Simulation of four-dimensional CT images from deformable registration between inhale and exhale breath-hold CT scans

• Published in: Medical physics (Lancaster) Vol. 33; no. 3; p. 605
• Main Authors: Sarrut, David, Boldea, Vlad, Miguet, Serge, Ginestet, Chantal
• Format: Journal Article
• Language: English
• Published: United States 01.03.2006
• Subjects: Algorithms; Computer Simulation; Exhalation; Inhalation; Lung Neoplasms - pathology; Lung Neoplasms - radiotherapy; Pattern Recognition, Automated; Radiographic Image Interpretation, Computer-Assisted - methods; Reproducibility of Results; Respiratory Mechanics; Sensitivity and Specificity; Subtraction Technique; Tomography, X-Ray Computed - methods
• DOI: 10.1118/1.2161409
• ISSN: 0094-2405

We propose to simulate an artificial four-dimensional (4-D) CT image of the thorax during breathing. It is performed by deformable registration of two CT scans acquired at inhale and exhale breath-hold. Breath-hold images were acquired with the ABC (Active Breathing Coordinator) system. Dense deformable registrations were performed. The method was a minimization of the sum of squared differences (SSD) using an approximated second-order gradient. Gaussian and linear-elastic vector field regularizations were compared. A new preprocessing step, called a priori lung density modification (APLDM), was proposed to take into account lung density changes due to inspiration. It consisted of modulating the lung densities in one image according to the densities in the other, in order to make them comparable. Simulated 4-D images were then built by vector field interpolation and image resampling of the two initial CT images. A variation in the lung density was taken into account to generate intermediate artificial CT images. The Jacobian of the deformation was used to compute voxel values in Hounsfield units. The accuracy of the deformable registration was assessed by the spatial correspondence of anatomic landmarks located by experts. APLDM produced statistically significantly better results than the reference method (registration without APLDM preprocessing). The mean (and standard deviation) of distances between automatically found landmark positions and landmarks set by experts were 2.7(1.1) mm with APLDM, and 6.3(3.8) mm without. Interexpert variability was 2.3(1.2) mm. The differences between Gaussian and linear elastic regularizations were not statistically significant. In the second experiment using 4-D images, the mean difference between automatic and manual landmark positions for intermediate CT images was 2.6(2.0) mm. The generation of 4-D CT images by deformable registration of inhale and exhale CT images is feasible. This can lower the dose needed for 4-D CT acquisitions or can help to correct 4-D acquisition artifacts. The 4-D CT model can be used to propagate contours, to compute a 4-D dose map, or to simulate CT acquisitions with an irregular breathing signal. It could serve as a basis for 4-D radiation therapy planning. Further work is needed to make the simulation more realistic by taking into account hysteresis and more complex voxel trajectories.