节点文献

基于同伦l0范数最小化重建的三维动态磁共振成像

3D Dynamic MRI with Homotopic l0 Minimization Reconstruction

  • 推荐 CAJ下载
  • PDF下载
  • 不支持迅雷等下载工具,请取消加速工具后下载。

【作者】 李嫣嫣李律李雪松郭华

【Author】 LI Yan-yan;LI Lv;LI Xue-song;GUO Hua;School of Computer Science and Technology, Beijing Institute of Technology;Center for Biomedical Imaging Research,School of Medicine, Tsinghua University;

【通讯作者】 李雪松;

【机构】 北京理工大学计算机学院清华大学医学院生物医学影像研究中心

【摘要】 高欠采倍数的动态磁共振图像重建具有重要意义,是同时实现高时间分辨率和高空间分辨率动态对比度增强成像的重要环节.本研究提出一种结合黄金角变密度螺旋采样、并行成像和基于同伦l0范数最小化的压缩感知的图像重建的三维动态磁共振成像方法.黄金角变密度螺旋采样轨迹被用来连续获取k空间数据,具有数据采集效率高、对运动不敏感等优点.在重建算法中,将多线圈稀疏约束应用于时间总变分域,使用基于l0范数最小化的非线性重建算法代替传统的l1范数最小化算法,进一步提高了欠采样率.仿真实验和在体实验表明本文所提的方法在保持图像质量的同时,也可以实现较高的空间分辨率和时间分辨率,初步验证了基于同伦l0范数最小化重建在三维动态磁共振成像上的优势和临床价值.

【Abstract】 Recovering dynamic magnetic resonance imaging(MRI) from highly undersampled raw data is of great significance, and is an important approach for achieving both high temporal and spatial resolution in dynamic contrast enhanced imaging. In this study, a method for 3D dynamic MRI with high spatiotemporal resolution and motion insensitivity was developed, which combines golden angle variable density spiral trajectory, parallel imaging and homotopic l0 minimization-based compressed sensing. Golden angle variable density spiral trajectory, which has the advantages of high data acquisition efficiency and motion insensitivity, was used to acquire k-space data. In the reconstruction algorithm, multicoil sparsity constrain was applied in the temporal total variation domain and l0 minimization, instead of traditional l1 minimization,was adopted, which could further increase the undersampling rate. Simulations and in vivo experiments demonstrated that high spatial resolution and temporal resolution can be achieved by the proposed method while maintaining the image quality. This study also suggested the advantages of homotopic l0 norm-based 3D dynamic MRI reconstruction and its clinical potential.

【基金】 国家自然科学基金资助项目(61801026,62071049)
  • 【文献出处】 波谱学杂志 ,Chinese Journal of Magnetic Resonance , 编辑部邮箱 ,2022年01期
  • 【分类号】O482.53
  • 【被引频次】1
  • 【下载频次】151
节点文献中: 

本文链接的文献网络图示:

本文的引文网络