Pulmo CMS




Densitometry using Computed Tomography (CT) has proven to be a sensitive and reproducible method for the assessment of pulmonary emphysema and is therefore being used in numerous drug evaluation trials. This high sensitivity could be obtained because emphysema is characterized by a decrease in lung tissue mass and blood vessel area and increased areas of trapped air, causing an overall decrease in lung density. Applications in other lung diseases has also been explored, such as sarcoidosis, pulmonary edema, systemic sclerosis and cystic fibrosis.

To quantify lung density, Pulmo-CMS has been developed at the division of image processing (LKEB, department of Radiology, Leiden University Medical Center, the Netherlands) in corporation with Medis Specials. Its development is based on more than 20 years of experience in applying lung densitometry in trials on pulmonary emphysema, with special emphasis placed on the reproducibility of the measurement to guarantee accurate assessment of disease progression.

Pulmo-CMS automatically detects the lungs in CT volume scans and analyses the density distribution of the lungs. By identifying different partitions of the lungs with equal sub-volumes, one can assess regional changes in the CT parameters and quantifying the distribution of emphysema over the lungs. The software is used in numerous reference sites for assessing the effects of drugs treatment in large clinical trials (EXACTLE, TESRA,REPAIR, ZEMAIRA). Advantages of Pulmo-CMS are the ability to assist diagnosis, improve therapy planning for lung volume reduction surgery and disease monitoring.


Method


Basically, the software package consists of four parts:

  • Recalibration. The densities in the images are recalibrated by rescaling these densities depending upon the measured density of blood and of air outside of the patient. By this recalibration procedure, changes in the spectrum of the X-ray tube, due to tube ageing, and changes in the patient's blood density, due the emphysema, are taken into account. The measurement of the mean blood density is carried out by a semi-automatic detection of the largest artery, the aorta, and by calculating the mean density value within this region, with the lowest standard error.

  • (Semi-)automatic selection of the lungs. The lungs are selected automatically by an automatically detected location within the trachea. Subsequently, the software carries out a so-called region growing, which lets this initial location expand (like a balloon) until it reaches the borders of the lungs. The septum (the border between the left and right lung), the trachea and the carina are detected separately. The lung parenchyma is defined by excluding the septa, trachea and large vessels from the initial segmentation result. By doing so, the lungs are selected with a high degree of reproducibility. A number of editing tools have been developed to facilitate user-friendly procedures for correcting the contours; after editing the program checks the resulting contours for inconsistencies.

  • Calculation and analysis of the density distributions. The histograms of the left and right lung parenchyma are calculated. From these histograms seven parameters are derived: the total lung volume, mean lung density, the lung weight (which is equal to the product of volume and density), the n-th percentile point (as described above), the area of the lungs (in %) below a certain density value, called the relative area, the heterogeneity of the density over the different partitions and the locality of the emphysema (basal vs apical).

  • Presentation of the results. Finally, the results are presented to the user, and can be saved on disk to facilitate further statistical analysis by spreadsheet programs or statistical packages.

  • Reference normal database. Lung density should not only be calculated but also be compared to normal healthy lung densities. By generating graphic reports of a Z-score, the results of individual patients can be interpreted.

More information

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