Automated analysis of low-field brain MRI in cerebral malaria
MRI; Markov random field; brain segmentation; data integration
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.
Tu, Danni; Goyal, Manu S.; Dworkin, Jordan D.; Kampondeni, Samuel; Vidal, Lorenna; Biondo-Savin, Eric; Juvvadi, Sandeep; Raghavan, Prashant; Nicholas, Jennifer; Chetcuti, Karen; Clark, Kelly; Robert-Fitzgerald, Timothy; Satterthwaite, Theodore D.; Yushkevich, Paul; Davatzikos, Christos; Erus, Guray; Tustison, Nicholas J.; Postels, Douglas G.; Taylor, Terrie E.; Small, Dylan S.; and Shinohara, Russell T., "Automated analysis of low-field brain MRI in cerebral malaria" (2022). GW Authored Works. Paper 1081.