Automated analysis of low-field brain MRI in cerebral malaria

• Published in: Biometrics Vol. 79; no. 3; pp. 2417 - 2429
• Main Authors: 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, Shinohara, Russell T
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 01.09.2023
• Subjects: Algorithms; Automation; Biomarkers; Brain; Brain - diagnostic imaging; Brain - pathology; Cerebrospinal fluid; Child; Differentiation (biology); Field strength; Humans; Image acquisition; Image Processing, Computer-Assisted - methods; Image quality; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Malaria; Malaria, Cerebral - diagnostic imaging; Malaria, Cerebral - pathology; Markov chains; Medical imaging; Neuroimaging; Scanners; Spatial data; Substantia alba; Ventricle; Markov random field; MRI; brain segmentation; data integration
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/biom.13708

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.