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

• URL: http://arxiv.org/abs/2409.01596v2
• Date: Fri, 24 Jan 2025 21:21:08 GMT
• Title: Synthesizing Late-Stage Contrast Enhancement in Breast MRI: A Comprehensive Pipeline Leveraging Temporal Contrast Enhancement Dynamics
• Authors: Ruben D. Fonnegra, Maria Liliana Hernández, Juan C. Caicedo, Gloria M. Díaz,
• Abstract summary: This study presents a pipeline for synthesizing late-phase DCE-MRI images from early-phase data.<n>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.<n>Two metrics are proposed to evaluate image quality: the Contrast Agent Pattern Score ($mathcalCP_s$), which validates enhancement patterns in annotated regions, and the Average Difference in Enhancement ($mathcalED$), measuring differences between real and generated enhancements.
• Score: 0.3499870393443268
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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