Quantitative Analysis of DCE-MRI Data using DL model based on Signal Intensity vs Concentration Curves

Motivation: Quantitative analysis of dynamic-contrast-enhanced MRI(DCE-MRI) is valuable approach for mapping tumor physiology; however, traditional non-linear-least squares(NLLS) methods are slow and provide noisy maps. Deep-learning(DL) approach offers solutions, yet reported models rely on signal-intensity-time-curves(SIC) which are MRI-acquisition protocol dependent. Goal(s): To develop DL network(CNNCON) that uses concentration-time-curves(CTCs) to estimate perfusion-parameters(GTKM) and compare with SIC-based DL network(CNNSIGNAL). Approach: Two CNN networks were developed using CTC and SIC data(simulations and in-vivo). Performance of models was evaluated on simulated data with different protocols and experimental data. Results: The CNNCONC outperforms NLLS & CNNSIGNAL in terms of speed, accuracy and smoothness of maps. Impact: The proposed DL framework improves DCE-MRI analysis by providing more accurate and robust results in less time. It eliminates protocol dependence and holds the potential for routine clinical use in the diagnosis and treatment planning of brain tumor patients.