Automated analysis of low-field brain MRI in cerebral malaria

Authors

• Danni Tu, The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
• Manu S. Goyal, Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.
• Jordan D. Dworkin, Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, USA.
• Samuel Kampondeni, Blantyre Malaria Project, Kamuzu University of Health Sciences, Southern Region, Blantyre, Malawi.
• Lorenna Vidal, Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
• Eric Biondo-Savin, Department of Radiology, Michigan State University, East Lansing, Michigan, USA.
• Sandeep Juvvadi, Tenet Diagnostics, Hyderabad, India.
• Prashant Raghavan, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA.
• Jennifer Nicholas, University Hospitals Cleveland Medical Center, Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
• Karen Chetcuti, Department of Paediatrics and Child Health, Kamuzu University of Health Sciences, Southern Region, Blantyre, Malawi.
• Kelly Clark, The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
• Timothy Robert-Fitzgerald, The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
• Theodore D. Satterthwaite, Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
• Paul Yushkevich, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
• Christos Davatzikos, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
• Guray Erus, Center for Biomedical Image Computing and Analysis (CBICA), Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
• Nicholas J. Tustison, Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA.
• Douglas G. Postels, Division of Neurology, George Washington University/Children's National Medical Center, Washington, District of Columbia, USA.
• Terrie E. Taylor, Blantyre Malaria Project, Kamuzu University of Health Sciences, Southern Region, Blantyre, Malawi.
• Dylan S. Small, Department of Statistics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
• Russell T. Shinohara, The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Document Type

Journal Article

Publication Date

6-22-2022

Journal

Biometrics

DOI

10.1111/biom.13708

Keywords

MRI; Markov random field; brain segmentation; data integration

Abstract

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.

APA Citation

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.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/1081

Department

Neurology