Augmenting the Cobb angle: Three-dimensional analysis of whole spine shapes using Bézier curves

Real-Time 3D Instrument Tip Tracking Using 2D X-Ray Fluoroscopy With Vessel Deformation Correction Under Free Breathing

OBJECTIVE

Accurate localization of the instrument tip within the hepatic vein is crucial for the success of transjugular intrahepatic portosystemic shunt (TIPS) procedures. Real-time tracking of the instrument tip in X-ray images is greatly influenced by vessel deformation due to patient's pose variation, respiratory motion, and puncture manipulation, frequently resulting in failed punctures.

METHOD

We propose a novel framework called deformable instrument tip tracking (DITT) to obtain the real-time tip positioning within the 3D deformable vasculature. First, we introduce a pose alignment module to improve the rigid matching between the preoperative vessel centerline and the intraoperative instrument centerline, in which the accurate matching of 3D/2D centerline features is implemented with an adaptive point sampling strategy. Second, a respiration compensation module using monoplane X-ray image sequences is constructed and provides the motion prior to predict intraoperative liver movement. Third, a deformation correction module is proposed to rectify the vessel deformation during procedures, in which a manifold regularization and the maximum likelihood-based acceleration are introduced to obtain the accurate and fast deformation learning.

RESULTS

Experimental results on simulated and clinical datasets show an average tracking error of 1.59 0.57 mm and 1.67 0.54 mm, respectively.

CONCLUSION

Our framework can track the tip in 3D vessel and dynamically overlap the branch roadmapping onto X-ray images to provide real-time guidance.

SIGNIFICANCE

Accurate and fast (43ms per frame) tip tracking with the proposed framework possesses a good potential for improving the outcomes of TIPS treatment and minimizes the usage of contrast agent.

Biomechanical design optimization and experimental verification of Bezier curve based two-sectional cervical pillow with variable-density cellular structure

The fundamental function of an optimal cervical pillow is to provide sufficient support to maintain normal spinal alignment and minimize biological stress on the contact surface throughout sleep. The recent advancements in cervical pillows have mainly focused on the subjective and objective evaluations of support comfort, as well as the relationship between pillow height and cervical vertebrae posture. However, only a few studies have addressed shape design guidelines and mechanical performances of the pillows themselves. In this study, a two-sectional contour cervical pillow comprising an arc and a Bezier curve is designed to support the head and neck. The design of the arc-shaped neck section incorporates the Cobb's angle and Borden value from healthy individuals to reflect the consistency of normal cervical anatomical features. The Bezier curve-based head section takes the head length and neck depth into account as significant individual differences. Static analysis and lattice optimization are performed in ANSYS Workbench to develop a variable-density cellular structure, aimed at improving air permeability and reducing the risk of pressure ulcers associated with the cervical pillow. The rapid prototyping technique fused deposition modeling (FDM) and thermoplastic material polylactic acid (PLA) are employed for fabricating different cellular structures. The results demonstrate that the neck section experiences less stress and greater deformation in comparison to the head section, indicating good comfort and support provided by the designed cervical pillow. Additionally, the compressive, bending, and cushion properties of the 3D-printed cervical pillow with variable-density cellular structure are experimentally validated, further confirming its effectiveness.

Application of machine learning standardized integral area algorithm in measuring the scoliosis

This study was to develop a computer vision evaluation method to automatically measure the degree of scoliosis based on the machine learning algorithm. For the X-ray images of 204 patients with idiopathic scoliosis who underwent full-spine radiography, histogram equalization of original image was performed before a flipping method was used to magnify asymmetric elements, search for the global maximum pixel value in each line, and scan local maximal pixel value, with the intersection set of two point sets being regarded as candidate anchor points. All fine anchors were fitted with cubic spline algorithm to obtain the approximate curve of the spine, and the degree of scoliosis was measured by the standardized integral area. All measured data were analyzed. In manual measurement, the Cobb angle was 11.70-25.00 (20.15 ± 3.60), 25.20-44.70 (33.89 ± 5.41), and 45.10-49.40 (46.98 ± 1.25) in the mild, moderate and severe scoliosis group, respectively, whereas the value for the standardized integral area algorithm was 0.072-0.298 (0.185 ± 0.040), 0.100-0.399 (0.245 ± 0.050), and 0.246-0.901 (0.349 ± 0.181) in the mild, moderate and severe scoliosis group, respectively. Correlation analysis between the manual measurement of the Cobb angle and the evaluation of the standardized integral area algorithm demonstrated the Spearman correlation coefficient r = 0.643 (P < 0.001). There was a positive correlation between the manual measurement of the Cobb angle and the measurement of the standardized integral area value. Two methods had good consistency in evaluating the degree of scoliosis. ROC curve analysis of the standardized integral area algorithm to measure the degree of scoliosis showed he cutoff value of the standardized integral area algorithm was 0.20 for the moderate scoliosis with an AUC of 0.865, sensitivity 0.907, specificity 0.635, accuracy 0.779, positive prediction value 0.737 and negative prediction value 0.859, and the cutoff value of the standardized integral area algorithm was 0.40 for the severe scoliosis with an AUC of 0.873, sensitivity 0.188, specificity 1.00, accuracy 0.936, positive prediction value 1 and a negative prediction value 0.935. Using the standardized integral area as an independent variable and the Cobb angle as a dependent variable, a linear regression equation was established as Cobb angle = 13.36 + 70.54 × Standardized area, the model has statistical significance. In conclusion, the integrated area algorithm method of machine learning can quickly and efficiently assess the degree of scoliosis and is suitable for screening the degree of scoliosis in a large dataset as a useful supplement to the fine measurement of scoliosis Cobb angle.

Multibody Models of the Thoracolumbar Spine: A Review on Applications, Limitations, and Challenges

Numerical models of the musculoskeletal system as investigative tools are an integral part of biomechanical and clinical research. While finite element modeling is primarily suitable for the examination of deformation states and internal stresses in flexible bodies, multibody modeling is based on the assumption of rigid bodies, that are connected via joints and flexible elements. This simplification allows the consideration of biomechanical systems from a holistic perspective and thus takes into account multiple influencing factors of mechanical loads. Being the source of major health issues worldwide, the human spine is subject to a variety of studies using these models to investigate and understand healthy and pathological biomechanics of the upper body. In this review, we summarize the current state-of-the-art literature on multibody models of the thoracolumbar spine and identify limitations and challenges related to current modeling approaches.