Author Of 1 Presentation
P0552 - BrainGraph A Novel Visualization of MRI Data as a 3D Graph to Reveal Temporal Features of Disease Progression In Patients with Multiple Sclerosis (ID 1229)
Abstract
Background
Novel visualization of neuroimaging data can lead to clinical insights and ultimately new imaging analysis capabilities. Graph models of magnetic resonance imaging (MRI) data can reveal the topology and temporal nature of multiple sclerosis disease progression, by exposing novel structural features of the brain through representation of data as interactive 3D projections. Existing standards and evolving approaches to neuroimaging can benefit from an integration of graph analytics and visualization.
Objectives
To develop a cloud-based workflow to translate DICOM imaging data files into a visual, interactive graph schema. The resulting application will enhance and support the current evaluation of disease features on conventional MRI and reveal the temporal features of lesion and disease progression in patients with multiple sclerosis.
Methods
3D voxels from DICOM data were modeled as a graph data structure on cloud infrastructure (Amazon). The graph is composed of nodes which represent voxels and the spatial relationships that exist between them. Nodes contain properties including a voxel’s x,y,z coordinates as well as features such as signal intensities across modalities. Nodes are projected on a 3D grid using their coordinates for placement. Relationships between voxels model spatial neighborhoods in x,y, and z dimensions and across time. For a given voxel, up to six other unique voxels are potentially designated as spatial neighbors, and another relationship across time.
Results
Visual graph representation of MRI data revealed temporal progression of all lesions simultaneously. Lesions can be visually classified as consolidating/merging, expanding, or splitting across time using an interactive slider. Using graph algorithms we established lesion nodes, separated lesion surfaces from internal components, and characterized lesion shapes, temporal changes, and volumetrics.
Conclusions
Interactive 3D graph representations of MRI graph data augment traditional visualization and analysis by providing connectedness and temporal resolution into the disease process. Graphs highlight the connectedness of MRI data, the communities that compose structural features and disease processes, and the temporal relationships revealed during MS disease progression.