Multimodal image registration of the scoliotic torso for surgical planning

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

doi:10.1186/1471-2342-13-1

Cited by 14 publications

(11 citation statements)

References 33 publications

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“…X-ray and TP data using articulated models and constrained thin-plate splines. 3 The articulated model represents the spine as a series of rigid, local, inter-vertebral transformations. First, a local coordinate system is obtained for each of the lumbar and thoracic vertebrae in each of the MRI and X-ray modalities using the manually obtained vertebral landmarks.…”

Section: Methodsmentioning

confidence: 99%

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Patient-specific model of a scoliotic torso for surgical planning

2013

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A method for the construction of a patient-specific model of a scoliotic torso for surgical planning via interpatient registration is presented. Magnetic Resonance Images (MRI) of a generic model are registered to surface topography (TP) and X-ray data of a test patient. A partial model is first obtained via thin-plate spline registration between TP and X-ray data of the test patient. The MRIs from the generic model are then fit into the test patient using articulated model registration between the vertebrae of the generic model's MRIs in prone position and the test patient's X-rays in standing position. A non-rigid deformation of the soft tissues is performed using a modified thin-plate spline constrained to maintain bone rigidity and to fit in the space between the vertebrae and the surface of the torso. Results show average Dice values of 0.975 ± 0.012 between the MRIs following inter-patient registration and the surface topography of the test patient, which is comparable to the average value of 0.976 ± 0.009 previously obtained following intra-patient registration. The results also show a significant improvement compared to rigid inter-patient registration. Future work includes validating the method on a larger cohort of patients and incorporating soft tissue stiffness constraints. The method developed can be used to obtain a geometric model of a patient including bone structures, soft tissues and the surface of the torso which can be incorporated in a surgical simulator in order to better predict the outcome of scoliosis surgery, even if MRI data cannot be acquired for the patient.

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Section: Methodsmentioning

confidence: 99%

“…We have previously obtained a full model of the patient's torso that incorporates bone structures, soft tissues and the external surface of the torso. 3 However, inter-patient registration was not performed. Most previous inter-patient registration methods focused on the brain.…”

Section: Introductionmentioning

confidence: 99%

Patient-specific model of a scoliotic torso for surgical planning

2013

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“…However, when the number of points is too large, the computation time may be too long for the optimization. Moreover, to our best knowledge, the registration of vertebrae was treated as a rigid registration problem in the literature [15] [16], and thus we prefer a rigid registration method rather than a non-rigid registration one. In this paper, we adopt the Fourier moment matching method [2], which is based on the fact that the affine transform between two point sets corresponds to a related affine transform between their Fourier spectrums, and the moments for the corresponding Fourier spectrum distributions can be calculated as probability density function.…”

Section: A Point Set Registrationmentioning

confidence: 99%

Reconstruction of 3D Vertebrae and Spinal Cord Models from CT and STIR-MRI Images

Yen

Lai

2013

2013 2nd IAPR Asian Conference on Pattern Recognition

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In this paper, we propose a system that integrates vertebrae, spinal cord and nerves segmentation results from STIR-MRI and CT images, by estimating the transformation relationship between the segmented vertebra models extracted from these two types of images. Since the segmentation results are already obtained, we build the 3D spinal cord and nerves model. Then we apply a deformable registration algorithm to register the pre-built 3D vertebra models in CT and STIR-MRI. Thus, we apply the local affine transformation to integrate STIR-MRI spinal cord and nerves information with CT vertebrae. This is accomplished by applying a linear interpolation method to achieve the purpose of local affine transformation. In the experimental results, we show the 3D segmentation results of spinal nerve from the STIR-MRI (Short Tau Inversion Recovery -Magnetic Resonance Imaging) images , and also show the integrated 3D spine models from CT and STIR-MRI images.

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“…It was shown that a completely rigid or completely elastic transformation was not able to compensate for the difference in posture between the two modality acquisitions. Finally, the soft tissue provided by the MR images was confined to the volume delimited by the trunk and bone surfaces using a constrained Thin-Plate Splines method [34]. Figure 4a shows a clear misalignment between the vertebrae extracted from X-ray data and those extracted from MRI data when rigid registration is used.…”

Section: Multimodal Image Fusionmentioning

confidence: 99%