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


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.


CONCLUSIONS
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.
Markstaller
K
K
Klinik und Poliklinik für Radiologie, Johannes-Gutenberg-Universität Mainz. klm@mail.uni-mainz.de
Arnold
M
M
Döbrich
M
M
Heitmann
K
K
Karmrodt
J
J
Weiler
N
N
Uthmann
T
T
Eberle
B
B
Thelen
M
M
Kauczor
H U
HU
D000818:Animals; D003937:Diagnosis, Differential; D004195:Disease Models, Animal; D006801:Humans; D007091:Image Processing, Computer-Assisted; D001261:Pulmonary Atelectasis / Q000000981:diagnostic imaging* / Q000503:physiopathology; D011659:Pulmonary Gas Exchange / Q000502:physiology*; D012128:Respiratory Distress Syndrome / Q000000981:diagnostic imaging* / Q000503:physiopathology; D012680:Sensitivity and Specificity; D012984:Software; D013552:Swine; D014057:Tomography, X-Ray Computed; D014692:Ventilation-Perfusion Ratio / Q000502:physiology