IEEE Trans Biomed Eng. 2011 Aug;58(8). doi: 10.1109/TBME.2011.2152839. Epub 2011 May 12.
In this paper, we propose eigendecomposition- (ED-) based clutter filtering technique for 3D optical imaging of blood flow. Due to its best mean square approximation of the clutter, eigenregression filters can theoretically provide maximum clutter suppression. Compared to the existing clutter rejection techniques in the literature used for optical imaging of blood flow, ED-based clutter filtering is less sensitive to tissue motion and can efficiently suppress the clutter while preserving the flow information. Therefore, it creates images with better contrast in the presence of bulk motion. The performance of the proposed ED-based technique is compared with that of phase compensation method and static high-pass filtering. The quantitative and qualitative performances are compared with each other in phantom studies and in vivo imaging, respectively. Also, 3D image of microvascular structures in mouse ear is presented where the clutter has been suppressed with ED-based technique. This technique can be used in applications where involuntary movements due to cardiac and respiratory cycles are inevitable (such as retinal imaging).
本文提出了一种基于特征分解（ED）的杂波滤波技术，用于 3D 血流光学成像。由于它对杂波的均方最佳近似，特征回归滤波器在理论上可以提供最大的杂波抑制。与文献中用于血流光学成像的现有杂波抑制技术相比，基于 ED 的杂波滤波对组织运动的敏感性较低，能够在保留血流信息的同时有效地抑制杂波。因此，它在存在大运动的情况下生成对比度更好的图像。将所提出的基于 ED 的技术的性能与相位补偿方法和静态高通滤波进行了比较。在体模研究和体内成像中分别对定量和定性性能进行了比较。此外，还展示了小鼠耳朵微血管结构的 3D 图像，其中已经使用基于 ED 的技术抑制了杂波。该技术可用于因心脏和呼吸周期引起的不可避免的无意识运动的应用中（如视网膜成像）。
