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多模态MRI级联增量重建与粗细的空间注册

Yulu Wang¹, Yubao Sun¹, Jia Liu¹

¹Engineering Research Center of Digital Forensics, Ministry of Education, CICAEET, Nanjing University of Information Science and Technology, Nanjing, 210044, Jiangsu, China.

Magnetic resonance imaging

|August 7, 2025

概括

这项研究引入了一种用于多模压感应MRI (CS-MRI) 的新型网络,该网络集成了空间注册和级联重建. 该方法有效地融合了跨模态图像,改善了加速MRI扫描的重建.

科学领域:

医疗成像医学成像
人工智能在医学中的应用

背景情况:

磁共振成像 (MRI) 使用多重对比用于组织评估,但长时间扫描可能会产生运动文物.
压缩感应MRI (CS-MRI) 通过计算重建加速成像,通常使用全样本辅助图像来帮助低样本目标图像重建.
由于空间偏移和不同的成像参数,辅助和目标MRI数据的交叉模式融合具有挑战性.

研究的目的:

为多模体CS-MRI提出一个端到端的网络,以应对跨模体融合挑战.
整合空间注册和级联增量重建,以改进CS-MRI.
为了提高加速MRI扫描的质量和减少文物.

主要方法:

一个端到端的网络,包括一个从粗到细的空间记录子网络,以对准样本不足的目标图像和样本完整的辅助MR图像.
一个级联增量重建子网络,使用单独的交叉窗口转换器在双路径架构中.
融合模式间和模式内特征,以代地改进目标MR图像.

主要成果:

拟议的网络有效地减轻了多模 MR 图像之间的空间偏移.
级联重建成功地恢复了增量细节,并完善了目标图像.
对IXI大脑数据集的实验验证表明,与现有的CS-MRI方法相比,其性能优越.

结论:

开发的网络提供了一个有效的解决方案,用于跨模态融合在多模态CS-MRI.
空间注册和级联重建的整合显著改善了加速MRI.
这种方法为提高MRI采集效率和质量提供了一个有希望的方向.

关键词:

级联重建的级联重建.压缩传感器的压缩传感器多模态核磁共振 (MRI) 是一种多模态核磁共振.空间登记是指空间登记.

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