Advances in imaging chest tuberculosis: blurring of differences between children and adults

This article reviews the ongoing role of imaging in the diagnosis of tuberculosis (TB) and its complications. A modern imaging classification of TB, taking into account both adults and children and the blurring of differences in the presentation patterns, must be absorbed into daily practice. Clinicians must not only be familiar with imaging features of TB but also become expert at detecting these when radiologists are unavailable. Communication between radiologists and clinicians with regard to local constraints, patterns of disease, human immunodeficiency virus (HIV) coinfection rates, and imaging parameters relevant for management (especially in drug resistance programs) is paramount for making an impact with imaging, and preserving clinician confidence. Recognition of special imaging, anatomic and vulnerability differences between children and adults is more important than trying to define patterns of disease exclusive to children

Semiautomated Skeletonization of the Pulmonary Arterial Tree in Micro-CT Images

We present a simple and robust approach that utilizes planar images at different angular rotations combined with unfiltered back-projection to locate the central axes of the pulmonary arterial tree. Three-dimensional points are selected interactively by the user. The computer calculates a sub- volume unfiltered back-projection orthogonal to the vector connecting the two points and centered on the first point. Because more x-rays are absorbed at the thickest portion of the vessel, in the unfiltered back-projection, the darkest pixel is assumed to be the center of the vessel. The computer replaces this point with the newly computer-calculated point. A second back-projection is calculated around the original point orthogonal to a vector connecting the newly-calculated first point and user-determined second point. The darkest pixel within the reconstruction is determined. The computer then replaces the second point with the XYZ coordinates of the darkest pixel within this second reconstruction. Following a vector based on a moving average of previously determined 3- dimensional points along the vessel\u27s axis, the computer continues this skeletonization process until stopped by the user. The computer estimates the vessel diameter along the set of previously determined points using a method similar to the full width-half max algorithm. On all subsequent vessels, the process works the same way except that at each point, distances between the current point and all previously determined points along different vessels are determined. If the difference is less than the previously estimated diameter, the vessels are assumed to branch. This user/computer interaction continues until the vascular tree has been skeletonized

Pulmonary Abnormalities in Mice with Paracoccidioidomycosis: A Sequential Study Comparing High Resolution Computed Tomography and Pathologic Findings

Paracoccidioidomycosis (PCM) is a fungal infection caused by the dimorphic fungus Paracoccidioides brasiliensis. It occurs preferentially in rural workers in whom the disease is severe and may cause incapacitating pulmonary sequelae. Assessment of disease progression and treatment outcome normally includes chest x-rays or CT studies. Existing experimental PCM models have focused on several aspects, but none has done a radiologic or image follow-up evaluation of pulmonary lesions considered as the fungus primary target. In this study, the lungs of mice infected with fungal conidia were studied sequentially during the chronic stage of their experimental mycosis by noninvasive high resolution medical computed tomography, and at time of sacrifice, also by histopathology to characterize pulmonary abnormalities. Three basic lung lesion patterns were revealed by both techniques: nodular-diffuse, confluent and pseudo-tumoral which were located mainly around the hilus thus accurately reflecting the situation in human patients. The experimental design of this study decreases the need to sacrifice a large number of animals, and serves to monitor treatment efficacy by means of a more rational approach to the study of human pulmonary diseases. The findings we are reporting open new avenues for experimental research, increase our understanding of the mycosis pathogenesis and consequently have repercussions in patients' care

Semi-Automatic Measurement of the Airway Dimension by Computed Tomography Using the Full-Width-Half-Maximum Method: a Study on the Measurement Accuracy according to the CT Parameters and Size of the Airway

Objective: To assess the influence of variable factors such as the size of the airway and the CT imaging parameters such as the reconstruction kernel, field-ofview (FOV), and slice thickness on the automatic measurement of airway dimension. Materials and Methods: An airway phantom was fabricated that contained eleven poly-acryl tubes of various lumen diameters and wall thicknesses. The measured density of the poly-acryl wall was 150 HU, and the measured density of the airspace filled with polyurethane foam was 900 HU. CT images were obtained using a 16-MDCT (multidetector CT) scanner and were reconstructed with various reconstruction kernels, thicknesses and FOV. The luminal radius and wall thickness were measured using in-house software based on the fullwidth- half-maximum method. The measured values as determined by CT and the actual dimensions of the tubes were compared. Results: Measurements were most accurate on images reconstructed with use of a standard kernel (mean error: 0.03+-0.21 mm for wall thickness and 0.12 0.11 mm for the luminal radius). There was no significant difference in accuracy among images with the use of variable slice thicknesses or a variable FOV. Below a 1-mm threshold, the measurement failed to represent the change of the real dimensions. Conclusion: Measurement accuracy was strongly influenced by the specific reconstruction kernel utilized. For accurate measurement, standardization of the imaging protocol and selection of the appropriate anatomic level are essential.This work was supported by a Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MOST) (No. R01-2006-000-11244-0)

New insights on COPD imaging via CT and MRI

Multidetector-row computed tomography (MDCT) can be used to quantify morphological features and investigate structure/function relationship in COPD. This approach allows a phenotypical definition of COPD patients, and might improve our understanding of disease pathogenesis and suggest new therapeutical options. In recent years, magnetic resonance imaging (MRI) has also become potentially suitable for the assessment of ventilation, perfusion and respiratory mechanics. This review focuses on the established clinical applications of CT, and novel CT and MRI techniques, which may prove valuable in evaluating the structural and functional damage in COPD

Development and validation of HRCT airway segmentation algorithms

Direct measurements of airway lumen and wall areas are potentially useful as a diagnostic tool and as an aid to understanding the pathophysiology underlying lung disease. Direct measurements can be made from images created by high resolution computer tomography (HRCT) by using computer-based algorithms to segment airways, but current validation techniques cannot adequately establish the accuracy and precision of these algorithms. A detailed review of HRCT airway segmentation algorithms was undertaken, from which three candidate algorithm designs were developed. A custom Windows-based software program was implemented to facilitate multi-modality development and validation of the segmentation algorithms. The performance of the algorithms was examined in clinical HRCT images. A centre-likelihood (CL) ray-casting algorithm was found to be the most suitable algorithm due to its speed and reliability in semi-automatic segmentation and tracking of the airway wall. Several novel refinements were demonstrated to improve the CL algorithm’s robustness in HRCT lung data. The performance of the CL algorithm was then quantified in two-dimensional simulated data to optimise customisable parameters such as edge-detection method, interpolation and number of rays. Novel correction equations to counter the effects of volume averaging and airway orientation angle were derived and demonstrated in three-dimensional simulated data. The optimal CL algorithm was validated with HRCT data using a plastic phantom and a pig lung phantom matched to micro-CT. Accuracy was found to be improved compared to previous studies using similar methods. The volume averaging correction was found to improve precision and accuracy in the plastic phantom but not in the pig lung phantom. When tested in a clinical setting the results of the optimised CL algorithm was in agreement with the results of other measures of lung function. The thesis concludes that the relative contributions of confounders of airway measurement have been quantified in simulated data and the CL algorithm’s performance has been validated in a plastic phantom as well as animal model. This validation protocol has improved the accuracy and precision of measurements made using the CL algorith

Computed tomography image analysis for the detection of obstructive lung diseases

Damage to the small airways resulting from direct lung injury or associated with many systemic disorders is not easy to identify. Non-invasive techniques such as chest radiography or conventional tests of lung function often cannot reveal the pathology. On Computed Tomography (CT) images, the signs suggesting the presence of obstructive airways disease are subtle, and inter- and intra-observer variability can be considerable. The goal of this research was to implement a system for the automated analysis of CT data of the lungs. Its function is to help clinicians establish a confident assessment of specific obstructive airways diseases and increase the precision of investigation of structure/function relationships. To help resolve the ambiguities of the CT scans, the main objectives of our system were to provide a functional description of the raster images, extract semi-quantitative measurements of the extent of obstructive airways disease and propose a clinical diagnosis aid using a priori knowledge of CT image features of the diseased lungs. The diagnostic process presented in this thesis involves the extraction and analysis of multiple findings. Several novel low-level computer vision feature extractors and image processing algorithms were developed for extracting the extent of the hypo-attenuated areas, textural characterisation of the lung parenchyma, and morphological description of the bronchi. The fusion of the results of these extractors was achieved with a probabilistic network combining a priori knowledge of lung pathology. Creating a CT lung phantom allowed for the initial validation of the proposed methods. Performance of the techniques was then assessed with clinical trials involving other diagnostic tests and expert chest radiologists. The results of the proposed system for diagnostic decision-support demonstrated the feasibility and importance of information fusion in medical image interpretation.Open acces