Abstract #0400

Mapping and visualization of white matter deformation in glioma patients using deep learning

Boshra Shams^1,2, Lucius Fekonja^1,2, Peter Vajkoczy¹, Thomas Picht^1,2, and Dogu Baran Aydogan^3,4

¹Department of Neurosurgery, Charité – Universitätsmedizin Berlin, Berlin, Germany, ²Cluster of Excellence: “Matters of Activity. Image Space Material'', Humboldt University, Berlin, Germany, ³A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland, ⁴Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland

Synopsis

Keywords: Tractography, Cancer, Glioma, Diffusion MRI, Tractography, Autoencoder

Motivation: Gliomas can induce alterations in white matter pathways, potentially resulting in structural anomalies that may contribute to functional impairments.

Goal(s): We aimed to develop a robust model for mapping structural anomalies caused by gliomas.

Approach: We developed a structural anomaly mapping pipeline using a convolutional autoencoder trained on randomly generated streamlines. By applying a clustering approach in the latent space, the model enables the mapping and visualization of structural anomalies induced by pathology.

Results: The model effectively highlighted white matter deformations caused by gliomas, revealing variability in anomaly values based on tumor size and grade.

Impact: Detecting and mapping white matter deformation is important for understanding the impact of gliomas on brain structure and its associated deficits.

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