3D registration of MR and X-ray spine images using an articulated model

Harmouche, Rola; Cheriet, Foued; Labelle, Hubert; Dansereau, J.

doi:10.1016/j.compmedimag.2012.03.003

Cited by 22 publications

(18 citation statements)

References 45 publications

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“…In order to register each MRI slice such that the spine information extracted from MRI data and that extracted from X-ray data are aligned, an articulated model previously used to align 3D models of the spine obtained from both modalities is calculated [28]. This articulated model allows us to define the spine as a combination of local intervertebral transformations which can be obtained in a number of ways.…”

Section: Methodsmentioning

confidence: 99%

“…Tang et al [37] apply rigid registration to fit a model of the vertebrae only onto Sagittal MRI slices in order to measure spinal curvature. Harmouche et al [28] used an articulated model representation of the spine in order to register the spine extracted from MRI to that obtained from X-ray data of the same patient. The remaining tissues on the MRI were not registered.…”

Section: Introductionmentioning

confidence: 99%

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Multimodal image registration of the scoliotic torso for surgical planning

2013

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BackgroundThis paper presents a method that registers MRIs acquired in prone position, with surface topography (TP) and X-ray reconstructions acquired in standing position, in order to obtain a 3D representation of a human torso incorporating the external surface, bone structures, and soft tissues.MethodsTP and X-ray data are registered using landmarks. Bone structures are used to register each MRI slice using an articulated model, and the soft tissue is confined to the volume delimited by the trunk and bone surfaces using a constrained thin-plate spline.ResultsThe method is tested on 3 pre-surgical patients with scoliosis and shows a significant improvement, qualitatively and using the Dice similarity coefficient, in fitting the MRI into the standing patient model when compared to rigid and articulated model registration. The determinant of the Jacobian of the registration deformation shows higher variations in the deformation in areas closer to the surface of the torso.ConclusionsThe novel, resulting 3D full torso model can provide a more complete representation of patient geometry to be incorporated in surgical simulators under development that aim at predicting the effect of scoliosis surgery on the external appearance of the patient’s torso.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multimodal image registration of the scoliotic torso for surgical planning

2013

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“…29,30 Alternative methods for estimating rigid motion in a deformable context include a point interpolation weighted by a distance function of rigid structures 31 and articulated models of the spines. 32,33 However, the methods require accurate definition of landmark feature points or rigid structures in both the fixed and moving images, which could be difficult to achieve in clinical workflow. While our proposed method avoids such user-defined input, the rigidity constraints were applied by defining tissue stiffness according to a simple thresholding of preoperative CT and/or CBCT images.…”

Section: Discussionmentioning

confidence: 99%

Deformable registration for image-guided spine surgery: preserving rigid body vertebral morphology in free-form transformations

et al. 2014

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Purpose: Deformable registration of preoperative and intraoperative images facilitates accurate localization of target and critical anatomy in image-guided spine surgery. However, conventional deformable registration fails to preserve the morphology of rigid bone anatomy and can impart distortions that confound high-precision intervention. We propose a constrained registration method that preserves rigid morphology while allowing deformation of surrounding soft tissues. Method: The registration method aligns preoperative 3D CT to intraoperative cone-beam CT (CBCT) using free-form deformation (FFD) with penalties on rigid body motion imposed according to a simple intensity threshold. The penalties enforced 3 properties of a rigid transformation -namely, constraints on affinity (AC), orthogonality (OC), and properness (PC). The method also incorporated an injectivity constraint (IC) to preserve topology. Physical experiments (involving phantoms, an ovine spine, and a human cadaver) as well as digital simulations were performed to evaluate the sensitivity to registration parameters, preservation of rigid body morphology, and overall registration accuracy of constrained FFD in comparison to conventional unconstrained FFD (denoted uFFD) and Demons registration. Result: FFD with orthogonality and injectivity constraints (denoted FFD+OC+IC) demonstrated improved performance compared to uFFD and Demons. Affinity and properness constraints offered little or no additional improvement. The FFD+OC+IC method preserved rigid body morphology at near-ideal values of zero dilatation ( = 0.05, compared to 0.39 and 0.56 for uFFD and Demons, respectively) and shear ( = 0.08, compared to 0.36 and 0.44 for uFFD and Demons, respectively). Target registration error (TRE) was similarly improved for FFD+OC+IC (0.7 mm), compared to 1.4 and 1.8 mm for uFFD and Demons. Results were validated in human cadaver studies using CT and CBCT images, with FFD+OC+IC providing excellent preservation of rigid morphology and equivalent or improved TRE. Conclusions: A promising method for deformable registration in CBCT-guided spine surgery has been identified incorporating a constrained FFD to preserve bone morphology. The approach overcomes distortions intrinsic to unconstrained FFD and could better facilitate high-precision image-guided spine surgery.

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“…Piece-wise rigid registration methods can provide perfect preservation of rigid bone morphology; however, deformation of soft-tissue might not be accurately modeled. Examples of piece-wise registration methods include: i) a point interpolation weighted by a distance function of rigid structures (Little et al, 1996, Moreno et al, 2008) or rigidity coefficients (Wei et al, 2008); ii) articulated models of the spine to estimate piecewise-rigid deformation from a rigid transformation of each vertebra (du Bois d’Aische et al, 2007, Harmouche et al, 2010, Harmouche et al, 2012); and iii) constrained group-wise registrations that concurrently estimate a rigid transformation of each rigid body (van de Giessen et al, 2009, Gill et al, 2012). The methods (i,ii) require accurate definition of rigid structures and/or landmark feature points, which could be difficult to achieve in clinical workflow.…”

Section: Introductionmentioning

confidence: 99%

Deformable image registration with local rigidity constraints for cone-beam CT-guided spine surgery

et al. 2014

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Image-guided spine surgery is associated with reduced co-morbidity and improved surgical outcome. However, precise localization of target anatomy and adjacent nerves and vessels relative to planning information (e.g., device trajectories) can be challenged by anatomical deformation. Rigid registration alone fails to account for deformation associated with changes in spine curvature, and conventional deformable registration fails to account for rigidity of the vertebrae, causing unrealistic distortions in the registered image that can confound high-precision surgery. We developed and evaluated a deformable registration method capable of preserving rigidity of bones while resolving the deformation of surrounding soft tissue. The method aligns preoperative CT to intraoperative CBCT using free-form deformation (FFD) with constraints on rigid body motion imposed according to a simple intensity threshold of bone intensities. The constraints enforced 3 properties of a rigid transformation – namely, constraints on affinity (AC), orthogonality (OC), and properness (PC). The method also incorporated an injectivity constraint (IC) to preserve topology. Physical experiments involving phantoms, an ovine spine, and a human cadaver as well as digital simulations were performed to evaluate the sensitivity to registration parameters, preservation of rigid body morphology, and overall registration accuracy of constrained FFD in comparison to conventional unconstrained FFD (denoted uFFD) and Demons registration. FFD with orthogonality and injectivity constraints (denoted FFD+OC+IC) demonstrated improved performance compared to uFFD and Demons. Affinity and properness constraints offered little or no additional improvement. The FFD+OC+IC method preserved rigid body morphology at near-ideal values of zero dilatation (𝒟 = 0.05, compared to 0.39 and 0.56 for uFFD and Demons, respectively) and shear (𝒮 = 0.08, compared to 0.36 and 0.44 for uFFD and Demons, respectively). Target registration error (TRE) was similarly improved for FFD+OC+IC (0.7 mm), compared to 1.4 and 1.8 mm for uFFD and Demons. Results were validated in human cadaver studies using CT and CBCT images, with FFD+OC+IC providing excellent preservation of rigid morphology and equivalent or improved TRE. The approach therefore overcomes distortions intrinsic to unconstrained FFD and could better facilitate high-precision image-guided spine surgery.

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3D registration of MR and X-ray spine images using an articulated model

Cited by 22 publications

References 45 publications

Multimodal image registration of the scoliotic torso for surgical planning

Multimodal image registration of the scoliotic torso for surgical planning

Deformable registration for image-guided spine surgery: preserving rigid body vertebral morphology in free-form transformations

Deformable image registration with local rigidity constraints for cone-beam CT-guided spine surgery

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