用单能CT生成特定造影相位的虚拟单能图像
A unified deeplearning framework for contrast-phase-specific virtual monochromatic imaging
- 将造影相位作为先验，通过深度学习从单能CT重建50 keV图像
- 在四种造影相位下均实现对比度增强，且跨相位泛化良好
- 适合希望降低成本但需高质量对比成像的临床场景
双能CT（DECT）可实现虚拟单能成像（VMI）并提升对比分辨率，但其临床应用受限于硬件复杂性和成本。本文提出一种统一的深度学习框架，利用造影相位信息作为先验，从单能CT（SECT）数据中合成特定造影相位的50 keV虚拟单能图像。模型基于四类造影相位——血管期、动脉期、门脉期和延迟期——的DECT衍生70 keV与50 keV图像对进行训练，采用新型先验条件化架构，将造影相位先验融入能量转换过程。结果表明，该统一模型在各造影相位下均实现对比度增强，并具有良好泛化能力；同时，模型可从SECT输入生成接近50 keV效果的图像，保留造影相位特异性动态特征。
原文摘要 · Abstract (English)
Dual-energy CT (DECT) enables virtual monochromatic imaging (VMI) and improved contrast resolution, but its clinical adoption is limited by hardware complexity and cost. In this work, we propose a unified deep learning framework that synthesizes contrast-phase-specific virtual monochromatic 50 keV images from single-energy CT (SECT) data by leveraging contrast phase information as a prior. The model is trained using DECT-derived 70 keV and 50 keV image pairs across four contrast phases -- Angio, Arterial, Portal, and Delayed -- using a novel prior conditioning architecture that integrates contrast phase priors into the energy transformation process. We demonstrate that the proposed unified model achieves contrast enhancement and generalizes well across contrast phases. Additionally, we show that the model can generate 50 keV-like images from SECT inputs, preserving contrast phase-specific dynamics.
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