Nonlinear motion compensation using cubature Kalman filter forin vivofluorescence microendoscopy in peripheral lung cancer intervention

Abstract

Fluorescence microendoscopy can potentially be a powerful modality in minimally invasive percutaneous intervention for cancer diagnosis because it has an exceptional ability to provide micron-scale resolution images in tissues inaccessible to traditional microscopy. After targeting the tumor with guidance by macroscopic images such as computed tomorgraphy or magnetic resonance imaging, fluorescence microendoscopy can help select the biopsy spots or perform an on-site molecular imaging diagnosis. However, one challenge of this technique for percutaneous lung intervention is that the respiratory and hemokinesis motion often renders instability of the sequential image visualization and results in inaccurate quantitative measurement. Motion correction on such serial microscopy image sequences is, therefore, an important post-processing step. We propose a nonlinear motion compensation algorithm using a cubature Kalman filter (NMC-CKF) to correct these periodic spatial and intensity changes, and validate the algorithm using preclinical imaging experiments. The algorithm integrates a longitudinal nonlinear system model using the CKF in the serial image registration algorithm for robust estimation of the longitudinal movements. Experiments were carried out using simulated and real microendoscopy videos captured from the CellVizio 660 system in rabbit VX2 cancer intervention. The results show that the NMC-CKF algorithm yields more robust and accurate alignment results.

Multiphoton intravital microscopy in small animals: motion artefact challenges and technical solutions

“…Given the raster‐scanning pattern used to generate images in two‐photon intravital microscopy, artefacts may occur while acquiring individual frames (intraframe artefacts). To limit the burden caused by these artefacts, different methods have been used, including the Lucas–Kanade framework, Hidden‐Markov Model and the Sequential IMaging Analysis Python package, which address artefacts on a line‐by‐line basis (Greenberg & Kerr, 2009; He et al ., 2013; Kaifosh et al ., 2014). Another method used to reduce intraframe artefacts is the cubature‐Kalman‐filter modelling in a nonlinear system whose constraints help the registration algorithm estimate geometrical transformations (Vercauteren et al ., 2006; Greenberg & Kerr, 2009; Kaifosh et al ., 2014).…”