Abstract
A central challenge of medical imaging studies is to extract biomarkers that characterize pathology or predict disease outcomes. State-of-the-art automated approaches to identify these biomarkers in high-resolution, high-quality magnetic resonance images have performed well. However, such methods may not translate to low resolution, lower quality images acquired on MRI scanners with lower magnetic field strength. In low-resource settings where low-field scanners are more common and there is a shortage of available radiologists to manually interpret MRI scans, it is therefore essential to develop automated methods that can augment or replace manual interpretation while accommodating reduced image quality. Motivated by a project in which a cohort of children with cerebral malaria were imaged using 0.35 Tesla MRI to evaluate the degree of diffuse brain swelling, we introduce a fully automated framework to translate radiological diagnostic criteria into image-based biomarkers. We integrate multi-atlas 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 further propose normalized image intensity and texture measurements to determine the loss of gray-to-white matter tissue differentiation and sulcal effacement. These integrated biomarkers are found to have excellent classification performance for determining severe cerebral edema due to cerebral malaria.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Sections 1 and 4 were updated to expand on clinical applications. Figure 5 and supplementary data were added.