A Motion Correction Algorithm for Microendoscope Video Computing in Image-Guided Intervention

In multimodality image-guided intervention for cancer diagnosis, a needle with cannula is first punctured using CT or MRI -guided system to target the tumor, then microendoscopy can be performed using an optical fiber through the same cannula. With real-time optical imaging, the operator can directly determine the malignance of the tumor or perform fine needle aspiration biopsy for further diagnosis. During this operation, stable microendoscopy image series are needed to quantify the tissue properties, but they are often affected by respiratory and heart systole motion even when the interventional probe is held steadily. This paper proposes a microendoscopy motion correction (MMC) algorithm using normalized mutual information (NMI)-based registration and a nonlinear system to model the longitudinal global transformations. Cubature Kalman filter is thus used to solve the underlying longitudinal transformations, which yields more stable and robust motion estimation. After global motion correction, longitudinal deformations among the image sequences are calculated to further refine the local tissue motion. Experimental results showed that compared to global and deformable image registrations, MMC yields more accurate alignment results for both simulated and real data.