In silico modeling of oxygen-enhanced MRI of specific ventilation.

Specific ventilation imaging (SVI) proposes that using oxygen-enhanced 1H MRI to capture signal change as subjects alternatively breathe room air and 100% O2 provides an estimate of specific ventilation distribution in the lung. How well this technique measures SV and the effect of currently adopted approaches of the technique on resulting SV measurement is open for further exploration. We investigated (1) How well does imaging a single sagittal lung slice represent whole lung SV? (2) What is the influence of pulmonary venous blood on the measured MRI signal and resultant SVI measure? and (3) How does inclusion of misaligned images affect SVI measurement? In this study, we utilized two patient-based in silico models of ventilation, perfusion, and gas exchange to address these questions for normal healthy lungs. Simulation results from the two healthy young subjects show that imaging a single slice is generally representative of whole lung SV distribution, with a calculated SV gradient within 90% of that calculated for whole lung distributions. Contribution of O2 from the venous circulation results in overestimation of SV at a regional level where major pulmonary veins cross the imaging plane, resulting in a 10% increase in SV gradient for the imaging slice. A worst-case scenario simulation of image misalignment increased the SV gradient by 11.4% for the imaged slice

Active contour approach for accurate quantitative airway analysis

Chronic airway disease causes structural changes in the lungs including peribronchial thickening and airway dilatation. Multi-detector computed tomography (CT) yields detailed near-isotropic images of the lungs, and thus the potential to obtain quantitative measurements of lumen diameter and airway wall thickness. Such measurements would allow standardized assessment, and physicians to diagnose and locate airway abnormalities, adapt treatment, and monitor progress over time. However, due to the sheer number of airways per patient, systematic analysis is infeasible in routine clinical practice without automation. We have developed an automated and real-time method based on active contours to estimate both airway lumen and wall dimensions; the method does not require manual contour initialization but only a starting point on the targeted airway. While the lumen contour segmentation is purely region-based, the estimation of the outer diameter considers the inner wall segmentation as well as local intensity variation, in order anticipate the presence of nearby arteries and exclude them. These properties make the method more robust than the Full-Width Half Maximum (FWHM) approach. Results are demonstrated on a phantom dataset with known dimensions and on a human dataset where the automated measurements are compared against two human operators. The average error on the phantom measurements was 0.10mm and 0.14mm for inner and outer diameters, showing sub-voxel accuracy. Similarly, the mean variation from the average manual measurement was 0.14mm and 0.18mm for inner and outer diameters respectively

Gender assessment through three-dimensional analysis of maxillary sinuses by means of Cone Beam Computed Tomography

OBJECTIVE: The availability of a low dose radiation technology such as Cone Beam Computed Tomography (CBCT) in dental practice has increased the number of scans available for forensic purposes. Moreover, specific software allows for three-dimensional (3D) characterization of the maxillary sinuses. This study was performed to determine whether sinus maxillary volumes can be useful to identify gender after validating the use of the Dolphin software as a tool for volumetric estimation of maxillary sinus volumes. PATIENTS AND METHODS: The validation was performed by four different operators measuring the volume of six phantoms, where the real volume was already known. The maxillary sinus volumes of 52 patients (26 males and 26 females) mean age 24.3 were calculated and compared between genders and sagittal skeletal class subdivision. The measurements for patients and phantoms were based on CBCT scans (ILUMA™) processed by Dolphin 3D software. RESULTS: No statistical difference was observed between the real volume and the volume measurements performed by the operators. No statistical difference was found in patient's maxillary sinus volumes between gender. CONCLUSIONS: Based on our results, it is not possible to support the use of maxillary sinuses to discern sexual difference in corpse identification

Dimensional Changes of Upper Airway after Rapid Maxillary Expansion: A Prospective Cone-beam Computed Tomography Study

Introduction: The aim of this prospective study was to use cone-beam computed tomography to assess the dimensional changes of the upper airway in orthodontic patients with maxillary constriction treated by rapid maxillary expansion. Methods: Fourteen orthodontic patients (mean age, 12.9 years; range, 9.7-16 years) were recruited. The patients with posterior crossbite and constricted maxilla were treated with rapid maxillary expansion as the initial part of their comprehensive orthodontic treatments. Before and after rapid maxillary expansion conebeam computed tomography scans were taken to measure the retropalatal and retroglossal airway changes in terms of volume, and sagittal and cross-sectional areas. The transverse expansions by rapid maxillary expansion were assessed between the midlingual alveolar bone plates at the maxillary first molar and first premolar levels. The measurements of the before and after rapid maxillary expansion scans were compared by using paired t tests with the Bonferroni adjustment for multiple comparisons. Results: After rapid maxillary expansion, significant and equal amounts of 4.8 mm of expansion were observed at the first molar (P 5 0.0000) and the first premolar (P 5 0.0000) levels. The width increase at the first premolar level (20.0%) was significantly greater than that at the first molar level (15.0%) (P 5 0.035). As the primary outcome variable, the cross-sectional airway measured from the posterior nasal spine to basion level was the only parameter showing a significant increase of 99.4 mm2 (59.6%) after rapid maxillary expansion (P 5 0.0004). Conclusions: These results confirm the findings of previous studies of the effect of rapid maxillary expansion on the maxilla. Additionally, we found that only the cross-sectional area of the upper airway at the posterior nasal spine to basion level significantly gains a moderate increase after rapid maxillary expansion

Patient specific numerical simulation of flow in the human upper airways for assessing the effect of nasal surgery

The study is looking into the potential of using computational fluid dynamics (CFD) as a tool for predicting the outcome of surgery for alleviation of obstructive sleep apnea syndrome (OSAS). From pre- and post-operative computed tomography (CT) of an OSAS patient, the pre- and post-operative geometries of the patient's upper airways were generated. CFD simulations of laminar flow in the patient's upper airway show that after nasal surgery the mass flow is more evenly distributed between the two nasal cavities and the pressure drop over the nasal cavity has increased. The pressure change is contrary to clinical measurements that the CFD results have been compared with, and this is most likely related to the earlier steps of modelling - CT acquisition and geometry retrieval.Comment: Proceedings of the 12th International Conference on CFD in Oil & Gas, Metallurgical and Process Industries, Trondheim, Norway, May 30th - June 1st, 2017, 11 pages, 13 figure

Spinal involvement in mucopolysaccharidosis IVA (Morquio-Brailsford or Morquio A syndrome): presentation, diagnosis and management.

Mucopolysaccharidosis IVA (MPS IVA), also known as Morquio-Brailsford or Morquio A syndrome, is a lysosomal storage disorder caused by a deficiency of the enzyme N-acetyl-galactosamine-6-sulphate sulphatase (GALNS). MPS IVA is multisystemic but manifests primarily as a progressive skeletal dysplasia. Spinal involvement is a major cause of morbidity and mortality in MPS IVA. Early diagnosis and timely treatment of problems involving the spine are critical in preventing or arresting neurological deterioration and loss of function. This review details the spinal manifestations of MPS IVA and describes the tools used to diagnose and monitor spinal involvement. The relative utility of radiography, computed tomography (CT) and magnetic resonance imaging (MRI) for the evaluation of cervical spine instability, stenosis, and cord compression is discussed. Surgical interventions, anaesthetic considerations, and the use of neurophysiological monitoring during procedures performed under general anaesthesia are reviewed. Recommendations for regular radiological imaging and neurologic assessments are presented, and the need for a more standardized approach for evaluating and managing spinal involvement in MPS IVA is addressed

Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks

Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications. In this work, we extract trees or collection of sub-trees from image data by, first deriving a graph-based representation of the volumetric data and then, posing the tree extraction as a graph refinement task. We present two methods to perform graph refinement. First, we use mean-field approximation (MFA) to approximate the posterior density over the subgraphs from which the optimal subgraph of interest can be estimated. Mean field networks (MFNs) are used for inference based on the interpretation that iterations of MFA can be seen as feed-forward operations in a neural network. This allows us to learn the model parameters using gradient descent. Second, we present a supervised learning approach using graph neural networks (GNNs) which can be seen as generalisations of MFNs. Subgraphs are obtained by training a GNN-based graph refinement model to directly predict edge probabilities. We discuss connections between the two classes of methods and compare them for the task of extracting airways from 3D, low-dose, chest CT data. We show that both the MFN and GNN models show significant improvement when compared to one baseline method, that is similar to a top performing method in the EXACT'09 Challenge, and a 3D U-Net based airway segmentation model, in detecting more branches with fewer false positives.Comment: Accepted for publication at Medical Image Analysis. 14 page

Absolute electrical impedance tomography (aEIT) guided ventilation therapy in critical care patients: simulations and future trends

Thoracic electrical impedance tomography (EIT) is a noninvasive, radiation-free monitoring technique whose aim is to reconstruct a cross-sectional image of the internal spatial distribution of conductivity from electrical measurements made by injecting small alternating currents via an electrode array placed on the surface of the thorax. The purpose of this paper is to discuss the fundamentals of EIT and demonstrate the principles of mechanical ventilation, lung recruitment, and EIT imaging on a comprehensive physiological model, which combines a model of respiratory mechanics, a model of the human lung absolute resistivity as a function of air content, and a 2-D finite-element mesh of the thorax to simulate EIT image reconstruction during mechanical ventilation. The overall model gives a good understanding of respiratory physiology and EIT monitoring techniques in mechanically ventilated patients. The model proposed here was able to reproduce consistent images of ventilation distribution in simulated acutely injured and collapsed lung conditions. A new advisory system architecture integrating a previously developed data-driven physiological model for continuous and noninvasive predictions of blood gas parameters with the regional lung function data/information generated from absolute EIT (aEIT) is proposed for monitoring and ventilator therapy management of critical care patients