An enhanced block matching algorithm for fast elastic registration in adaptive radiotherapy

Abstract

Image registration has many medical applications in diagnosis, therapy planning and therapy. Especially for time-adaptive radiotherapy, an efficient and accurate elastic registration of images acquired for treatment planning, and at the time of the actual treatment, is highly desirable. Therefore, we developed a fully automatic and fast block matching algorithm which identifies a set of anatomical landmarks in a 3D CT dataset and relocates them in another CT dataset by maximization of local correlation coefficients in the frequency domain. To transform the complete dataset, a smooth interpolation between the landmarks is calculated by modified thin-plate splines with local impact. The concept of the algorithm allows separate processing of image discontinuities like temporally changing air cavities in the intestinal track or rectum. The result is a fully transformed 3D planning dataset (planning CT as well as delineations of tumour and organs at risk) to a verification CT, allowing evaluation and, if necessary, changes of the treatment plan based on the current patient anatomy without time-consuming manual re-contouring. Typically the total calculation time is less than 5 min, which allows the use of the registration tool between acquiring the verification images and delivering the dose fraction for online corrections. We present verifications of the algorithm for five different patient datasets with different tumour locations (prostate, paraspinal and head-and-neck) by comparing the results with manually selected landmarks, visual assessment and consistency testing. It turns out that the mean error of the registration is better than the voxel resolution (2 x 2 x 3 mm(3)). In conclusion, we present an algorithm for fully automatic elastic image registration that is precise and fast enough for online corrections in an adaptive fractionated radiation treatment course.

Assessment of a commercially available automatic deformable registration system

“…The first is the consistency approach: in three given images (A, B, C), a comparison of transformations produced by registering A to B, B to C, and C to A provides a measure of registration error, assuming that errors are random and distributed evenly between each transformation. ( 47 ) For the most part, consistency methods have been used for rigid registration validation, ( 10 ) but have recently been applied to deformable models by Malsch et al ( 48 ) A novel registration assessment tool recently introduced is the concept of unbalanced energy ( 49 ) whereby, instead of using gold standards, the physical fidelity of the deformation field is quantified through finite element models (FEMs). It has been applied with success to deformable registration of truncated pelvic CT images that include only a small region surrounding the prostate gland.…”

Assessment of dose reconstruction errors in image-guided radiation therapy

“…Because of various factors, h i is not error free. Registration errors, validated with different approaches such as phantoms (Wang et al 2005, Xiong et al 2006, landmarks (Brock et al 2005, Bharatha et al 2001, Malsch et al 2006, contours (Lu et al 2006, Foskey et al 2005, Gao et al 2006 or simulated reference frames (Wang et al 2005, Zhong et al 2007, Chi et al 2006, have been reported. Compounding the DIR errors, delivered dose distributions are frequently non-uniform, with large dose changes between adjacent voxels, for example, on a beam edge.…”

Three-dimensional object registration using wavelet features

“…Malsch et al [47] perform fully automatic non-rigid registration of voxelized objects by identifying anatomical landmarks in both images and matching them elastically. All points in the object are first classified as to tissue type using rough thresholds.…”

“…Hausdorff Distance [2] 2003 NR Darboux Frames [10] 1996 70-153 sec Feature Point Mesh [93] 2003 NR Footprints [3] 1997 73-340 sec Point Fingerprint [82] 2003 NR KH Method [38] 2002 225 sec Lines of 0 H [40] 2002 NR Splash [77] 1992 35-1800 sec Umbilics [39] 2003 NR Dynamical Systems [16] 2003 NR Hierarchical Patches [75] 2006 NR Spin Images [33] 1997 415 sec Surface Signatures [99] 2002 120 sec WAV [98] 2003 NR Combination [85] 2006 1600-2200 sec Voxel Intensity Methods Mutual Information [88,95] 1995 400 sec Hardware Accelerated [23] 1998 400 sec Cumulative Distribution [89] 2003 NR Gradient Descent [6] 2000 150 sec PET/CT Auto Elastic [70] 2005 2700-4500 sec Q-MI [44] 2008 NR Block Match [47] 2006 1000 sec Table 2.2: Speed of registration methods. The times listed are those reported in the literature; NR indicates that computation time was not reported.…”