Synthesizing late-stage contrast enhancement in breast MRI: A comprehensive pipeline leveraging temporal contrast enhancement dynamics

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is essential for breast cancer diagnosis because it can characterize tissue based on contrast agent kinetics. Conventional DCE-MRI protocols require multiple imaging phases, including both early and late post-contrast acquisitions, leading to prolonged scanning times that can cause patient discomfort, motion artifacts, and contribute to higher costs and limited availability in clinical settings. To address these limitations, this paper presents a comprehensive pipeline for synthesizing long-term (late-phase) contrast-enhanced breast MRI images from short-term (early-phase) counterparts, aiming to replicate the behavior of the time-intensity (TI) curve in enhanced regions while maintaining visual properties across the entire image. The proposed approach introduces a new loss function called the Time Intensity Loss (TI-loss), which leverages the temporal behavior of the contrast agent to guide the training of a generative model. Furthermore, as established normalization strategies show undesirable effects on the enhancement behavior, a novel normalization strategy (TI-norm) is also proposed, which preserves the contrast enhancement pattern across multiple image sequences at various timestamps. Additionally, two new metrics are proposed to evaluate the synthesized image quality, i.e., the Contrast agent Pattern score (CPs), which determines the validity of annotated regions according to their enhancement patterns (plateau, persistent, washout), and the average difference in enhancement (ED), which quantifies the difference between the real and generated enhancement in selected regions. Evaluation was performed using a public DCE-MRI dataset that includes studies from 3T and 1.5T scanners with different imaging techniques. Experimental results demonstrate that our method accurately synthesizes the contrast enhancement response in terms of the TI curve in regions of interest and significantly outperforms other models, while maintaining visual properties comparable to real late-phase contrast-enhanced images. By enabling accurate synthesis of late-phase contrast-enhanced images from early-phase data, our method has the potential to optimize DCE-MRI protocols, reducing scanning time without compromising diagnostic accuracy. This advancement brings generative models closer to practical implementation in clinical scenarios, enhancing efficiency in breast cancer imaging.