A Time-Intensity Aware Pipeline for Generating Late-Stage Breast DCE-MRI using Generative Adversarial Models

• URL: http://arxiv.org/abs/2409.01596v1
• Date: Tue, 3 Sep 2024 04:31:49 GMT
• Title: A Time-Intensity Aware Pipeline for Generating Late-Stage Breast DCE-MRI using Generative Adversarial Models
• Authors: Ruben D. Fonnegra, Maria Liliana Hernández, Juan C. Caicedo, Gloria M. Díaz,
• Abstract summary: A novel loss function that leverages the biological behavior of contrast agent (CA) in tissue is proposed to optimize a pixel-attention based generative model. Unlike traditional normalization and standardization methods, we developed a new normalization strategy that maintains the contrast enhancement pattern across the image sequences at multiple timestamps.
• Score: 0.3499870393443268
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Contrast-enhancement pattern analysis is critical in breast magnetic resonance imaging (MRI) to distinguish benign from probably malignant tumors. However, contrast-enhanced image acquisitions are time-consuming and very expensive. As an alternative to physical acquisition, this paper proposes a comprehensive pipeline for the generation of accurate long-term (late) contrast-enhanced breast MRI from the early counterpart. The proposed strategy focuses on preserving the contrast agent pattern in the enhanced regions while maintaining visual properties in the entire synthesized images. To that end, a novel loss function that leverages the biological behavior of contrast agent (CA) in tissue, given by the Time-Intensity (TI) enhancement curve, is proposed to optimize a pixel-attention based generative model. In addition, unlike traditional normalization and standardization methods, we developed a new normalization strategy that maintains the contrast enhancement pattern across the image sequences at multiple timestamps. This ensures the prevalence of the CA pattern after image preprocessing, unlike conventional approaches. Furthermore, in order to objectively evaluate the clinical quality of the synthesized images, two metrics are also introduced to measure the differences between the TI curves of enhanced regions of the acquired and synthesized images. The experimental results showed that the proposed strategy generates images that significantly outperform diagnostic quality in contrast-enhanced regions while maintaining the spatial features of the entire image. This results suggest a potential use of synthetic late enhanced images generated via deep learning in clinical scenarios.

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