Automated contour mapping using sparse volume sampling for 4D radiation therapy

Abstract

The purpose of this work is to develop a novel strategy to automatically map organ contours from one phase of respiration to all other phases on a four-dimensional computed tomography (4D CT). A region of interest (ROI) was manually delineated by a physician on one phase specific image set of a 4D CT. A number of cubic control volumes of the size of approximately 1 cm were automatically placed along the contours. The control volumes were then collectively mapped to the next phase using a rigid transformation. To accommodate organ deformation, a model-based adaptation of the control volume positions was followed after the rigid mapping procedure. This further adjustment of control volume positions was performed by minimizing an energy function which balances the tendency for the control volumes to move to their correspondences with the desire to maintain similar image features and shape integrity of the contour. The mapped ROI surface was then constructed based on the central positions of the control volumes using a triangulated surface construction technique. The proposed technique was assessed using a digital phantom and 4D CT images of three lung patients. Our digital phantom study data indicated that a spatial accuracy better than 2.5 mm is achievable using the proposed technique. The patient study showed a similar level of accuracy. In addition, the computational speed of our algorithm was significantly improved as compared with a conventional deformable registration-based contour mapping technique. The robustness and accuracy of this approach make it a valuable tool for the efficient use of the available spatial-tempo information for 4D simulation and treatment.

Tracking the motion trajectories of junction structures in 4D CT images of the lung

“…A metric of sumsquared tissue volume difference was also developed (Yin et al 2011(Yin et al , 2009 to improve lung registration in regions lacking in structural landmarks by particularly considering intensity changes during breathing. Depending on the applications, the metric can be defined over the whole image domain or in a region of interest (Chao et al 2008(Chao et al , 2007. Lung deformation can be defined by either parametric or non-parametric transformations.…”

Four‐dimensional inverse treatment planning with inclusion of implanted fiducials in IMRT segmented fields

“…The ROI contours on the other nine phases of the 4D CT data are automatically generated using the deformation field obtained by using deformable registration. 29…”

Adaptive radiotherapy for NSCLC patients: utilizing the principle of energy conservation to evaluate dose mapping operations

“…For example, the original “demons”(Thirion, 1998) has been enhanced to be efficient (Wang et al, 2005), inverse consistent(Yang et al, 2008) and diffeomorphic (Vercauteren et al, 2009, Yeo et al, 2010); the B-spline-based algorithm (Rueckert et al, 1999) has been extended to be hierarchical (Klein et al, 2010), diffeomorphic (Rueckert et al, 2006), and with non-uniform knot placement (Jacobson and Murphy, 2011) or with simplified regularization forms (Chun and Fessler, 2009). These improvements have facilitated the functionality of DIR in clinical settings, such as efficient contour propagation (Hardcastle et al, 2013, Chao et al, 2007), and organ segmentation even in regions with low image intensity contrast (Liu et al, 2012, Yeo et al, 2013). While these DIR algorithms may deform a treatment image to a reference image with a high similarity, they cannot guarantee accurate dose to be reconstructed on the reference image, because the dose reconstructed at each image voxel also depends on the dose mapping method used (Siebers and Zhong, 2008).…”