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Updated: Jun 27, 2026

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MPM-DCE:早期和晚期DCE前列腺MRI合成的多阶段渐进机制

Siva Singireddi, Sriprabha R, Kishore Kumar M

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

概括

MPM-DCE是一种新的多阶段框架,通过合成动态对比增强MRI (DCE-MRI) 图像来增强前列腺癌诊断. 这种方法完善了 perfusion 的特性,提高了诊断准确度,同时减少了对比剂的依赖.

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

动态对比增强型MRI (DCE-MRI) 对前列腺癌的诊断至关重要.
目前用于DCE-MRI合成的深度学习方法使用单阶段架构,限制了功能改进.
保存输液细节对于准确的诊断信息至关重要.

研究的目的:

开发一个基于生成对抗网络 (GAN) 的多阶段框架 (MPM-DCE) 来合成早期和晚期响应的DCE-MRI图像.
改进DCE-MRI合成中的 perfusion 特性逐步的精细化.
通过减少对对比剂的依赖来提高前列腺癌诊断.

主要方法:

拟议的MPM-DCE是一个多阶段的GAN框架,具有编码器-解码器生成器.
使用自适应级联特征融合 (AICF) 块,用于阶段之间的顺序和横向信息交换.
使用多式输入来合成DCE-MRI图像.

主要成果:

在 ProstateX 数据集上,MPM-DCE 实现了最先进的性能.
与DCE-Former相比,表现出优异的结果,在PSNR,SSIM和MAE的早期和晚期反应方面都有显著的改进.
验证了多阶段渐进特征改进和跨阶段关注的重要性.

结论:

MPM-DCE显著提高了DCE-MRI合成质量,保留了关键的 perfusion 细节.
该方法减少了对加多对比剂的需求,可能提高前列腺癌诊断.
从统计学上显著的改善表明,临床相关性改善了诊断准确度和减少不必要的活检.

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