HeteroMRI: Robust white matter abnormality classification across multi-scanner MRI data.

BACKGROUND

Magnetic resonance imaging (MRI) is commonly used for analyzing white matter abnormalities in the human brain. Integrating machine learning into MRI analysis can enhance diagnostic processes. However, the application of such techniques for white matter analysis in clinical practice is often limited when MRI data are multi-scanner (i.e., heterogeneous), particularly in scenarios with limited data, as seen in rare diseases. Therefore, it is crucial to develop methods that are highly independent of the MRI scanner and acquisition protocol.

RESULTS

This study introduces HeteroMRI, a deep learning method for classifying MRIs based on white matter abnormalities. Most importantly, HeteroMRI mitigates the effects of data heterogeneity on classification performance. Herein, HeteroMRI is employed to detect brain MRIs with white matter abnormalities. This method utilizes intensity clustering of the white matter tissue to reduce the effects of the heterogeneity of MRIs. MRI data from 11 public datasets with 40 MRI protocols are included. By using 200 MRIs for training the model, the binary classifier achieves an average accuracy of 93% ± 4%. Furthermore, the method is evaluated in limited data scenarios, simulating conditions of rare diseases. By reducing the data by 64% and 75%, the model's accuracy has a 4% and 12% decrease, respectively.

CONCLUSIONS

The presented method opens new avenues for white matter abnormality-related classification of heterogeneous MRI data without additional machine learning methods to reduce MRI heterogeneity. This classification approach demonstrates a high degree of independence from the MRI scanner and protocol, while also proving to be relatively generalizable to unseen MRI protocols.