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

Tracer 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.