Model-based registration for assessment of spinal deformities in idiopathic scoliosis

Accuracy of 3D printed spine models for pre-surgical planning of complex adolescent idiopathic scoliosis (AIS) in spinal surgeries: a case series

Adolescent idiopathic scoliosis (AIS) is a noticeable spinal deformity in both adult and adolescent population. In majority of the cases, the gold standard of treatment is surgical intervention. Technological advancements in medical imaging and 3D printing have revolutionised the surgical planning and intraoperative decision making for surgeons in spinal surgery. However, its applicability for planning complex spinal surgeries is poorly documented with human subjects. The objective of this study is to evaluate the accuracy of 3D printed models for complex spinal deformities based on Cobb angles between 40° to 95°.This is a retrospective cohort study where, five CT scans of the patients with AIS were segmented and 3D printed for evaluating the accuracy. Consideration was given to the Inter-patient and acquisition apparatus variability of the CT-scan dataset to understand the effect on trueness and accuracy of the developed CAD models. The developed anatomical models were re-scanned for analysing quantitative surface deviation to assess the accuracy of 3D printed spinal models. Results show that the average of the root mean square error (RMSE) between the 3DP models and virtual models developed using CT scan of mean surface deviations for the five 3d printed models was found to be 0.5±0.07 mm. Based on the RMSE, it can be concluded that 3D printing based workflow is accurate enough to be used for presurgical planning for complex adolescent spinal deformities. Image acquisition and post processing parameters, type of 3D printing technology plays key role in acquiring required accuracy for surgical applications.

Biomechanical Morphing for Personalized Fitting of Scoliotic Torso Skeleton Models

The use of patient-specific biomechanical models offers many opportunities in the treatment of adolescent idiopathic scoliosis, such as the design of personalized braces. The first step in the development of these patient-specific models is to fit the geometry of the torso skeleton to the patient’s anatomy. However, existing methods rely on high-quality imaging data. The exposure to radiation of these methods limits their applicability for regular monitoring of patients. We present a method to fit personalized models of the torso skeleton that takes as input biplanar low-dose radiographs. The method morphs a template to fit annotated points on visible portions of the spine, and it relies on a default biomechanical model of the torso for regularization and robust fitting of hardly visible parts of the torso skeleton, such as the rib cage. The proposed method provides an accurate and robust solution to obtain personalized models of the torso skeleton, which can be adopted as part of regular management of scoliosis patients. We have evaluated the method on ten young patients who participated in our study. We have analyzed and compared clinical metrics on the spine and the full torso skeleton, and we have found that the accuracy of the method is at least comparable to other methods that require more demanding imaging methods, while it offers superior robustness to artifacts such as interpenetration of ribs. Normal-dose X-rays were available for one of the patients, and for the other nine we acquired low-dose X-rays, allowing us to validate that the accuracy of the method persisted under less invasive imaging modalities.

Model-Based Radiation-Free Assessment of Scoliosis: A Principle Validation Study

Purpose

Adolescent scoliosis is one of the common pediatric spinal diseases which has a high risk of progression due to the rapid growth of the skeleton during the growing stage therefore needs regular clinical monitoring including X-rays. Because X-rays could lead to ionizing radiation-related health problems, an ionizing radiation-free, non-invasive method is presented here to estimate the degree of scoliosis and to potentially support the medical assessment.

Methods

The radiation-free body scanner provides a 3D surface scan of the torso. A basic 3D structure of the human ribcage and vertebral column was modeled and simulated with computer-aided design software and finite element method calculation. For comparison with X-rays, courses of vertebral columns derived from 3D torso images and 3D models were analyzed with respect to their apex positions and angles.

Results

The methods show good results in the estimation of the apex positions of scoliosis. Strong correlations (R = 0.8924) were found between the apex and Cobb angle from X-rays. Similar correlations (R = 0.8087) was obtained between the apex angles extracted from X-rays and the combination of torso scan images with 3D model simulations. Promising agreement was obtained between the spinal trajectories extracted from X-ray and 3D torso images.

Conclusions

Very strong correlations suggest that the apex angle could potentially be used for scoliosis assessment in follow-up examinations in complement to the Cobb angle. However, further improvements of the methods and tests on a larger number of data set are necessary before their introduction into the clinical application.

Automated Measurement of Lumbar Lordosis on Radiographs Using Machine Learning and Computer Vision

STUDY DESIGN

Cross sectional database study.

OBJECTIVE

To develop a fully automated artificial intelligence and computer vision pipeline for assisted evaluation of lumbar lordosis.

METHODS

Lateral lumbar radiographs were used to develop a segmentation neural network (n = 629). After synthetic augmentation, 70% of these radiographs were used for network training, while the remaining 30% were used for hyperparameter optimization. A computer vision algorithm was deployed on the segmented radiographs to calculate lumbar lordosis angles. A test set of radiographs was used to evaluate the validity of the entire pipeline (n = 151).

RESULTS

The U-Net segmentation achieved a test dataset dice score of 0.821, an area under the receiver operating curve of 0.914, and an accuracy of 0.862. The computer vision algorithm identified the L1 and S1 vertebrae on 84.1% of the test set with an average speed of 0.14 seconds/radiograph. From the 151 test set radiographs, 50 were randomly chosen for surgeon measurement. When compared with those measurements, our algorithm achieved a mean absolute error of 8.055° and a median absolute error of 6.965° (not statistically significant, P > .05).

CONCLUSION

This study is the first to use artificial intelligence and computer vision in a combined pipeline to rapidly measure a sagittal spinopelvic parameter without prior manual surgeon input. The pipeline measures angles with no statistically significant differences from manual measurements by surgeons. This pipeline offers clinical utility in an assistive capacity, and future work should focus on improving segmentation network performance.

Intelligent Measurement of Spinal Curvature Using Cascade Gentle AdaBoost Classifier and Region-Based DRLSE

For spinal curvature measurements, because of the anatomical complexity of the spine CT image, developing an automated method to avoid manual landmark is a challenging task. In this study, we propose an intelligent framework that integrates the cascade AdaBoost classifier and region-based distance regularized level set evolution (DRLSE) with the vertebral centroid measurement. First, the histogram-of-oriented-gradients based cascade gentle AdaBoost classifier is used to detect automatically and localize vertebral bodies from computer tomography (CT) spinal images. Considering these vertebral pathological images enables us to produce a diverse training dataset. Then, the DRLSE method introduces the local region information to converge the vertebral boundary quickly. The located bounding box is regarded as an accurate initial contour. This avoids the negative impact of manual initialization. Finally, we perform vertebral centroid extraction and spinal curve fitting. The spinal curvature angle is determined by calculating the angle between two tangents to the curve. We verified the effectiveness of the proposed method on 10 spine CT volumes. Quantitative comparison against the ground-truth centroids yielded a detection accuracy rate of 98.3% and a mean centroid location error of 1.15 mm. The comparative results with existing methods demonstrate that the proposed method can accurately detect and segment vertebral bodies. Furthermore, the spinal curvature can be automatically measured without manual landmark.

Vertebral Axial Asymmetry in Adolescent Idiopathic Scoliosis

STUDY DESIGN

Retrospective study.

OBJECTIVES

To investigate parameters of axial vertebral deformation in patients with scoliosis compared to a control group, and to determine whether these parameters correlated with the severity of spine curvature, measured as the Cobb angle.

SUMMARY OF BACKGROUND DATA

Adolescent idiopathic scoliosis (AIS) is the most common type of spinal deformity. Many studies have investigated vertebral deformation, in terms of wedging and pedicle deformations, but few studies have investigated actual structural changes within vertebrae.

METHODS

This study included 20 patients with AIS (Lenke 1-3, mean age: 15.6 years, range: 11-20). We compared preoperative low-dose computed tomography (CT) examinations of patients with AIS to those of a control group matched for age and sex. The control individuals had no spinal deformity, but they were admitted to the emergency department for trauma CTs. We measured the Cobb angles and the axial vertebral rotation (AVR), axial vertebral body asymmetry (AVBA), and frontal vertebral body rotation (FVBR) for the superior end, inferior end, and apical vertebrae, with in-house-developed software. Correlations between entities were investigated with the Pearson correlation test.

RESULTS

The average Cobb angles were 49.3° and 1.3° for the scoliotic and control groups, respectively. The patient and control groups showed significant differences in the AVRs of all three vertebra levels (p < .01), the AVBAs of the superior end and apical vertebrae (p < .008), and the FVBR of the apical vertebra (p = .011). Correlations were only found between the AVBA and FVBR in the superior end vertebra (r = 0.728, p < .001) and in the apical vertebra (r = 0.713, p < .001).

CONCLUSIONS

Compared with controls, patients with scoliosis showed clear morphologic differences in the midaxial plane vertebrae. Differences in AVR, AVBA, and FVBR were most pronounced at the apical vertebra. The FVBR provided valuable additional information about the internal rotation and deformation of vertebrae.

LEVEL OF EVIDENCE

Level III.

Improving visualization of 4D flow cardiovascular magnetic resonance with four-dimensional angiographic data: generation of a 4D phase-contrast magnetic resonance CardioAngiography (4D PC-MRCA)

Magnetic Resonance Angiography (MRA) and Phase-Contrast MRA (PC-MRA) approaches used for assessment of cardiovascular morphology typically result in data containing information from the entire cardiac cycle combined into one 2D or 3D image. Information specific to each timeframe of the cardiac cycle is, however, lost in this process. This study proposes a novel technique, called Phase-Contrast Magnetic Resonance CardioAngiography (4D PC-MRCA), that utilizes the full potential of 4D Flow CMR when generating temporally resolved PC-MRA data to improve visualization of the heart and major vessels throughout the cardiac cycle. Using non-rigid registration between the timeframes of the 4D Flow CMR acquisition, the technique concentrates information from the entire cardiac cycle into an angiographic dataset at one specific timeframe, taking movement over the cardiac cycle into account. Registration between the timeframes is used once more to generate a time-resolved angiography. The method was evaluated in ten healthy volunteers. Visual comparison of the 4D PC-MRCAs versus PC-MRAs generated from 4D Flow CMR using the traditional approach was performed by two observers using Maximum Intensity Projections (MIPs). The 4D PC-MRCAs resulted in better visibility of the main anatomical regions of the cardiovascular system, especially where cardiac or vessel motion was present. The proposed method represents an improvement over previous PC-MRA generation techniques that rely on 4D Flow CMR, as it effectively utilizes all the information available in the acquisition. The 4D PC-MRCA can be used to visualize the motion of the heart and major vessels throughout the entire cardiac cycle.

Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression. Preliminary results

Spine is a structure commonly involved in several prevalent diseases. In clinical diagnosis, therapy, and surgical intervention, the identification and segmentation of the vertebral bodies are crucial steps. However, automatic and detailed segmentation of vertebrae is a challenging task, especially due to the proximity of the vertebrae to the corresponding ribs and other structures such as blood vessels. In this study, to overcome these problems, a probabilistic atlas of the spine, including cervical, thoracic and lumbar vertebrae has been built to introduce anatomical knowledge in the segmentation process, aiming to deal with overlapping gray levels and the proximity to other structures. From a set of 3D images manually segmented by a physician (training data), a 3D volume indicating the probability of each voxel of belonging to the spine has been developed, being necessary the generation of a probability map and its deformation to adapt to each patient. To validate the improvement of the segmentation using the atlas developed in the testing data, we computed the Hausdorff distance between the manually-segmented ground truth and an automatic segmentation and also between the ground truth and the automatic segmentation refined with the atlas. The results are promising, obtaining a higher improvement especially in the thoracic region, where the ribs can be found and appropriately eliminated.