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

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° for anteversion and difference of 1.5±1.5° for inclination. When the present technique was evaluated on patients' datasets, differences of 2.2±1.3° and differences of 2.0±0.8° were found for the anteversion and the inclination, respectively. The experimental results, though still preliminary, demonstrated the efficacy of the present approac

Validation of a statistical shape model-based 2D/3D reconstruction method for determination of cup orientation after THA

Purpose: The aim of this study was to validate the accuracy and reproducibility of a statistical shape model-based 2D/3D reconstruction method for determining cup orientation after total hip arthroplasty. With a statistical shape model, this method allows reconstructing a patient-specific 3D-model of the pelvis from a standard AP X-ray radiograph. Cup orientation (inclination and anteversion) is then calculated with respect to the anterior pelvic plane that is derived from the reconstructed model. Materials and methods: The validation study was conducted retrospectively on datasets of 29 patients (31 hips). Among them, there were 15 men (15 hips) and 14 women (16 hips). The average age of the patients was 69.4±8.5(49−82) years. Each dataset has one postoperative X-ray radiograph and one postoperative CT scan. The postoperative CT scan for each patient was used to establish the ground truth for the cup orientation. The cup anteversion and inclination that were calculated from the 2D/3D reconstruction method were compared to the associated ground truth. To validate reproducibility and reliability, two observers performed measurements for each dataset twice in order to measure the reproducibility and the reliability of the 2D/3D reconstruction method. Results: Our validation study demonstrated a mean accuracy of 0.4 ± 1.8°(−2.6° to 3.3°) for inclination and a mean accuracy of 0.6±1.5°(−2.0° to 3.9°) for anteversion. Through the Bland-Altman analysis, no systematic errors in accuracy were detected. The method showed very good consistency for both parameters. Conclusions: Our validation results demonstrate that the statistical shape model-based 2D/3D reconstruction-based method is an accurate, consistent, and reproducible technique to measure cup orientation from postoperative X-ray radiographs. The best results were achieved with radiographs including the bilateral anterior superior iliac spines and the cranial part of non-fractured pelvise

Towards multiple 3D bone surface identification and reconstruction using few 2D X-ray images for intraoperative applications

This article discusses a possible method to use a small number, e.g. 5, of conventional 2D X-ray images to reconstruct multiple 3D bone surfaces intraoperatively. Each bone’s edge contours in X-ray images are automatically identified. Sparse 3D landmark points of each bone are automatically reconstructed by pairing the 2D X-ray images. The reconstructed landmark point distribution on a surface is approximately optimal covering main characteristics of the surface. A statistical shape model, dense point distribution model (DPDM), is then used to fit the reconstructed optimal landmarks vertices to reconstruct a full surface of each bone separately. The reconstructed surfaces can then be visualised and manipulated by surgeons or used by surgical robotic systems

Statistical atlas based registration and planning for ablating bone tumors in minimally invasive interventions

Bone tumor ablation has been a viable treatment in a minimally invasive way compared with surgical resections. In this paper, two key challenges in the computer-Assisted bone tumor ablation have been addressed: 1) establishing the spatial transformation of patient's tumor with respect to a global map of the patient using a minimum number of intra-operative images and 2) optimal treatment planning for large tumors. Statistical atlas is employed to construct the global reference map. The atlas is deformably registered to a pair of intra-operative fluoroscopy images, constructing a patient-specific model, in order to reduce the radiation exposure to the sensitive patients such as pregnant and infants. The optimal treatment planning system incorporates clinical constraints on ablations and trajectories using a multiple objective optimization, which obtains optimal trajectory planning and ablation coverage using integer programming. The proposed system is presented and validated by experiments. © 2012 IEEE.published_or_final_versio

Three-Dimensional Biplanar Reconstruction of the Scoliotic Spine for Standard Clinical Setup

Tese de Doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201

Scaled, patient-specific 3D vertebral model reconstruction based on 2D lateral fluoroscopy

Backgrounds: Accurate three-dimensional (3D) models of lumbar vertebrae are required for image-based 3D kinematics analysis. MRI or CT datasets are frequently used to derive 3D models but have the disadvantages that they are expensive, time-consuming or involving ionizing radiation (e.g., CT acquisition). An alternative method using 2D lateral fluoroscopy was developed. Materials and methods: A technique was developed to reconstruct a scaled 3D lumbar vertebral model from a single two-dimensional (2D) lateral fluoroscopic image and a statistical shape model of the lumbar vertebrae. Four cadaveric lumbar spine segments and two statistical shape models were used for testing. Reconstruction accuracy was determined by comparison of the surface models reconstructed from the single lateral fluoroscopic images to the ground truth data from 3D CT segmentation. For each case, two different surface-based registration techniques were used to recover the unknown scale factor, and the rigid transformation between the reconstructed surface model and the ground truth model before the differences between the two discrete surface models were computed. Results: Successful reconstruction of scaled surface models was achieved for all test lumbar vertebrae based on single lateral fluoroscopic images. The mean reconstruction error was between 0.7 and 1.6mm. Conclusions: A scaled, patient-specific surface model of the lumbar vertebra from a single lateral fluoroscopic image can be synthesized using the present approach. This new method for patient-specific 3D modeling has potential applications in spine kinematics analysis, surgical planning, and navigatio

Anatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs

An important factor for the development of spinal degeneration, pain and the outcome of spinal surgery is known to be the balance of the spine. It must be analyzed in an upright, standing position to ensure physiological loading conditions and visualize load-dependent deformations. Despite the complex 3D shape of the spine, this analysis is currently performed using 2D radiographs, as all frequently used 3D imaging techniques require the patient to be scanned in a prone position. To overcome this limitation, we propose a deep neural network to reconstruct the 3D spinal pose in an upright standing position, loaded naturally. Specifically, we propose a novel neural network architecture, which takes orthogonal 2D radiographs and infers the spine’s 3D posture using vertebral shape priors. In this work, we define vertebral shape priors using an atlas and a spine shape prior, incorporating both into our proposed network architecture. We validate our architecture on digitally reconstructed radiographs, achieving a 3D reconstruction Dice of 0.95, indicating an almost perfect 2D-to-3D domain translation. Validating the reconstruction accuracy of a 3D standing spine on real data is infeasible due to the lack of a valid ground truth. Hence, we design a novel experiment for this purpose, using an orientation invariant distance metric, to evaluate our model’s ability to synthesize full-3D, upright, and patient-specific spine models. We compare the synthesized spine shapes from clinical upright standing radiographs to the same patient’s 3D spinal posture in the prone position from CT

The accuracy of statistical shape models in predicting bone shape: a systematic review

Background This systematic review aims to ascertain how accurately 3D models can be predicted from two-dimensional (2D) imaging utilising statistical shape modelling. Methods A systematic search of published literature was conducted in September 2022. All papers which assessed the accuracy of 3D models predicted from 2D imaging utilising statistical shape models and which validated the models against the ground truth were eligible. Results 2127 papers were screened and a total of 34 studies were included for final data extraction. The best overall achievable accuracy was 0.45 mm (root mean square error) and 0.16 mm (average error). Conclusion Statistical shape modelling can predict detailed 3D anatomical models from minimal 2D imaging. Future studies should report the intended application domain of the model, the level of accuracy required, the underlying demographics of subjects, and the method in which accuracy was calculated, with root mean square error recommended if appropriate

Reconstruction of Patient-Specific Bone Models from X-Ray Radiography

The availability of a patient‐specific bone model has become an increasingly invaluable addition to orthopedic case evaluation and planning [1]. Utilized within a wide range of specialized visualization and analysis tools, such models provide unprecedented wealth of bone shape information previously unattainable using traditional radiographic imaging [2]. In this work, a novel bone reconstruction method from two or more x‐ray images is described. This method is superior to previous attempts in terms of accuracy and repeatability. The new technique accurately models the radiological scene in a way that eliminates the need for expensive multi‐planar radiographic imaging systems. It is also flexible enough to allow for both short and long film imaging using standard radiological protocols, which makes the technology easily utilized in standard clinical setups