Comparison of helical, maximum intensity projection (MIP), and averaged intensity (AI) 4D CT imaging for stereotactic body radiation therapy (SBRT) planning in lung cancer.

BACKGROUND AND PURPOSE: To compare helical, MIP and AI 4D CT imaging, for the purpose of determining the best CT-based volume definition method for encompassing the mobile gross tumor volume (mGTV) within the planning target volume (PTV) for stereotactic body radiation therapy (SBRT) in stage I lung cancer. MATERIALS AND METHODS: Twenty patients with medically inoperable peripheral stage I lung cancer were planned for SBRT. Free-breathing helical and 4D image datasets were obtained for each patient. Two composite images, the MIP and AI, were automatically generated from the 4D image datasets. The mGTV contours were delineated for the MIP, AI and helical image datasets for each patient. The volume for each was calculated and compared using analysis of variance and the Wilcoxon rank test. A spatial analysis for comparing center of mass (COM) (i.e. isocenter) coordinates for each imaging method was also performed using multivariate analysis of variance. RESULTS: The MIP-defined mGTVs were significantly larger than both the helical- (p=0.001) and AI-defined mGTVs (p=0.012). A comparison of COM coordinates demonstrated no significant spatial difference in the x-, y-, and z-coordinates for each tumor as determined by helical, MIP, or AI imaging methods. CONCLUSIONS: In order to incorporate the extent of tumor motion from breathing during SBRT, MIP is superior to either helical or AI images for defining the mGTV. The spatial isocenter coordinates for each tumor were not altered significantly by the imaging methods.

New method to assess the registration of CT-MR images of the head.

Due to their complementary information content, both x-ray computed tomography (CT) and magnetic resonance (MR) imaging are employed in certain clinical cases to improve the understanding of pathology involved. o spatially relate the two datasets, image registration and image fusion are employed. However, registration errors, either global or local, are common and are nonuniform within the image volume. In this paper, we propose a new algorithm that assesses the quality of the registration locally within the CT-MR volume and provides visual, color-coded feedback to the user about the location and extent of good and bad correspondence between the two images. The proposed registration assessment algorithm is based on a correspondence analysis of bone structures in the CT and MR images. For that purpose, a custom segmentation algorithm for bone in MR images has been developed that is based on a stochastic threshold computation method. This segmentation method for MR images and the CT-MR registration assessment algorithm were validated on simulated MR datasets and real CT-MR image pairs of the head. Some partial-volume effects occur at the borders of the bone structures and at the bone interfaces with air, which cannot be separated from bone in the MR image. The presented assessment method of CT-MR image registration offers the user a new tool to evaluate the overall and local quality of the registration. With this information, the user does not have to blindly trust the fused CT-MR datasets but can easily identify areas of inaccurate correspondence. The application of the algorithm is so far limited to T1-weighted MR and CT images of the head area.