Adluru Ganesh, Dibella Edward V R
Laboratory for Structural NMR Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Int J Biomed Imaging. 2008;2008:341684. doi: 10.1155/2008/341684. Epub 2008 Dec 11.
Recently, there has been a significant interest in applying reconstruction techniques, like constrained reconstruction or compressed sampling methods, to undersampled k-space data in MRI. Here, we propose a novel reordering technique to improve these types of reconstruction methods. In this technique, the intensities of the signal estimate are reordered according to a preprocessing step when applying the constraints on the estimated solution within the iterative reconstruction. The ordering of the intensities is such that it makes the original artifact-free signal monotonic and thus minimizes the finite differences norm if the correct image is estimated; this ordering can be estimated based on the undersampled measured data. Theory and example applications of the method for accelerating myocardial perfusion imaging with respiratory motion and brain diffusion tensor imaging are presented.
最近，人们对将诸如约束重建或压缩采样方法等重建技术应用于磁共振成像（MRI）中欠采样的k空间数据产生了浓厚兴趣。在此，我们提出一种新颖的重排序技术以改进这类重建方法。在该技术中，当在迭代重建过程中对估计解施加约束时，信号估计的强度会根据一个预处理步骤进行重排序。强度的排序方式使得如果估计出正确图像，原始无伪影信号呈单调变化，从而使有限差分范数最小化；这种排序可基于欠采样测量数据来估计。文中给出了该方法在加速呼吸运动下心肌灌注成像和脑扩散张量成像方面的理论及示例应用。
