J Karmrodt | PubMed Health

PubMed2001

[Multi-rotation CT and acute respiratory distress syndrome. Animal experiment studies].

PURPOSE: Aim of the study was to investigate alveolar inspiration and expiration using multiscan CT. Results of a visual assessment using a scoring system were compared with density ranges known to represent alveolar ventilation best. METHOD: Pigs were examined before and after lavage-induced ARDS. All animals were examined using dynamic multiscan CT. The visual assessment was done by a scoring system proposed by Gattinoni. The results were compared with planimetric determination of defined density ranges. RESULTS: In the healthy lung, the visual analysis showed higher scores at lower airway pressures with a marked gradient, whereas at higher pressures neither opacities nor gradients were observed. In ARDS-lungs, the scores were double as high as in healthy lungs at low pressures. At the same time the differences between inspiration and expiration were minor. There was good correlation between lung density measurements and lung opacities under different airway pressures. In healthy lungs, the greatest area increase is found between -910 and -700 HU. The biggest area growth in the ARDS-model is observed between -910 and -300 HU. CONCLUSION: Dynamic multiscan CT allows for determining different ventilation-relevant lung compartments and lung density ranges.

Animals↗

PubMed2001

[A software tool for automatic image-based ventilation analysis using dynamic chest CT-scanning in healthy and in ARDS lungs].

PURPOSE: Density measurements in dynamic CT image series of the lungs allow one to quantify ventilated, hyperinflated, and atelectatic pulmonary compartments with high temporal resolution. Fast automatic segmentation of lung parenchyma and a subsequent evaluation of it's respective density values are a prerequisite for any clinical application of this technique. MATERIAL AND METHODS: For automatic lung segmentation in thoracic CT scans, an algorithm was developed which uses (a) different density masks, and (b) anatomic knowledge to differentiate heart, diaphragm and chest wall from ventilated and atelectatic lung parenchyma. With Animal Care Committee approval, the automated technique was tested in 8 anaesthetized ventilated pigs undergoing dynamic CT before and after induction of lavage-ARDS. Images were acquired in one supradiaphragmatic, cross-sectional slice (temporal resolution of 100 ms; slice thickness of 1 mm, high resolution reconstruction algorithm). In 120 CT images the total pixel number and the calculated MLD from the automatically segmentated lung were compared to the values obtained from an interactive lung segmentation. RESULTS: The software tool was able to read all image series (DICOM standard). Automatic and interactive segmentation were in high agreement (R(2) = 0.99 for the total number of pixels and the MLD). Originally, the most frequent error was misclassification of atelectasis as extrapulmonary solid tissue. CONCLUSION: An automatic software tool is presented for lung segmentation in healthy lungs and in ARDS. Aerated lung and atelectasis were identified with high accuracy. This post-processing tool allows for a quantitative, CT based assessment of ventilation and recruitment processes in the lung. Thus, it may help to optimize ventilation patterns in patients with ARDS.

Animals↗