Deformable 2D-3D Registration of the Pelvis with a Limited Field of View, Using Shape Statistics

Sadowsky, Ofri; Chintalapani, Gouthami; Taylor, Russell H.

doi:10.1007/978-3-540-75759-7_63

Cited by 45 publications

(59 citation statements)

References 10 publications

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“…5,6,16,24,25,32,45,60,62,63 Except the method presented by Sadowsky et al, 45 and the method presented by Mahfouz et al, 32 which depend on a deformable 2D/3D registration between the digitally reconstructed radiographs (DRRs) of a SSM and the X-ray images, all other methods have their reliance on feature-based statistical instantiation from a Point Distribution Model (PDM) in common. 5,6,16,24,25,60,62,63 Common to all these previous works on reconstructing a patient-specific 3D model from X-ray images are: (a) at least two images are used 5,6,16,18,21,24,25,27,29,32,[36][37][38]43,45,60,62,63 ; and/or (b) all images are calibrated. 5,6,16,18,21,24,25,27,29,…”

Section: Introductionmentioning

confidence: 99%

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Statistically Deformable 2D/3D Registration for Estimating Post-operative Cup Orientation from a Single Standard AP X-ray Radiograph

Zheng

2010

Ann Biomed Eng

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The widely used procedure of estimating post-operative cup orientation based on a single standard AP X-ray radiograph is known inaccurate, largely due to the wide variability in individual pelvic orientation relative to X-ray plate. CT-based 2D/3D rigid image registration methods have been developed to measure post-operative cup orientation. Although encouraging results have been reported, their extensive usage in clinical routine is still limited. This may be explained by their requirement of having a CT study of the patient at some point during treatment, which is not available for vast majority of Total Hip Arthroplasty procedures performed nowadays. To address this limitation, this article proposes a statistically deformable 2D/3D registration approach for estimating post-operative cup orientation. No CT study of the patient is required any more. Compared to ground truths established from post-operative CT images, the cup orientations measured by the present technique in a cadaver experiment showed differences of 1.7 +/- 1.4 degrees for anteversion and difference of 1.5 +/- 1.5 degrees for inclination. When the present technique was evaluated on patients' datasets, differences of 2.2 +/- 1.3 degrees and differences of 2.0 +/- 0.8 degrees were found for the anteversion and the inclination, respectively. The experimental results, though still preliminary, demonstrated the efficacy of the present approach.

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Section: Introductionmentioning

confidence: 99%

“…A priori information is often required to handle this otherwise ill-posed problem. Previously, krigingbased methods, 18,21,27,29,[36][37][38]40,43 as well as statistical shape model (SSM) based methods, 5,6,16,24,25,32,45 have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Statistically Deformable 2D/3D Registration for Estimating Post-operative Cup Orientation from a Single Standard AP X-ray Radiograph

Zheng

2010

Ann Biomed Eng

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“…A tetrahedral mesh density model was used for representing both shape and density distributions [31], and PCA was simultaneously applied to both of them to construct statistical shape and density model (SSDM) [32]. In the latest work, the femur in which bone cement was injected was tomographically reconstructed from incomplete projection datasets by using SSDM [33], [34].…”

Section: D X-ray Imagingmentioning

confidence: 99%

A Survey on Statistical Modeling and Machine Learning Approaches to Computer Assisted Medical Intervention: Intraoperative Anatomy Modeling and Optimization of Interventional Procedures

Morooka

Nakamoto

Sato

2013

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper reviews methods for computer assisted medical intervention using statistical models and machine learning technologies, which would be particularly useful for representing prior information of anatomical shape, motion, and deformation to extrapolate intraoperative sparse data as well as surgeons' expertise and pathology to optimize interventions. Firstly, we present a review of methods for recovery of static anatomical structures by only using intraoperative data without any preoperative patient-specific information. Then, methods for recovery of intraoperative motion and deformation are reviewed by combining intraoperative sparse data with preoperative patient-specific stationary data, which is followed by a survey of articles which incorporated biomechanics. Furthermore, the articles are reviewed which addressed the used of statistical models for optimization of interventions. Finally, we conclude the survey by describing the future perspective.

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“…In order to enhance the speed of generating the DRR, a tetrahedral mesh representation of the target anatomy was used, (described in detail in our prior publication [8][9][10]). Then, the mutual information between the DRRs and fluoroscope images was optimized to yield the maximum mutual information using a downhill simplex algorithm.…”

Section: Intraoperative Navigationmentioning

confidence: 99%

An image-guided femoroplasty system: development and initial cadaver studies

et al. 2010

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This paper describes the development and initial cadaver studies using a prototype image-guided surgery system for femoroplasty, which is a potential alternative treatment for reducing fracture risk in patients with severe osteoporosis. Our goal is to develop an integrated surgical guidance system that will allow surgeons to augment the femur using patient-specific biomechanical planning and intraoperative analysis tools. This paper focuses on the intraoperative module, which provides real-time navigation of an injection device and estimates the distribution of the injected material relative to the preoperative plan. Patient registration is performed using intensity-based 2D/3D registration of X-ray images and preoperative CT data. To co-register intraoperative X-ray images and optical tracker coordinates, we integrated a custom optically-tracked fluoroscope fiducial allowing real-time visualization of the injection device with respect to the patient's femur. During the procedure, X-ray images were acquired to estimate the 3D distribution of the injected augmentation material (e.g. bone cement). Based on the injection progress, the injection plan could be adjusted if needed to achieve optimal distribution. In phantom experiments, the average target registration error at the center of the femoral head was 1.4 mm and the rotational error was 0.8 degrees when two images were used. Three cadaveric studies demonstrated efficacy of the navigation system. Our preliminary simulation study of the 3D shape reconstruction algorithm demonstrated that the 3D distribution of the augmentation material could be estimated within 12% error from six X-ray images.

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Deformable 2D-3D Registration of the Pelvis with a Limited Field of View, Using Shape Statistics

Cited by 45 publications

References 10 publications

Statistically Deformable 2D/3D Registration for Estimating Post-operative Cup Orientation from a Single Standard AP X-ray Radiograph

Statistically Deformable 2D/3D Registration for Estimating Post-operative Cup Orientation from a Single Standard AP X-ray Radiograph

A Survey on Statistical Modeling and Machine Learning Approaches to Computer Assisted Medical Intervention: Intraoperative Anatomy Modeling and Optimization of Interventional Procedures

An image-guided femoroplasty system: development and initial cadaver studies

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