3D reconstruction of ribcage geometry from biplanar radiographs using a statistical parametric model approach

Rib cage 3D reconstruction is an important prerequisite for thoracic spine modelling, particularly for studies of the deformed thorax in adolescent idiopathic scoliosis. This study proposes a new method for rib cage 3D reconstruction from biplanar radiographs, using a statistical parametric model approach. Simplified parametric models were defined at the hierarchical levels of rib cage surface, rib midline and rib surface, and applied on a database of 86 trunks. The resulting parameter database served to statistical models learning which were used to quickly provide a first estimate of the reconstruction from identifications on both radiographs. This solution was then refined by manual adjustments in order to improve the matching between model and image. Accuracy was assessed by comparison with 29 rib cages from CT scans in terms of geometrical parameter differences and in terms of line-to-line error distance between the rib midlines. Intra and inter-observer reproducibility were determined regarding 20 scoliotic patients. The first estimate (mean reconstruction time of 2’30) was sufficient to extract the main rib cage global parameters with a 95% confidence interval lower than 7%, 8%, 2% and 4° for rib cage volume, antero-posterior and lateral maximal diameters and maximal rib hump, respectively. The mean error distance was 5.4 mm (max 35mm) down to 3.6 mm (max 24 mm) after the manual adjustment step (+3’30). The proposed method will improve developments of rib cage finite element modeling and evaluation of clinical outcomes.This work was funded by Paris Tech BiomecAM chair on subject specific muscular skeletal modeling, and we express our acknowledgments to the chair founders: Cotrel foundation, Société générale, Protéor Company and COVEA consortium. We extend your acknowledgements to Alina Badina for medical imaging data, Alexandre Journé for his advices, and Thomas Joubert for his technical support

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

Application of particle filter for vertebral body extraction: a simulation study

Lumbar vertebra motion analysis provides objective measurement of lumbar disorder. The automatic tracking algorithm has been applied to Digitalized Video Fluoroscopy (DVF) sequence. This paper proposes a new Auto-Tracking System (ATS) with a guide device and a motion analysis to automatically measure human lumbar motion. Digitalized Video Fluoroscopy (DVF) sequence was obtained during flexion-extension lumbar movement under guide device. An extraction of human vertebral body and its motion tracking were developed by particle filter. The results showed a good repeatability, reliability and robustness. In model test, the maximum fiducial error is 3.7% and the repeatability error is 1.2% in translation and the maximal repeatability error is 2.6% in rotation angle. In this simulation study, we employed a lumbar model to simulate the motion of lumber flexion- extension with the stepping translation of 1.3 mm and rotation angle of 1?. Results showed that the fiducial error was measured as 1.0%, while the repeatability error was 0.7%. The sequence can be detected even noise contamination as more as 0.5 of the density. The result demonstrates that the data from the auto-tracking algorithm shows a strong correlation with the actual measurement and that the Vertebral Auto-Tracking System (VATS) is highly repetitive. In the human lumbar spine evaluation, the study not only shows the reliability of Auto-Tracking Analysis System (ATAS), but also reveals that it is robust and variable in vivo. The VATS is evaluated by the model, the simulated sequence and the human subject. It could be concluded that the developed system could provide a reliable and robust system to detect spinal motion in future medical application.published_or_final_versio

Accuracy and repeatability of quantitative fluoroscopy for the measurement of sagittal plane translation and finite centre of rotation in the lumbar spine

Quantitative fluoroscopy (QF) was developed to measure intervertebral mechanics in vivo and has been found to have high repeatability and accuracy for the measurement of intervertebral rotations. However, sagittal plane translation and finite centre of rotation (FCR) are potential measures of stability but have not yet been fully validated for current QF. This study investigated the repeatability and accuracy of QF for measuring these variables. Repeatability was assessed from L2-S1 in 20 human volunteers. Accuracy was investigated using 10 consecutive measurements from each of two pairs of linked and instrumented dry human vertebrae as reference; one which tilted without translation and one which translated without tilt. The results found intra- and inter-observer repeatability for translation to be 1.1mm or less (SEM) with fair to substantial reliability (ICC 0.533-0.998). Intra-observer repeatability of FCR location for inter-vertebral rotations of 5o and above ranged from 1.5mm to 1.8mm (SEM) with moderate to substantial reliability (ICC 0.626-0.988). Inter-observer repeatability for FCR ranged from 1.2mm to 5.7mm, also with moderate to substantial reliability (ICC 0.621-0.878). Reliability was substantial (ICC>0.81) for 10/16 measures for translation and 5/8 for FCR location. Accuracy for translation was 0.1mm (fixed centre) and 2.2mm (moveable centre), with an FCR error of 0.3mm(x) and 0.4mm(y) (fixed centre). This technology was found to have a high level of accuracy and with a few exceptions, moderate to substantial repeatability for the measurement of translation and FCR from fluoroscopic motion sequences

Deformable Multisurface Segmentation of the Spine for Orthopedic Surgery Planning and Simulation

Purpose: We describe a shape-aware multisurface simplex deformable model for the segmentation of healthy as well as pathological lumbar spine in medical image data. Approach: This model provides an accurate and robust segmentation scheme for the identification of intervertebral disc pathologies to enable the minimally supervised planning and patient-specific simulation of spine surgery, in a manner that combines multisurface and shape statistics-based variants of the deformable simplex model. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user assistance is allowed to disable the prior shape influence during deformation. Results: Results demonstrate validation against user-assisted expert segmentation, showing excellent boundary agreement and prevention of spatial overlap between neighboring surfaces. This section also plots the characteristics of the statistical shape model, such as compactness, generalizability and specificity, as a function of the number of modes used to represent the family of shapes. Final results demonstrate a proof-of-concept deformation application based on the open-source surgery simulation Simulation Open Framework Architecture toolkit. Conclusions: To summarize, we present a deformable multisurface model that embeds a shape statistics force, with applications to surgery planning and simulation

An objective spinal motion imaging assessment (OSMIA): reliability, accuracy and exposure data

BACKGROUND: Minimally-invasive measurement of continuous inter-vertebral motion in clinical settings is difficult to achieve. This paper describes the reliability, validity and radiation exposure levels in a new Objective Spinal Motion Imaging Assessment system (OSMIA) based on low-dose fluoroscopy and image processing. METHODS: Fluoroscopic sequences in coronal and sagittal planes were obtained from 2 calibration models using dry lumbar vertebrae, plus the lumbar spines of 30 asymptomatic volunteers. Calibration model 1 (mobile) was screened upright, in 7 inter-vertebral positions. The volunteers and calibration model 2 (fixed) were screened on a motorised table comprising 2 horizontal sections, one of which moved through 80 degrees. Model 2 was screened during motion 5 times and the L2-S1 levels of the volunteers twice. Images were digitised at 5fps. Inter-vertebral motion from model 1 was compared to its pre-settings to investigate accuracy. For volunteers and model 2, the first digitised image in each sequence was marked with templates. Vertebrae were tracked throughout the motion using automated frame-to-frame registration. For each frame, vertebral angles were subtracted giving inter-vertebral motion graphs. Volunteer data were acquired twice on the same day and analysed by two blinded observers. The root-mean-square (RMS) differences between paired data were used as the measure of reliability. RESULTS: RMS difference between reference and computed inter-vertebral angles in model 1 was 0.32 degrees for side-bending and 0.52 degrees for flexion-extension. For model 2, X-ray positioning contributed more to the variance of range measurement than did automated registration. For volunteer image sequences, RMS inter-observer variation in intervertebral motion range in the coronal plane was 1.86 degreesand intra-subject biological variation was between 2.75 degrees and 2.91 degrees. RMS inter-observer variation in the sagittal plane was 1.94 degrees. Radiation dosages in each view were below the levels recommended for a plain film. CONCLUSION: OSMIA can measure inter-vertebral angular motion patterns in routine clinical settings if modern image intensifier systems are used. It requires skilful radiography to achieve optimal positioning and dose limitation. Reliability in individual subjects can be judged from the variance of their averaged inter-vertebral angles and by observing automated image registration

Generalizable Methods for Modeling Lumbar Spine Kinematics

A more complete understanding of lumbar spine kinematics could improve diagnoses and treatment of low back pathologies and may advance the development of biomechanical models. Kinematics describes motion of the five lumbar vertebrae without consideration for the forces that cause the motion. Despite considerable attention from researchers and clinicians, lumbar spine kinematics are not fully understood because the anatomy is not accessible for direct observation and the complex governing biomechanics produce small magnitude, coupled intervertebral movements. The overall goal of this project was to develop a descriptive model of intervertebral lumbar spine kinematics that is applicable to a generalizable subject population with diverse anthropometry. To accomplish this, a method was developed for measuring three-dimensional vertebral configuration using positional magnetic resonance imaging (MRI). The method makes use of automated vertebral registration to address time limitations in current data processing techniques and improves the ability to power experimental investigations. Finally, a geometric model of lumbar vertebral kinematics was developed using principal component regression applied to in vivo vertebral measurement data across the range of flexion and extension joint motion. This principal component-based approach offers unique advantages for predicting and interpreting performance of complex systems such as lumbar joint biomechanics because no assumptions are made regarding the governing mechanisms. This provides an opportunity to infer mechanistic characteristics about intervertebral joint kinematics and to use in vivo data to validate musculoskeletal models