联合交叉注意力网络与深度模式之前的快速MRI重建
IEEE transactions on medical imaging
|September 11, 2023
概括
本研究介绍了jCAN,这是一种用于更快地进行磁共振成像 (MRI) 重建的深度学习模型. 通过共同优化MRI图像和灵敏度图,jCAN提高了图像质量,优于现有方法.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 使用深度学习的加速磁共振成像 (MRI) 重建通常依赖于卷积神经网络 (CNN) 来获取分样 k 空间数据.
- 现有的模型在线圈灵敏度估计,结构先前利用和CNN感应偏差方面面临局限性,阻碍了性能.
研究的目的:
- 提出一个基于展开的联合交叉注意网络 (jCAN),用于加速的多线圈MRI重建.
- 提高线圈灵敏度图 (SM) 估计的准确性,并提高模型的表示能力.
- 为了利用人体内数据作为指导重建的参考模式.
主要方法:
- jCAN同时优化潜伏的MR图像和灵敏度图 (SM),结合盖廷和高斯层来改进SM估计.
- 该模型将视觉变压器 (ViT) 集成到图像域中,并将CNN集成到k空间域中,以实现增强的表示.
- 一个自我和交叉注意力机制利用预先获得的实体内扫描来指导部分样本目标模式的重建.
主要成果:
- 与最先进的方法相比,jCAN在公共膝盖和内部大脑数据集上的表现优越.
- 在各种加速度因子和采样口罩中,在结构相似性指数测量 (SSIM) 和峰值信号噪声比 (PSNR) 中观察到显著改善.
- 拟议的方法有效地解决了卷轴灵敏度估计和加速MRI的表示学习方面的挑战.
结论:
- 开发的jCAN模型为加速的多线圈MRI重建提供了强大而有效的解决方案.
- 联合优化策略以及ViT和注意力机制的整合显著提高了MRI重建质量.
- 公开可用的代码有助于在快速MRI领域的进一步研究和应用.
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