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).