Development, calibration and validation of a comprehensive customizable lumbar spine FE model for simulating fusion constructs.

Instrumentation alters the biomechanics of the spine, and therefore prediction of all output quantities that have critical influence post-surgically is significant for engineering models to aid in clinical predictions. Geometrical morphological finite element models can bring down the development time and cost of custom intact and instrumented models and thus aids in the better inference of biomechanics of surgical instrumentation on patient-specific diseased spine segments. A comprehensive hexahedral morphological lumbosacral finite element model is developed in this work to predict the range of motions, disc pressures, and facet contact forces of the intact and instrumented spine. Facet contact forces are needed to predict the impact of fusion surgeries on adjacent facet contacts in bending, axial rotation, and extension motions. Extensive validation in major physiological loading regimes of the pure moment, pure compression, and combined loading is undertaken. In vitro, experimental corridor results from six different studies reported in the literature are compared and the generated model had statistically significant comparable values with these studies. Flexion, extension and bending moment rotation curves of all segments of the developed model were favourable and within two separately established experimental corridor windows as well as recent simulation results. Axial torque moment rotation curves were comparable to in vitro results for four out of five lumbar functional units. The facet contact force results also agreed with in vitro experimental results. The current model is also computationally efficient with respect to contemporary models since it uses significantly smaller number of elements without losing the accuracy in terms of response prediction. This model can further be used for predicting the impact of different instrumentation techniques on the lumbar vertebral column.

Classification of certain vertebral degenerations using MRI image features.

BACKGROUND AND OBJECTIVE: This article describes a fully automatic system for classifying various spinal degenerative phenotypes namely Modic changes, endplate defects and focal changes which are associated with lower back pain. These are obtained from T1/T2 Magnetic Resonance Imaging (MRI) scans. Lower back pain is a predominantly occurring ailment, which is prone to have various roots including the anatomical and pathophysciological aspects. Clinicians and radiologist use MRI to assess and evaluate the extent of damage, cause, and to decide on the future course of treatment. In large healthcare systems, to circumvent the manual reading of various image slices, we describe a system to automate the classification of various vertebral degeneracies that cause lower back pain. METHODS: We implement a combination of feature extraction, image analysis based on geometry and classification using machine learning techniques for identifying vertebral degeneracies. Image features like local binary pattern, Hu's moments and gray level co-occurrence matrix (GLCM) based features are extracted to identify Modic changes, endplate defects, and presence of any focal changes. A combination of feature set is used for describing the extent of Modic change on the end plate. Feature sensitivity studies towards efficient classification is presented. A STIR based acute/chronic classification is also attempted in the current work. RESULTS: The implemented method is tested and validated over a dataset containing 100 patients. The proposed framework for detecting the extent of Modic change achieves an accuracy of 85.91%. From the feature sensitivity analysis, it is revealed that entropy based measure obtained from gray level co-occurrence matrix alone is sufficient for detection of focal changes. The classification performance for detecting endplate defect is highly sensitive to the first 2 Hu's moments. CONCLUSION: A novel approach to identify the allied vertebral degenerations and extent of Modic changes in vertebrae by exploiting image features and classification through machine learning is proposed. This shall assist radiologists in detecting abnormalities and in treatment planning.

Influence of morphological variations on cervical spine segmental responses from inertial loading.

OBJECTIVES: The objective of this study was to investigate the influence of morphological variations in osteoligamentous lower cervical spinal segment responses under postero-anterior inertial loading. METHODS: A parametric finite element model of the C5-C6 spinal segment was used to generate models. Variations in the vertebral body and facet depth (anteroposterior), posterior process length, intervertebral disc height, facet articular process height and slope, segment orientation ranging from lordotic to straight, and segment size were parameterized. These variations included male-female differences. A Latin hypercube sampling method was used to select parameter values for model generation. Forces and moments associated with the inertial loading were applied to the generated model segments. The 7 parameters were grouped as local or global depending on the number of spinal components involved in the shape variation. Four output responses representing overall segmental and soft tissue responses were analyzed for each model variation: response angle of the segment, anterior longitudinal ligament stretch, anterior capsular ligament stretch, and facet joint compression in the posterior region. Pearson's correlation coefficient was used to compute the correlations of these output responses with morphological variations. RESULTS: Fifty models were generated from the parameterized model using a Latin hypercube sampling technique. Variation in response angle among the models was 4° and was most influenced by change in the combined dimension of vertebral body and facet depth, followed by size of the segment. The maximum anterior longitudinal ligament stretch varied between 0.1 and 0.3 and was strongly influenced by the change in the segment orientation. The anterior facet joint region sustained tension, whereas the posterior region sustained compression. For the anterior capsular ligament stretch, the most influential global variation was segment orientation, whereas the most influential local variations were the facet height and facet angle parameters. In the case of posterior facet joint compression, segment orientation was again most influential, whereas among the local variations, the facet angle had the most influence. CONCLUSION: Shape variations in the intervertebral disc influenced segmental rotation and ligament responses; however, the influence of shape variations in the facet joint was confined to capsular ligament responses. Response angle was most influenced by the vertebral body depth variations, explaining greater segmental rotations in female spines. Straighter spine segments sustained greater posterior facet joint compression, which may offer an explanation for the higher incidence of whiplash-associated disorders among females, who exhibit a straighter cervical spine. The anterior longitudinal ligament stretch was also greater in straighter segments. These findings indicate that the morphological features specific to the anatomy of the female cervical spine may predispose it to injury under inertial loading.

Automatic segmentation of vertebral contours from CT images using fuzzy corners.

Automatic segmentation of bone in computed tomography (CT) images is critical for the implementation of computer-assisted diagnosis which has increasing potential in the evaluation of various spine disorders. Of the many techniques available for delineating the region of interest (ROI), active contour methods (ACM) are well-established techniques that are used to segment medical images. The initialization for these methods is either through manual intervention or by applying a global threshold, thus making them semi-automatic in nature. The paper presents a methodology for automatic contour initialization in ACM and demonstrates the applicability of the method for medical image segmentation from spinal CT images. Initially, a set of feature markers from the image is extracted to construct an initial contour for the ACM. A fuzzified corner metric, based on image intensity, is proposed to identify the feature markers to be enclosed by the contour. A concave hull based on α shape, is constructed using these fuzzy corners to give the initial contour. The proposed method was evaluated against conventional feature detectors and other initialization methods. The results show the method׳s robust performance in the presence of simulated Gaussian noise levels. The method enables the ACM to efficiently converge to the ground truth segmentation. The reference standard for comparison was the annotated images from a radiologist, and the Dice coefficient and Hausdorff distance measures were used to evaluate the segmentation.

Performance of Caesalpinia sappan heartwood extract as photo sensitizer for dye sensitized solar cells.

A natural dye extracted from Caesalpinia sappan heartwood was used as photo sensitizer for the first time to fabricate titanium dioxide (TiO2) nanoparticles based dye sensitized solar cells. Brazilin and brazilein are the major pigments present in the natural dye and their optimized molecular structure were calculated using Density functional theory (DFT) at 6-31G (d) level. The HOMO-LUMO were performed to reveal the energy gap using optimized structure. Pure TiO2 nanoparticles in anatase phase were synthesized by sol-gel technique. The pure and natural dye sensitized TiO2 nanoparticles were subjected to structural, optical, spectral and morphological studies. Low cost and environment friendly dye sensitized solar cells were fabricated using natural dye sensitized TiO2 based photo anode. The solar light to electron conversion efficiency of Caesalpinia sappan heartwood extract sensitized dye sensitized solar cell is 1.1%.

Synthesis, spectral analysis, optical and thermal properties of new organic NLO crystal: N,N'-Diphenylguanidinium Nitrate (DPGN).

A new organic NLO material N,N'-Diphenylguanidinium Nitrate (DPGN) single crystal was grown by slow evaporation technique using methanol as solvent. Single crystal X-ray diffraction and powder X-ray diffraction experiments were carried out in order to confirm the structure and crystalline nature of DPGN crystal. Wide band gap of 3.9eV with transmittance of 57% up to 800nm is observed for the grown crystal using UV-vis spectral analysis. The chemical bonding and presence of various functional groups were confirmed by the FT-IR and FT-Raman spectral studies. The thermal behavior of DPGN crystal was analyzed by simultaneous TG-DTA studies. The second harmonic generation (SHG) nonlinearity of the grown crystal was measured by Kurtz and Perry powder technique and was found to be comparable with that of the standard reference material potassium dihydrogen phosphate (KDP) crystal.