Synthesizing Late-Stage Contrast Enhancement in Breast MRI: A Comprehensive Pipeline Leveraging Temporal Contrast Enhancement Dynamics

TL;DR

This paper introduces a novel pipeline for synthesizing late-phase breast MRI images from early-phase data, utilizing a new loss function and normalization strategy to reduce scan times while maintaining diagnostic quality.

Contribution

It presents a new generative model with TI-loss and TI-norm that accurately replicates contrast enhancement dynamics, improving upon existing methods for MRI synthesis.

Findings

Accurately synthesizes late-phase images from early-phase data.

Outperforms existing models in replicating contrast enhancement patterns.

Reduces MRI scan time without losing diagnostic information.

Abstract

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is essential for breast cancer diagnosis due to its ability to characterize tissue through contrast agent kinetics. However, traditional DCE-MRI protocols require multiple imaging phases, including early and late post-contrast acquisitions, leading to prolonged scan times, patient discomfort, motion artifacts, high costs, and limited accessibility. To overcome these limitations, this study presents a pipeline for synthesizing late-phase DCE-MRI images from early-phase data, replicating the time-intensity (TI) curve behavior in enhanced regions while maintaining visual fidelity across the entire image. The proposed approach introduces a novel loss function, Time Intensity Loss (TI-loss), leveraging the temporal behavior of contrast agents to guide the training of a generative model. Additionally, a new normalization strategy, TI-norm, preserves the contrast enhancement pattern across multiple image sequences at various timestamps, addressing limitations of conventional normalization methods. Two metrics are proposed to evaluate image quality: the Contrast Agent Pattern Score ($\mathcal{CP}_{s}$), which validates enhancement patterns in annotated regions, and the Average Difference in Enhancement ($\mathcal{ED}$), measuring differences between real and generated enhancements. Using a public DCE-MRI dataset with 1.5T and 3T scanners, the proposed method demonstrates accurate synthesis of late-phase images that outperform existing models in replicating the TI curve's behavior in regions of interest while preserving overall image quality. This advancement shows a potential to optimize DCE-MRI protocols by reducing scanning time without compromising diagnostic accuracy, and bringing generative models closer to practical implementation in clinical scenarios to enhance efficiency in breast cancer imaging.