Automated analysis of low‐field brain MRI in cerebral malaria

A central challenge of medical imaging studies is to extract biomarkers that characterize disease pathology or outcomes. Modern automated approaches have found tremendous success in high‐resolution, high‐quality magnetic resonance images. These methods, however, may not translate to low‐resolution images acquired on magnetic resonance imaging (MRI) scanners with lower magnetic field strength. In low‐resource settings where low‐field scanners are more common and there is a shortage of radiologists to manually interpret MRI scans, it is critical to develop automated methods that can augment or replace manual interpretation, while accommodating reduced image quality. We present a fully automated framework for translating radiological diagnostic criteria into image‐based biomarkers, inspired by a project in which children with cerebral malaria (CM) were imaged using low‐field 0.35 Tesla MRI. We integrate multiatlas label fusion, which leverages high‐resolution images from another sample as prior spatial information, with parametric Gaussian hidden Markov models based on image intensities, to create a robust method for determining ventricular cerebrospinal fluid volume. We also propose normalized image intensity and texture measurements to determine the loss of gray‐to‐white matter tissue differentiation and sulcal effacement. These integrated biomarkers have excellent classification performance for determining severe brain swelling due to CM.