Mutual information based CT registration of the lung at exhale and inhale breathing states using thin‐plate splines

Abstract

The advent of dynamic radiotherapy modeling and treatment techniques requires an infrastructure to weigh the merits of various interventions (breath holding, gating, tracking). The creation of treatment planning models that account for motion and deformation can allow the relative worth of such techniques to be evaluated. In order to develop a treatment planning model of a moving and deforming organ such as the lung, registration tools that account for deformation are required. We tested the accuracy of a mutual information based image registration tool using thin-plate splines driven by the selection of control points and iterative alignment according to a simplex algorithm. Eleven patients each had sequential CT scans at breath-held normal inhale and exhale states. The exhale right lung was segmented from CT and served as the reference model. For each patient, thirty control points were used to align the inhale CT right lung to the exhale CT right lung. Alignment accuracy (the standard deviation of the difference in the actual and predicted inhale position) was determined from locations of vascular and bronchial bifurcations, and found to be 1.7, 3.1, and 3.6 mm about the RL, AP, and IS directions. The alignment accuracy was significantly different from the amount of measured movement during breathing only in the AP and IS directions. The accuracy of alignment including thin-plate splines was more accurate than using affine transformations and the same iteration and scoring methodology. This technique shows promise for the future development of dynamic models of the lung for use in four-dimensional (4-D) treatment planning.

Computed Tomography Studies of Lung Mechanics

“…A second approach generates a three-dimensional warping function by aligning landmarks or other image features and then deforming the structure to minimize the differences in these parameters. Simpler approaches use manually identified landmarks in both image sets (54,55), but this approach is extremely labor intensive. More advanced image registration approaches use combined image intensities and landmark information to determine correspondences.…”

3D Model-Based Approach to Lung Registration and Prediction of Respiratory Cardiac Motion

“…Although not strictly true, this assumption provides a global but accurate conformal mapping between lungs in extreme respiratory states. That mapping can be beneficial in establishing preliminary correspondence between the major anatomical landmarks of the modeled organ and subsequent intensity-based registration of the lungs in oncological applications [13,14]. Further improvement involves non-rigid transformation that allows to recover local deformation due to the shape changes in the lungs and should be considered as a potential extension of the model-based registration framework.…”

Feature‐based rectal contour propagation from planning CT to cone beam CT

“…Control points are manually selected for many TPS applications. 13,25,26 This may introduce interuser variability and is a major source of error. Malsch et al presented an automatic block matching method, 18 which is similar to the control volume based approach proposed by Schreibmann and Xing.…”