Abstract #0938

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

Farzad Alizadeh^1,2, Anahita Fathi Kazerooni^3,4, Hanieh Bahrampour⁵, Hanieh Mobarak Salari^1,2, and Hamidreza Saligheh Rad^1,2

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.

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