Meeting Banner
Abstract #0938

Tumorous Tissue Characterization in Diffuse Glioma Based on 1H-MRS Data Employing 1D Convolutional Neural Networks

Farzad Alizadeh1,2, Anahita Fathi Kazerooni3,4, Hanieh Bahrampour5, Hanieh Mobarak Salari1,2, and Hamidreza Saligheh Rad1,2
1Department of Medical Physics and Biomedical Engineering, Tehran university of Medical Science, Tehran, Iran (Islamic Republic of), 2Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran, Iran (Islamic Republic of), 3Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, United States, 4Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States, 5Biomaterials Engineering, School of Metallurgy and Materials Engineering, Iran University of Science and Technology, Tehran, Iran (Islamic Republic of)

Characterization of intra-tumour subregions in diffuse gliomas helps to guide biopsy procedure and to determine extent of tumour infiltration in the brain tissues. Conventional MRI cannot accurately differentiate intra-tumour subregions, including the most active tumour component and infiltrated edema (IE) from each other and from the normal tissue (NT). In this work, we explore the potential of differentiation of brain tumorous tissue subregions (tumour core, infiltrated edema, normal tissue, bone, and non-brain areas) based on 1H-MRS data using artificial intelligence (AI) techniques.

This abstract and the presentation materials are available to members only; a login is required.

Join Here