Petr Jan, Kybic Jan, Bock Michael, Müller Sven, Hlavác Václav
Czech Technical University in Prague, Faculty of Electrical Engineering, Department of Cybernetics, Center for Machine Perception, Praha, Czech Republic.
Magn Reson Med. 2007 Sep;58(3):582-91. doi: 10.1002/mrm.21366.
A new reconstruction method for parallel MRI called PROBER is proposed. The method PROBER works in an image domain similar to methods based on Sensitivity Encoding (SENSE). However, unlike SENSE, which first estimates the spatial sensitivity maps, PROBER approximates the reconstruction coefficients directly by B-splines. Also, B-spline coefficients are estimated at once in order to minimize the reconstruction error instead of estimating the reconstruction in each pixel independently (as in SENSE). This makes the method robust to noise in reference images. No presmoothing of reference images is necessary. The number of estimated parameters is reduced, which speeds up the estimation process. PROBER was tested on simulated, phantom, and in vivo data. The results are compared with commercial implementations of the algorithms SENSE and GRAPPA (Generalized Autocalibrating Partially Parallel Acquisitions) in terms of elapsed time and reconstruction quality. The experiments showed that PROBER is faster than GRAPPA and SENSE for images wider than 150x150 pixels for comparable reconstruction quality. With more basis functions, PROBER outperforms both SENSE and GRAPPA in reconstruction quality at the cost of slightly increased computational time.
提出了一种用于并行磁共振成像的新重建方法——PROBER。PROBER方法在图像域中的工作方式与基于灵敏度编码（SENSE）的方法类似。然而，与先估计空间灵敏度图的SENSE不同，PROBER通过B样条直接近似重建系数。此外，B样条系数是一次性估计的，以最小化重建误差，而不是像SENSE那样在每个像素中独立估计重建。这使得该方法对参考图像中的噪声具有鲁棒性。无需对参考图像进行预平滑处理。估计参数的数量减少，从而加快了估计过程。PROBER在模拟数据、体模数据和体内数据上进行了测试。在运行时间和重建质量方面，将结果与算法SENSE和GRAPPA（广义自校准部分并行采集）的商业实现进行了比较。实验表明，对于宽度大于150x150像素的图像，在可比的重建质量下，PROBER比GRAPPA和SENSE更快。使用更多的基函数时，PROBER在重建质量上优于SENSE和GRAPPA，但计算时间略有增加。
