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Abstract #4289

Three-Point Deep Learning Framework for Protocol-Independent and AIF-Free DCE-MRI Parameter Estimation in Gliomas

Piyush Kumar Prajapati1, Ankit Kandpal1, Sanskriti Srivastava1, Rakesh Kumar Gupta2, and Anup Singh1,3,4
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: Analysis/Processing, DSC & DCE Perfusion

Motivation: Quantitative assessment of brain tumor by dynamic contrast enhanced MRI(DCE-MRI) using non-linear least squares(NLLS) method is slow, scan length dependent and estimate noisy maps. Deep-learning(DL) approach shows promise, inheriting temporal sampling or AIF dependencies which are acquisition protocol dependent.

Goal(s): Develop and compare convolutional neural network(CNN) and U-Net architecture for estimating perfusion parameters(GTKM) using three strategic time points and compare with NLLS.

Approach: Networks map three concentration-time points(bolus arrival, peak, and tail concentrations to tracer kinetic(TK) parameters . Networks performance was evaluated against NLLS on in-vivo data.

Results: Our approach demonstrated high accuracy and substantial reduction in computation time for TK parameter estimation.

Impact: The proposed DL approach enables robust DCE-MRI quantification in gliomas using minimal temporal sampling, eliminating AIF dependencies while maintaining accuracy in substantially less time. Facilitating multi-center clinical adoption and efficient pre-operative tumor characterization and treatment monitoring.

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