In clinical routine, lower limb analysis relies on conventional X-ray (2D view) or computerised tomography (CT) Scan (lying position). However, these methods do not allow 3D analysis in standing position. The aim of this study is to propose a fast and accurate 3D-reconstruction-method based on parametric models and statistical inferences from biplanar X-rays with clinical measurements' (CM) assessment in standing position for a clinical routine use. For the reproducibility study, the 95% CI was under 2.7° for all lower limbs' angular measurements except for tibial torsion, femoral torsion and tibiofemoral rotation ( < 5°). The 95% CI were under 2.5 mm for lower limbs' lengths and 1.5 to 3° for the pelvis' CM. Comparisons between X-rays and CT-scan based 3D shapes in vitro showed mean differences of 1.0 mm (95% CI = 2.4 mm). Comparisons of 2D lower limbs' and 3D pelvis' CM between standing ‘Shifted-Feet’ and ‘Non-Shifted-Feet’ position showed means differences of 0.0 to 1.4°. Significant differences were f...

Fast 3D reconstruction of the lower limb using a parametric model and statistical inferences and clinical measurements calculation from biplanar X-rays

Purpose
In 1992, Georges Charpak invented a new type of X-ray detector, which in turn led to the development of the EOS® 2D/3D imaging system. This system takes simultaneous anteroposterior and lateral 2D images of the whole body and can be utilized to perform 3D reconstruction based on statistical models. The purpose of this review is to present the state of the art for this EOS® imaging technique, to report recent developments and advances in the technique, and to stress its benefits while also noting its limitations.

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EOS® biplanar X-ray imaging: concept, developments, benefits, and limitations

One of the major challenges in anatomical landmark detection, based on deep neural networks, is the limited availability of medical imaging data for network learning. To address this problem, we present a two-stage task-oriented deep learning method to detect large-scale anatomical landmarks simultaneously in real time , using limited training data . Specifically, our method consists of two deep convolutional neural networks (CNN), with each focusing on one specific task. Specifically, to alleviate the problem of limited training data, in the first stage, we propose a CNN based regression model using millions of image patches as input, aiming to learn inherent associations between local image patches and target anatomical landmarks. To further model the correlations among image patches, in the second stage, we develop another CNN model, which includes a) a fully convolutional network that shares the same architecture and network weights as the CNN used in the first stage and also b) several extra layers to jointly predict coordinates of multiple anatomical landmarks. Importantly, our method can jointly detect large-scale ( e.g. , thousands of) landmarks in real time. We have conducted various experiments for detecting 1200 brain landmarks from the 3D T1-weighted magnetic resonance images of 700 subjects, and also 7 prostate landmarks from the 3D computed tomography images of 73 subjects. The experimental results show the effectiveness of our method regarding both accuracy and efficiency in the anatomical landmark detection.

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Detecting Anatomical Landmarks From Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks

In a telemedicine diagnosis system, the emergence of 3D imaging enables doctors to make clearer judgments, and its accuracy also directly affects doctors’ diagnosis of the disease. In order to ensure the safe transmission and storage of medical data, a 3D medical watermarking algorithm based on wavelet transform is proposed in this paper. The proposed algorithm employs the principal component analysis (PCA) transform to reduce the data dimension, which can minimize the error between the extracted components and the original data in the mean square sense. Especially, this algorithm helps to create a bacterial foraging model based on particle swarm optimization (BF-PSO), by which the optimal wavelet coefficient is found for embedding and is used as the absolute feature of watermark embedding, thereby achieving the optimal balance between embedding capacity and imperceptibility. A series of experimental results from MATLAB software based on the standard MRI brain volume dataset demonstrate that the proposed algorithm has strong robustness and make the 3D model have small deformation after embedding the watermark. 

https://file.techscience.com/ueditor/files/csse/TSP_CSSE-41-3/TSP_CSSE_22305/TSP_CSSE_22305.pdf

A Robust 3-D Medical Watermarking Based on Wavelet Transform for Data Protection

Study design Retrospective review, full-body radiographical analysis of adult patients with sagittal spinal malalignment (SSM). Objective To investigate the compensatory mechanisms involved in the sagittal plane of the body after progressive spinal sagittal malalignment and to study the impact of age on compensatory mechanism recruitment. Summary of background data Patients with SSM recruit compensatory mechanisms to maintain erect posture and horizontal gaze. Mechanisms such as pelvic retroversion, knee flexion, and pelvic shift have been proposed, but how they contribute and how age affects their recruitment are poorly understood. Methods Retrospective review of adult patients with SSM who underwent full-standing axis stereoradiography (EOS imaging). Radiographical measurements were performed with Surgimap. Patients were categorized on the basis of the mismatch between pelvic incidence (PI) and lumbar lordosis (PI-LL). Compensatory mechanisms were normalized to each patient's PI-LL and compared by mismatch groups. In addition, patients were subcategorized into 2 age groups (≥65 and Results A total of 161 patients with a mean age of 62.93 ± 12.8 years. Mean sagittal vertical axis = 62.3 ± 61.5 mm; pelvic tilt (PT) = 29.2° ± 8.4°; and PI-LL = 21.0° ± 14.9°. Mismatch groups were as follows: group 1: PI-LL 0°-10°; group 2: 10°-20°; group 3: 20°-30°; and group 4: >30°. There were significant differences between all groups with regard to thoracic kyphosis (TK), PT, knee flexion angle, and pelvic shift by analysis of variance (P Conclusion Spinopelvic mismatch is an important driver in SSM. Pelvic retroversion and flattening of TK (reduction) become exhausted with increasing mismatch, at which point there seems to be a steady transfer of compensation toward significant participation of the lower limbs. Further analysis suggests differential recruitment of these compensatory mechanisms based upon age. Level of evidence 3.

Recruitment of Compensatory Mechanisms in Sagittal Spinal Malalignment Is Age and Regional Deformity Dependent A Full-Standing Axis Analysis of Key Radiographical Parameters

3D reconstruction of the spine from biplanar X-rays using parametric models based on transversal and longitudinal inferences.

Three-dimensional reconstruction of the lower limb from biplanar calibrated radiographs.

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 train statistical models 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 was determined for 20 scoliotic p...

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

To date, 3D spine reconstruction from biplanar radiographs involves intensive user supervision and semi-automated methods that are time-consuming and not effective in clinical routine. This paper proposes a new, fast, and automated 3D spine reconstruction method through which a realistic statistical shape model of the spine is fitted to images using convolutional neural networks (CNN). The CNNs automatically detect the anatomical landmarks controlling the spine model deformation through a hierarchical and gradual iterative process. The performance assessment used a set of 68 biplanar radiographs, composed of both asymptomatic subjects and adolescent idiopathic scoliosis patients, in order to compare automated reconstructions with ground truths build using multiple experts-supervised reconstructions. The mean (SD) errors of landmark locations (3D Euclidean distances) were 1.6 (1.3) mm, 1.8 (1.3) mm, and 2.3 (1.4) mm for the vertebral body center, endplate centers, and pedicle centers, respectively. The clinical parameters extracted from the automated 3D reconstruction (reconstruction time is less than one minute) presented an absolute mean error between 2.8° and 4.7° for the main spinal parameters and between 1° and 2.1° for pelvic parameters. Automated and expert’s agreement analysis reported that, on average, 89% of automated measurements were inside the expert’s confidence intervals. The proposed automated 3D spine reconstruction method provides an important step that should help the dissemination and adoption of 3D measurements in clinical routine.

B. Aubert

Papers

3D reconstruction of the spine from biplanar X-rays using parametric models based on transversal and longitudinal inferences.

Fast 3D reconstruction of the lower limb using a parametric model and statistical inferences and clinical measurements calculation from biplanar X-rays

Three-dimensional reconstruction of the lower limb from biplanar calibrated radiographs.

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

Toward Automated 3D Spine Reconstruction from Biplanar Radiographs Using CNN for Statistical Spine Model Fitting