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以知识为导向的框架,用于合成来自多序非对比MRI的对比依赖数据

Jinwei Dong¹, Yihua Chen², Nuoxi Li²

¹College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China.

Diagnostics (Basel, Switzerland)

|February 27, 2026

概括

KGSynth从非对比扫描中合成了诊断质量的对比增强型MRI,在没有加多基对比剂 (GBCAs) 的情况下保留了关键的病变细节. 这种以知识为导向的深度学习方法为有禁忌的患者提供了安全的替代方案.

科学领域:

医疗成像医学成像
人工智能的人工智能
放射学放射学是一门学科.

背景情况:

增强对比度的MRI (磁共振成像),包括晚期加多增强 (LGE) 和脑血量 (CBV) 地图,对于诊断心肌痕和脑瘤至关重要.
基于加多的对比剂 (GBCA) 是必要的,但在某些患者中禁用.
目前用于合成对比增强MRI的深度学习方法往往无法保持病理细节.

研究的目的:

开发KGSynth,一种以知识为导向的框架,用于从非对比序列中合成对比增强的MRI.
为了改善病变细节的保存和合成医疗图像中的病理准确性.
为特定患者群体提供GBCA的可行替代品.

主要方法:

KGSynth使用知识估计器来提取关键的病变和解剖特征.
一个风格映射网络捕获对比特异的特定视觉特征.
该框架明确模拟这些组件,以增强生成图像中的病态真实性.

主要成果:

在心脏和大脑MRI数据集上,KGSynth表现出比现有方法更好的性能.
实现了高结构相似度指数 (SSIM) 和LGE和CBV地图合成的峰值信号噪声比 (PSNR).
与基线模型相比,在划分心肌梗塞和脑瘤区域方面显示出更好的准确性.

结论:

将知识指导整合到生成模型中,可以在没有GBCA的情况下产生诊断质量的MRI.
KGSynth有效地保持了病理学准确性,使虚拟对比度增强成为可能.
这项技术在临床应用方面显示出显著的前景,特别是在GBCA禁忌的患者中.

关键词:

核磁共振成像合成损伤意识的一代.多式联络融合多式联络融合 perfusion 输出映射输出映射输出映射输出映射

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