Acquisition of Ktrans perfusion parameter maps from DCE-MRI using a deep learning approach

Motivation: Breast cancer has become the leading cancer worldwide. Hemodynamic features obtained from breast DCE-MRI perfusion maps can accurately quantify tumor pathophysiology. However, traditional estimation of perfusion parameter maps requires significant computational resources and time. Goal(s): To investigate whether deep learning techniques can synthesize Ktrans perfusion parameter maps from contrast-enhanced MRI. Approach: A pix2pix-based cGAN architecture was proposed to generate breast Ktrans perfusion maps. Results: The Ktrans values of the tumor regions in the synthetic and real Ktrans maps show a strong correlation. Two experienced radiologists could not distinguish between real and synthetic Ktrans maps. Impact: This study presents a novel feasible approach for synthesizing Ktrans perfusion maps, which enables rapid generation of high-quality and low-noise perfusion maps, thereby facilitating more effective application of these maps in clinical practice by physicians.