Numerical and experimental investigation of mucociliary clearance breakdown in cystic fibrosis.

The human tracheobronchial tree surface is covered with mucus. A healthy mucus is a heterogeneous material flowing toward the esophagus and a major defense actor against local pathogen proliferation and pollutant deposition. An alteration of mucus or its environment such as in cystic fibrosis dramatically impacts the mucociliary clearance. In the present study, we investigate the mechanical organization and the physics of such mucus in human lungs by means of a joint experimental and numerical work. In particular, we focus on the influence of the shear-thinning mucus mobilized by a ciliated epithelium for mucociliary clearance. The proposed robust numerical method is able to manage variations of more than 5 orders of magnitude in the shear rate and viscosity. It leads to a cartography that allows to discuss major issues on defective mucociliary clearance in cystic fibrosis. Furthermore, the computational rheological analysis based on measurements shows that cystic fibrosis shear-thinning mucus tends to aggregate in regions of lower clearance. Yet, a rarefaction of periciliary fluid has a greater impact than the mucus shear-thinning effects.

A parametric study of mucociliary transport by numerical simulations of 3D non-homogeneous mucus.

Mucociliary clearance is the natural flow of the mucus which covers and protects the lung from the outer world. Pathologies, like cystic fibrosis, highly change the biological parameters of the mucus flow leading to stagnation situations and pathogens proliferation. As the lung exhibits a complex dyadic structure, in-vivo experimental study of mucociliary clearance is almost impossible and numerical simulations can bring important knowledge about this biological flow. This paper brings a detailed study of the biological parameters influence on the mucociliary clearance, in particular for pathological situations such as cystic fibrosis. Using recent suitable numerical methods, a non-homogeneous mucus flow (including non-linearities) can be simulated efficiently in 3D, allowing the identification of the meaningful parameters involved in this biological flow. Among these parameters, it is shown that the mucus viscosity, the stiffness transition between pericilliary fluid and mucus, the pericilliary fluid height as well as both cilia length and beating frequency have a great influence on the mucociliary transport.

Clinical impact of optical imaging with 3-D reconstruction of torso topography in common anterior chest wall anomalies.

BACKGROUND: Standard modalities to assist in determining the extent of chest wall developmental deformities in patients include x-ray and computed tomography (CT). The purpose of this study is to describe an optical imaging technique that provides accurate cross-sectional images of the chest, and to compare these with standard CT-derived images of chest wall abnormalities. PATIENTS AND METHODS: Ten patients (5 pectus excavatum and 5 pectus carinatum) underwent imaging that included limited CT and optical cross-sectional imaging. Severity indices of the deformity using the standard Haller index (HI) were calculated from CT scans. A similar severity measurement of deformity was derived from the outline of torso cross sections (ie, from skin to skin measurements) obtained from optical images. To assess the severity of carinatum defects, a modified pectus index was derived, which measures the anterior chest protrusion from the central chord of the chest cross section. We performed regression analyses, comparing the indices obtained from CT and optical imaging methodologies. RESULTS: Optical measures of cross-sectional deformities correlated well with standard HI (r2 = 0.94) and even better with the modified pectus index (r2 = 0.96). Adaptation of the HI for pectus carinatum deformity evaluation was effective, and consistent with the torso surface deformity measures. CONCLUSIONS: Torso models from optical imaging offer 3-D images of the chest wall deformity with no radiation exposure. This preliminary study showed promising results for the use of torso surface measurement as an alternative index of pectus deformities; if validated in larger studies, these measures may be useful for following chest wall abnormalities, using repeated studies in patients.

The Calgary protocol for bracing of pectus carinatum: a preliminary report.

BACKGROUND: The optimal treatment of pectus carinatum (PC) deformities is unclear. We propose a nonoperative approach using a lightweight, patient-controlled dynamic chest-bracing device. MATERIAL AND METHODS: With ethical approval, 24 patients with PC were treated at the Alberta Children's Hospital between January 1998 and April 2005. There were 6 (25%) females and 18 (75%) males, with a mean age of 12.9 years at the onset of treatment. Treatment involved fitting of a lightweight, patient-controlled chest brace, worn for 23 hours per day (correction phase [CP]) until the convex deformity was corrected. Following correction of the deformity, bracing was reduced to 8 hours per day (maintenance phase) until axial skeletal maturation ceased. Monitoring was done by measurement of the external pectus carinatum protrusion as well as subjective patient and surgeon appraisal of appearance and exercise tolerance. RESULTS: Nineteen (79.2%) patients have completed initial treatment (mean CP time, 4.3 +/- 2.1 months). There were 3 patients (12.5%) who were noncompliant, and 2 (8.3%) are still in the initial CP phase of therapy. Fourteen (58.3%) patients are presently in maintenance phase, nocturnally braced, and 2 (8.3%) have completed therapy. In patients completing initial treatment, the protrusion pectus carinatum protrusion (pre 22 +/- 6 vs post 6.0 +/- 6.2) and subjective appearance (change + 1.8+/-0.4) showed a significant improvement (P < .001 for both) with no change in exercise tolerance. CONCLUSION: Compressive bracing results in a significant subjective and objective improvement in PC appearance in skeletally immature patients. However, patient compliance and diligent follow up appear to be paramount for the success of this method of treatment. Further studies are required to show the durability of this method of treatment.

Prediction of scoliosis progression in time series using a hybrid learning technique.

Scoliosis is a common and poorly understood spinal disorder that is clinically monitored with a series of full spinal X-rays. The purpose of this study was to predict scoliosis future progression at 6- and 12-month intervals with successive spinal indices and a hybrid learning technique (i.e., the combination of fuzzy c-means clustering and artificial neural network (ANN)). Ultimately this could decrease scoliotic patients' radiation exposure and the associated cancer risk in growing adolescents. Seventy-two data sets were derived from a database of 56 acquisitions from 11 subjects (29.8 +/- 9.6 degrees Cobb angle, 11.4 +/- 2.4 yr), each consisting of 4 sequential values of Cobb angle and lateral deviations at apices in 6- and 12-month intervals in the coronal plane. Progression patterns in Cobb angles (n = 10) and lateral deviations (n = 8) were successfully identified using a fuzzy c-means clustering algorithm. The accuracies of the trained ANN, having a structure of three input variables, four nonlinear hidden nodes, and one linear output variable, for training and test data sets were within 3.64 degrees (+/- 2.58 degrees) and 4.40 degrees (+/- 1.86 degrees) of Cobb angles, and within 3.59 (+/-3.96) mm and 3.98 (+/- 3.41) mm of lateral deviations, respectively. Those results were twice the accuracy of typical clinical measurement (~10 degrees) and in close agreement with those using cubic spline extrapolation and adaptive neuro-fuzzy inference system (ANFIS) techniques. The adapted technique for predicting the scoliosis deformity progression holds significant promise for clinical applications.

Genetic algorithm-neural network estimation of cobb angle from torso asymmetry in scoliosis.

Scoliosis severity, measured by the Cobb angle, was estimated by artificial neural network from indices of torso surface asymmetry using a genetic algorithm to select the optimal set of input torso indices. Estimates of the Cobb angle were accurate within 5 degrees in two-thirds, and within 10 degrees in six-sevenths, of a test set of 115 scans of 48 scoliosis patients, showing promise for future longitudinal studies to detect scoliosis progression without use of X-rays.

Indices of torso asymmetry related to spinal deformity in scoliosis.

OBJECTIVE: To develop indices that quantify 360 degrees torso surface asymmetry sufficiently well to estimate the Cobb angle of scoliotic spinal deformity within the clinically important 5-10 degrees range. DESIGN: Prospective study in 48 consecutive adolescent scoliosis patients (Cobb angles 10-71 degrees ). BACKGROUND: Scoliotic surface asymmetry has been quantified on the back surface by indices such as back surface rotation (BSR) and curvature of the spinous process line and torso centroid line, though with limited success in spinal deformity estimation. Quantification of 360 degrees torso shape may enhance surface-spine correlation and permit reduced use of harmful X-rays in scoliosis. METHODS: For each patient a 3D torso surface model was generated concurrently with postero-anterior X-rays. We computed indices describing principal axis orientation, back surface rotation, and asymmetry of the torso centroid line, left and right half-areas and the spinous process line. We calculated correlations of each index to the Cobb angle and used stepwise regression to estimate the Cobb angle. RESULTS: Several torso asymmetry indices correlated well to the Cobb angle (r up to 0.8). The Cobb angle was best estimated by age, rib hump and left-right variation in torso width in unbraced patients and by centroid lateral deviation in braced patients. A regression model estimated the Cobb angle from torso indices within 5 degrees in 65% of patients and 10 degrees in 88% (r=0.91, standard error=6.1 degrees ). CONCLUSION: Consideration of 360 degrees torso surface data yielded indices that correlated well to the Cobb angle and estimated the Cobb angle within 10 degrees in 88% of cases. RELEVANCE: The torso asymmetry indices developed here show a strong surface-spine relation in scoliosis, encouraging development of a model to detect scoliosis magnitude and progression from the surface shape with minimal X-ray radiation.

Comparison of Cobb angles measured manually, calculated from 3-D spinal reconstruction, and estimated from torso asymmetry.

While scoliotic spinal deformity is traditionally measured by the Cobb angle, we seek to estimate scoliosis severity from the torso surface without X-ray radiation. Here, we measured the Cobb angle in three ways: by protractor from postero-anterior X-ray, by computer from a 3-D digitized model of the vertebral body line, and by neural-network estimation from indices of torso surface asymmetry. The estimates of the Cobb angle by computer and by neural network were equally accurate in 153 records from 52 patients (standard deviation of 6 degrees from the Cobb angle, r=0.93), showing that torso asymmetry reliably predicted spinal deformity. Further improvements in predictive accuracy may require estimation of other 3-D indices of spinal deformity besides the Cobb angle with its wide measurement variability.

Prediction of spinal deformity in scoliosis from geometric torsion.

The shape of a curved line that passes through thoracic and lumbar vertebrae is often used to study spinal deformity with measurements in "auxiliary" planes that are not truly three-dimensional (3D). Here we propose a new index, the geometric torsion, which could uniquely describe the spinal deformity. In this study we assessed whether geometric torsion could be effectively used. to predict spinal deformity with the aid of multiple linear regression. Anatomical landmarks were obtained from multi-view radiographic reconstruction and used to generate 3D model of the spine and rib cage of 28 patients. Fourier series best fitted to the vertebral centroids approximated the spinal shape. For each patient, spinal deformity indices were computed. Torsion was calculated and 20 derived parameters were recorded. Torsion inputs were used in a multiple linear regression model for prediction of key spinal indices. The primary clinical Cobb angle (mainly thoracic) was predicted well, with r=0.89 using all 20 inputs of torsion or r=0.83 using just two. Torsion was also well related to the orientation of plane of maximal deformity (r=0.87). Torsion was less accurate but still significant in predicting maximal vertebral axial rotation (r=0.77). This preliminary study showed promising results for the use of geometric torsion as an alternative 3D index of spinal deformity.

Curvilinear Three-Dimensional Modeling of Spinal Curves with Dual Kriging.

In spinal deformation studies, three-dimensional reconstruction of the spine is frequently represented as a curve in space fitted to the vertebral centroids. Conventional interpolation techniques such as splines, Bezier and the least squares method are limited since they cannot describe precisely the great variety of spinal morphologies. This article presents a more general technique called dual kriging, which includes two mathematical constituents (drift and covariance) to adjust the interpolated functions to spinal deformity better. The cross-validation technique was used to compare the parametric representations of spinal curves with different combinations of drift and covariance functions. Model validation was performed from a series of analytic curves reflecting typical scoliotic spines. Calculation of geometric torsion, a sensitive parameter, was done to evaluate the accuracy of the kriging models. The best model showed an absolute mean difference of 1.2 x 10(-5) (+/- 7.1 x 10(-5) ) mm(-1) between the analytical and estimated geometric torsions compared to 5.25 x 10(-3) (+/- 3.7 x 10(-2) ) mm(-1) for the commonly used least-squares Fourier series method, a significant improvement in spinal torsion evaluation.