Abstract #5191

Quantitative DCE-MRI parameter estimation using Deep Learning Framework in the Brain Tumor Patients

Piyush Kumar Prajapati¹, Rakesh Kumar Gupta², and Anup Singh^1,3,4

¹Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India, ²Department of Radiology, Fortis Memorial Research Institute, Gurugram, India, ³Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi, India, ⁴Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India

Synopsis

Keywords: Machine Learning/Artificial Intelligence, BrainTracer Kinetic (TK) parametric maps are obtained from Dynamic Contrast Enhanced (DCE) - MRI which aid in the detection and grading of brain tumors. Conventionally, TK maps are obtained using Non-Linear-Least-Square (NLLS) fitting approach, which is time consuming and data noise. In the current study, we implemented a deep learning framework whose backbone is attention networks to estimate TK parametric maps. Transfer learning was performed to extend work from synthetic data to high-grade glioma (HGG) patients’ DCE-MRI data to obtain better quality TK maps in lesser time.

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