Combining fast MR acquisition sequences and high resolution imaging is a major issue in dynamic imaging. Reducing the acquisition time can be achieved by using non-Cartesian and sparse acquisitions. The reconstruction of MR images from these measurements is generally carried out using gridding that interpolates the missing data to obtain a dense Cartesian k-space filling. The MR image is then reconstructed using a conventional Fast Fourier Transform. The estimation of the missing data unavoidably introduces artifacts in the image that remain difficult to quantify.
A general reconstruction method is proposed to take into account these limitations. It can be applied to any sampling trajectory in k-space, Cartesian or not, and specifically takes into account the exact location of the measured data, without making any interpolation of the missing data in k-space. Information about the expected characteristics of the imaged object is introduced to preserve the spatial resolution and improve the signal to noise ratio in a regularization framework. The reconstructed image is obtained by minimizing a non-quadratic convex objective function. An original rewriting of this criterion is shown to strongly improve the reconstruction efficiency. Results on simulated data and on a real spiral acquisition are presented and discussed.
Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI
University of Toulouse（图卢兹大学）；CEA-NeuroSpin（法国原子能和替代能源委员会-NeuroSpin）；University Paris-Est（巴黎东大学）
并行MRI是一种快速成像技术，能够在空间或/和时间上采集高分辨率图像。并行成像的性能在很大程度上取决于重建算法，该算法可以在原始k空间（GRAPPA，SMASH）或图像域（SENSE类方法）中进行。为了提高广泛使用的SENSE算法的性能，在小波域中的二维或切片特定的正则化已被深入研究。在本文中，我们扩展了这种方法，使用三维小波表示，以处理所有切片在一起，并解决重建文物传播相邻切片。这种扩展（3D-无约束小波正则化-SENSE：3D-UWR-SENSE）引起的增益在不考虑时间采集的解剖图像重建上得到验证。另一个重要的扩展解释了功能性MRI（fMRI）中连续扫描之间存在的时间相关性。除了2D+t采集方案的情况下，解决了一些其他方法，如kt-FOXLS，我们的方法允许我们处理3D+t采集方案，广泛用于神经成像。所得到的3D-UWR-SENSE和4D-UWR-SENSE重建方案在参考扫描上以极大似然意义估计所有正则化参数的意义上是完全无监督的。这种扩展引起的增益在解剖和功能图像重建上示出，并且还在快速事件相关fMRI协议期间针对4D-UWR-SENSE方法的统计灵敏度方面进行测量。我们的4D-UWR-SENSE算法在受试者和组水平（15名受试者）上优于SENSE重建，适用于不同的感兴趣对比度（例如，运动或计算任务），并在2x2 x3 mm 3 EPI图像上使用不同的并行加速因子（R=2和R…
Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation
Purdue University（普渡大学）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Air Force Research Laboratory（美国空军研究实验室）
