A fast inverse consistent deformable image registration method based on symmetric optical flow computation

Department of Radiation Oncology, Washington University, St. Louis, MO 63110, USA.
Physics in Medicine and Biology (Impact Factor: 2.76). 11/2008; 53(21):6143-65. DOI: 10.1088/0031-9155/53/21/017
Source: PubMed


Deformable image registration is widely used in various radiation therapy applications including 4D-CT and treatment planning adaptation. In this work, a simple and efficient inverse consistency deformable registration method is proposed with aims of higher registration accuracy and faster convergence speed. Instead of registering image I to the second image J, two images are symmetrically deformed toward one another in multiple passes, until both deformed images are registered. In every pass, a delta motion field is computed by minimizing a symmetric optical flow system cost function using the modified optical flow algorithms. The images are then further deformed with the delta motion field in positive and negative directions, respectively, and then used for the next pass. The magnitude of the delta motion field is forced to be less than 0.4 voxel for every pass in order to guarantee the smoothness and invertibility of the two overall motion fields which are accumulating the delta motion fields in positive and negative directions, respectively. The final motion fields to register the original images I and J, in either direction, are calculated by inverting one overall motion field and composing the inversion result with the other overall motion field. The final motion fields are inversely consistent and this is ensured by the symmetric way that registration is carried out. Results suggest that the method is able to improve the overall accuracy by 30% or more, reduce the inverse consistency error, and increase the convergence rate. The computation speed may slightly decrease, or increase in some cases because the new method converges faster. Comparing to previously published inverse consistency algorithms, the proposed method is simpler in theory, easier to implement, and faster.

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Available from: Daniel Low, Mar 21, 2014
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    • "In this study, we use the distances between points of interest (POIs) in the CT and CBCT acquisition images and the IC property to validate a the RayStation's treatment planning system (TPS) DIR algorithm (v., RaySearch Laboratories AB, Stockholm, Sweden ) [12]. This study was divided into two parts: firstly, the distance-accuracy of the TPS hybrid DIR algorithm was ascertained by placing POIs on anatomical features in CT and CBCT acquisition images obtained for head and neck cancer patients, and the distances from these POIs mapped from the CBCT to CT were measured. "
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    ABSTRACT: In recent years one of the areas of interest in radiotherapy has been adaptive radiation therapy (ART), with the most efficient way of performing ART being the use of deformable image registration (DIR). In this paper we use the distances between points of interest (POIs) in the computed tomography (CT) and the cone beam computed tomography (CBCT) acquisition images and the inverse consistence (IC) property to validate the RayStation treatment planning system (TPS) DIR algorithm. This study was divided into two parts: Firstly the distance-accuracy of the TPS DIR algorithm was ascertained by placing POIs on anatomical features in the CT and CBCT images from five head and neck cancer patients. Secondly , a method was developed for studying the implication of these distances on the dose by using the IC. This method compared the dose received by the structures in the CT, and the structures that were quadruply-deformed. The accuracy of the TPS was 1.7 ± 0.8 mm, and the distance obtained with the quadruply-deformed IC method was 1.7 ± 0.9 mm, i.e. the difference between the IC method multiplied by two, and that of the TPS validation method, was negligible. Moreover, the IC method shows very little variation in the dose-volume histograms when comparing the original and quadruply-deformed structures. This indicates that this algorithm is useful for planning adaptive radiation treatments using CBCT in head and neck cancer patients, although these variations must be taken into account when making a clinical decision to adapt a treatment plan.
    Full-text · Article · Jan 2015 · Physica Medica
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    • "In addition, in radiation therapy, the implication of registration asymmetry has been discussed for daily dose computation (Yang et al., 2008) and auto re-contouring (Ye and Chen, 2009). To address this issue, existing approaches primarily aim to restore inverse-consistency to registration by computing the integral in both image spaces and taking the average (Alvarez et al., 2007; Bondar et al., 2010; Cachier and Rey, 2000; Christensen and Johnson, 2001; Chui, 2001; Feng et al., 2009; Geng, 2007; Gholipour et al., 2010; Leow et al., 2007; Modat et al., 2012; Mohagheghian et al., 2010; Sabuncu et al., 2009; Tagare et al., 2009; Tao et al., 2009; Trouvé and Younes, 2000; Vercauteren et al., 2008b; Zeng and Chen, 2008; Zhang et al., 2006) or computing the integral in an abstract mid-space chosen to be " in between " the native spaces of the images (Beg and Khan, 2007; Chen and Ye, 2010; Joshi et al., 2004; Lorenzen et al., 2004; Lorenzi et al., 2013; Noblet et al., 2008; Škrinjar et al., 2008; Yang et al., 2008; Ye and Chen, 2009). Other approaches based on similar ideas have been proposed in the literature, including (Ashburner et al., 1999, 2000; Avants et al., 2008; Basri et al., 1998; Christensen and Johnson, 2003; Dedeoglu and Kanade, 2005; He and Christensen, 2003; Rogelj and Kovačič, 2006; Yanovsky et al., 2008b; Yeung et al., 2008). "
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    ABSTRACT: The choice of a reference image typically influences the results of deformable image registration, thereby making it asymmetric. This is a consequence of a spatially non-uniform weighting in the cost function integral that leads to general registration inaccuracy. The inhomogeneous integral measure - which is the local volume change in the transformation, thus varying through the course of the registration - causes image regions to contribute differently to the objective function. More importantly, the optimization algorithm is allowed to minimize the cost function by manipulating the volume change, instead of aligning the images. The approaches that restore symmetry to deformable registration successfully achieve inverse-consistency, but do not eliminate the regional bias that is the source of the error. In this work, we address the root of the problem: the non-uniformity of the cost function integral. We introduce a new quasi-volume-preserving constraint that allows for volume change only in areas with well-matching image intensities, and show that such a constraint puts a bound on the error arising from spatial non-uniformity. We demonstrate the advantages of adding the proposed constraint to standard (asymmetric and symmetrized) demons and diffeomorphic demons algorithms through experiments on synthetic images, and real X-ray and 2D/3D brain MRI data. Specifically, the results show that our approach leads to image alignment with more accurate matching of manually defined neuroanatomical structures, better tradeoff between image intensity matching and registration-induced distortion, improved native symmetry, and lower susceptibility to local optima. In summary, the inclusion of this space- and time-varying constraint leads to better image registration along every dimension that we have measured it. Copyright © 2014. Published by Elsevier Inc.
    Full-text · Article · Oct 2014 · NeuroImage
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    • "), Reg is a regularisation term, and α u , α v are regularisation weights. To solve this problem, the Demon-like force established in an iterative optimisation framework [6] [7] was chosen: "
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    ABSTRACT: This paper presents a novel technique for a consistent symmetric deformable image registration based on an accurate method for a direct inversion of a large motion model deformation field. The proposed image registration algorithm maintains one-to-one map- ping between registered images by symmetrically warping them to another image. This makes the final estimation of forward and backward deformation fields anatomically plausible and applicable to adaptive prostate radiotherapy. The quantitative validation of the method is performed on magnetic resonance data obtained for pelvis area. The experiments demonstrate the improved robustness in terms of inverse consistency error and estimation accuracy of prostate position in comparison to the previously proposed methods.
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