics is preserved while removing variability attributable to the scanner. Experiments with alterations using a free water compartment, which is not simulated in the training data, shows that the modifications applied to the diffusion weighted images are preserved in the diffusion metrics after harmonization, while still reducing global variability at the same time. The algorithm could help multicenter studies pooling their data by removing scanner specific confounds, and increase statistical power in the process. In this work, we propose a new harmonization algorithm based on adaptive dictionary learning to mitigate the unwanted variability of diffusion magnetic resonance imaging data sets associated with the scanning protocol. Our harmonization algorithm does not require paired training data sets, nor spatial registration or matching spatial resolution. Results show that the effect size of the four studied diffusion metrics is preserved while removing variability attributable to the scanner, while still reducing global variability at the same time.