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

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

Prediction of the 3D shape of the L1 vertebral body from adjacent vertebrae

The aim of treatments of vertebral fractures is the anatomical reduction to restore the physiological biomechanics of the spine and the stabilization of the fracture to allow bone healing. However, the three-dimensional shape of the fractured vertebral body before the fracture is unknown in the clinical setting. Information about the pre-fracture vertebral body shape could help surgeons to select the optimal treatment. The goal of this study was to develop and validate a method based on Singular Value Decomposition (SVD) to predict the shape of the vertebral body of L1 from the shapes of T12 and L2. The geometry of the vertebral bodies of T12, L1 and L2 vertebrae of 40 patients were extracted from CT scans available from the VerSe2020 open-access dataset. Surface triangular meshes of each vertebra were morphed onto a template mesh. The set of vectors with the node coordinates of the morphed T12, L1 and L2 were compressed with SVD and used to build a system of linear equations. This system was used to solve a minimization problem and to reconstruct the shape of L1. A leave-one-out cross-validation was performed. Moreover, the approach was tested against an independent dataset with large osteophytes. The results of the study show a good prediction of the shape of the vertebral body of L1 from the shapes of the two adjacent vertebrae (mean error equal to 0.51 ± 0.11 mm on average, Hausdorff distance equal to 2.11 ± 0.56 mm on average), compared to current CT resolution typically used in the operating room. The error was slightly higher for patients presenting large osteophytes or severe bone degeneration (mean error equal to 0.65 ± 0.10 mm, Hausdorff distance equal to 3.54 ± 1.03 mm). The accuracy of the prediction was significantly better than approximating the shape of the vertebral body of L1 by the shape of T12 or L2. This approach could be used in the future to improve the pre-planning of spine surgeries to treat vertebral fractures

An Evaluation of passive recumbent quantitative fluoroscopy to measure mid-lumber intervertebral motion in patients with chronic non-specific low back pain and healthy volunteers.

Introduction: The biomechanical model of back pain has failed to find distinct relationships between intervertebral movement and pain due to limitations and variation in methods, and errors in measurement. Quantitative fluoroscopy (QF) reduces variation and error and measures dynamic intervertebral motion in vivo. This thesis used recumbent QF to examine continuous mid-lumbar intervertebral motion (L2 to L5) in patients with assumed mechanical chronic non-specific low back pain (CNSLBP) that had been clinically diagnosed. It aimed to develop kinematic parameters from the continuous data and determine whether these could detect subtle mechanical differences by comparing this to data obtained from healthy volunteers. Methods: This was a prospective cross sectional study. Forty patients with CNSLBP (age 21 to 51 years), and 40 healthy volunteers matched for gender, age and body mass index underwent passive recumbent QF in the coronal and sagittal planes. The patient group completed questionnaires for pain and disability. Four kinematic parameters were developed and compared for differences and diagnostic accuracy. Reference intervals were developed for three of the parameters and reproducibility of two were assessed. The radiation dose was compared to lumbar spine radiographs and diagnostic reference levels were established. Finally, relationships between patient’s pain and disability and one of the kinematic parameters (continuous proportional motion CPM) were explored. Results: Reproducibility was high. There were some differences in the coronal plane and flexion for each kinematic parameter, but no consistency across segments and none had high diagnostic accuracy. Radiation dose for QF is of the same magnitude as radiographs, and there were no associations between patient characteristics of pain and disability and CPM. Conclusion: Although the kinematic differences were weak, they indicate that biomechanics may be partly responsible for clinically diagnosed mechanical CNSLBP, but this is not detectable by any one kinematic parameter. It is likely that other factors such as loading, central sensitisation and motor control may also be responsible for back pain that is considered mechanical. QF is easily adapted to clinical practice and is recommended to replace functional radiography, but further work is needed to determine which kinematic parameters are clinically useful