Simulating deformations of MR brain images for validation of atlas-based segmentation and registration algorithms

Xue, Zhong; Shen, Dinggang; Karaçali, B.; Stern, Josh; Rottenberg, David A.; Davatzikos, Christos

doi:10.1016/j.neuroimage.2006.08.007

Cited by 84 publications

(69 citation statements)

References 15 publications

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“…Diffeomorphisms are also considered to be a good working framework when no additional information about the spatial transformation is available. With the development of computational anatomy and in the absence of a justified physical model of inter-subject variability, statistics on diffeomorphisms also become an important topic (Arsigny et al, 2006;Lepore et al, 2008;Vaillant et al, 2004;Xue et al, 2006). Diffeomorphic registration algorithms are at the core of this research field since they often provide the input data.…”

Section: Introducing Diffeomorphisms Into the Demonsmentioning

confidence: 99%

Diffeomorphic demons: Efficient non-parametric image registration

et al. 2009

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We propose an efficient non-parametric diffeomorphic image registration algorithm based on Thirion's demons algorithm. In the first part of this paper, we show that Thirion's demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. We provide strong theoretical roots to the different variants of Thirion's demons algorithm. This analysis predicts a theoretical advantage for the symmetric forces variant of the demons algorithm. We show on controlled experiments that this advantage is confirmed in practice and yields a faster convergence. In the second part of this paper, we adapt the optimization procedure underlying the demons algorithm to a space of diffeomorphic transformations. In contrast to many diffeomorphic registration algorithms, our solution is computationally efficient since in practice it only replaces an addition of displacement fields by a few compositions. Our experiments show that in addition to being diffeomorphic, our algorithm provides results that are similar to the ones from the demons algorithm but with transformations that are much smoother and closer to the gold standard, available in controlled experiments, in terms of Jacobians.

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Section: Introducing Diffeomorphisms Into the Demonsmentioning

confidence: 99%

Diffeomorphic demons: Efficient non-parametric image registration

et al. 2009

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“…Without loss of generality, suppose the baseline image of the patient to be tested is . A lung field motion vector-constrained deformation simulation method is applied to generate the serial deformation fields [13]. Finally, the deformations are transformed onto the subject space using T P − Φ .…”

Section: Resultsmentioning

confidence: 99%

Online 4-D CT Estimation for Patient-Specific Respiratory Motion Based on Real-Time Breathing Signals

Xue

Xie

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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Abstract. In image-guided lung intervention, the electromagnetic (EM) tracked needle can be visualized in a pre-procedural CT by registering the EM tracking and the CT coordinate systems. However, there exist discrepancies between the static pre-procedural CT and the patient due to respiratory motion. This paper proposes an online 4-D CT estimation approach to patient-specific respiratory motion compensation. First, the motion patterns between 4-D CT data and respiratory signals such as fiducials from a number of patients are trained in a template space after image registration. These motion patterns can be used to estimate the patient-specific serial CTs from a static 3-D CT and the real-time respiratory signals of that patient, who do not generally take 4-D CTs. Specifically, the respiratory lung field motion vectors are projected onto the Kernel Principal Component Analysis (K-PCA) space, and a motion estimation model is constructed to estimate the lung field motion from the fiducial motion using the ridge regression method based on the least squares support vector machine (LS-SVM). The algorithm can be performed onsite prior to the intervention to generate the serial CT images according to the respiratory signals in advance, and the estimated CTs can be visualized in real-time during the intervention. In experiments, we evaluated the algorithm using leave-one-out strategy on 30 4-D CT data, and the results showed that the average errors of the lung field surfaces are 1.63mm.

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“…Intra‐modality deformable registration validation using known transformations is common in inter‐subject brain studies, ( 26 , 41 , 42 , 43 , 44 ) but has had limited use in applications directly relevant to radiation therapy. By registering multiple B‐spline warped images to original thoracic CT images, we quantified the commercial system's performance over a range of potential initial clinical deformations.…”

Section: Discussionmentioning

confidence: 99%

Assessment of a commercially available automatic deformable registration system

Fallone

Rivest

Riauka

et al. 2010

J Applied Clin Med Phys

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In recent years, a number of approaches have been applied to the problem of deformable registration validation. However, the challenge of assessing a commercial deformable registration system – in particular, an automatic registration system in which the deformable transformation is not readily accessible – has not been addressed. Using a collection of novel and established methods, we have developed a comprehensive, four‐component protocol for the validation of automatic deformable image registration systems over a range of IGRT applications. The protocol, which was applied to the Reveal‐MVS system, initially consists of a phantom study for determination of the system's general tendencies, while relative comparison of different registration settings is achieved through postregistration similarity measure evaluation. Synthetic transformations and contour‐based metrics are used for absolute verification of the system's intra‐modality and inter‐modality capabilities, respectively. Results suggest that the commercial system is more apt to account for global deformations than local variations when performing deformable image registration. Although the protocol was used to assess the capabilities of the Reveal‐MVS system, it can readily be applied to other commercial systems. The protocol is by no means static or definitive, and can be further expanded to investigate other potential deformable registration applications.PACS numbers: 87.19.xj, 87.56.Da, 87.57.nj

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Simulating deformations of MR brain images for validation of atlas-based segmentation and registration algorithms

Cited by 84 publications

References 15 publications

Diffeomorphic demons: Efficient non-parametric image registration

Diffeomorphic demons: Efficient non-parametric image registration

Online 4-D CT Estimation for Patient-Specific Respiratory Motion Based on Real-Time Breathing Signals

Assessment of a commercially available automatic deformable registration system

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