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通过跨模态特征融合和层次特征提取进行多模态磁共振成像合成

Hongyu Guo^1,2, Hongmiao Zhang¹, Mingyu Li¹

¹College of Electrical Engineering, Shenyang University of Technology, Shenyang, China.

Quantitative imaging in medicine and surgery

|December 10, 2025

概括

这项研究开发了一种用于多式磁共振成像 (MRI) 合成的新框架,通过生成丢失或损坏的MRI数据来提高诊断可靠性和减少扫描负担,即使有运动文物.

科学领域:

医疗成像医学成像
医疗保健中的人工智能
生物医学工程生物医学工程

背景情况:

多模式磁共振成像 (MRI) 对于临床诊断至关重要,提供补充组织对比.
高成本,漫长的扫描时间和运动工件往往导致不完整或退化的多式联接MRI数据.
开发强大的合成框架对于克服这些局限性和提高诊断准确性至关重要.

研究的目的:

创建一个新的框架来合成丢失或损坏的多模式MRI数据.
提高诊断可靠性,减少MRI采集的负担.
为了产生高质量的合成MRI模式,这些模式在结构上是忠实和坚固的.

主要方法:

一个统一的架构,集成用于全球上下文建模的变压器模块和用于多频局部特征提取的ResOctave块.
一个基于注意力的特征融合模块,以实现有效的跨模态特征交互.
对BraTS2018数据集的验证,以评估多模式MRI合成中的性能.

主要成果:

该框架在没有工件的输入上实现了高性能指标 (PSNR: 32.61,SSIM: 0.90,NRMSE: 0.15).
在运动工件干扰下保持了强大的性能 (PSNR: 30.02,SSIM: 0.88,NRMSE: 0.18).
通过T1和T2合成任务的可比结果证实了可通用性.

结论:

拟议的框架有效地合成了多模式MRI,具有高结构保真性和细节保存.
它表现出对运动工件的显著稳定性,提高了生成合成模式的可靠性.
未来的工作重点是将验证扩展到各种数据集,以确认临床适用性.

关键词:

磁共振成像 (MRI) 是一种磁共振成像.在 ResOctave 區塊中使用 ResOctave.跨模态特征融合的跨模态特征融合多模式磁共振成像合成 (多模式MRI合成)变压器的变压器是一个变压器.

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