Modeling of Respiratory Motion to Support the Minimally Invasive Destruction of Liver Tumors.

OBJECTIVE: Respiratory movements are a significant factor that may hinder the use of image navigation systems during minimally invasive procedures used to destroy focal lesions in the liver. This article aims to present a method of estimating the displacement of the target point due to respiratory movements during the procedure, working in real time. METHOD: The real-time method using skin markers and non-rigid registration algorithms has been implemented and tested for various classes of transformation. The method was validated using clinical data from 21 patients diagnosed with liver tumors. For each patient, each marker was treated as a target and the remaining markers as target position predictors, resulting in 162 configurations and 1095 respiratory cycles analyzed. In addition, the possibility of estimating the respiratory phase signal directly from intraoperative US images and the possibility of synchronization with the 4D CT respiratory sequence are also presented, based on ten patients. RESULTS: The median value of the target registration error (TRE) was 3.47 for the non-rigid registration method using the combination of rigid transformation and elastic body spline curves, and an adaptation of the assessing quality using image registration circuits (AQUIRC) method. The average maximum distance was 3.4 (minimum: 1.6, maximum 6.8) mm. CONCLUSIONS: The proposed method obtained promising real-time TRE values. It also allowed for the estimation of the TRE at a given geometric margin level to determine the estimated target position. Directions for further quantitative research and the practical possibility of combining both methods are also presented.

Target registration error reduction for percutaneous abdominal intervention.

A real-time methodology that finds spatio-temporal correspondence between the positions of the target point in the pre-treatment 3DCT image and during the procedure was proposed. It based on minimizing the target registration error in III tier registration circuits. Particle Swarm Optimization and Differential Evaluation were used to find optimal values of Elastic Body Spline parameters in the generation of abdominal deformation field. Different transformation classes have been tested: rigid, affine, Thin Plate Spline, Elastic Body Spline. The lowest TRE was obtained for the swarm optimization algorithm - differential evolution for the rigid and affine version: 3.47 and 3.73 mm, respectively.

Selection of the respiratory phase in minimally invasive interventions for target registration error minimization.

BACKGROUND: In minimally invasive surgery, the main challenge is precisely locating the target during the intervention. For abdominal intervention, one of most important factors causing target motion is breathing. The aim of the study is to efficiently predict target localization during the respiratory in breathing cycle. METHOD: Analysis of target registration error (TRE) for the registration circuits method was used to find the breathing phase corresponding to the preoperative Computed Tomography spatial configuration. Then, Elastic Body Spline (EBS) for modeling deformation field and Particle Swarm Optimization method were used to find the desired values of EBS parameters: ∝ and stiffness were used. RESULTS: The proposed methodology was verified during experiments conducted on 21 patients diagnosed with liver tumors. This ability of TRE reduction has been achieved for the respiratory phases founded in registration chain analysis. CONCLUSIONS: The proposed method presents the usability of spatio-temporal analysis of collected real breathing data in order to estimate the position of a target during the respiratory cycle. This method has been developed to perform operations in real-time on a standard workstation.