Healthy Lung Vessel Morphology Derived From Thoracic Computed Tomography

Knowledge of the lung vessel morphology in healthy subjects is necessary to improve our understanding about the functional network of the lung and to recognize pathologic deviations beyond the normal inter-subject variation. Established values of normal lung morphology have been derived from necropsy material of only very few subjects. In order to determine morphologic readouts from a large number of healthy subjects, computed tomography pulmonary angiography (CTPA) datasets, negative for pulmonary embolism, and other thoracic pathologies, were analyzed using a fully-automatic, in-house developed artery/vein separation algorithm. The number, volume, and tortuosity of the vessels in a diameter range between 2 and 10mm were determined. Visual inspection of all datasets was used to exclude subjects with poor image quality or inadequate artery/vein separation from the analysis. Validation of the algorithm was performed manually by a radiologist on randomly selected subjects. In 123 subjects (men/women: 55/68), aged 59 +/- 17 years, the median overlap between visual inspection and fully-automatic segmentation was 94.6% (69.2-99.9%). The median number of vessel segments in the ranges of 8-10, 6-8, 4-6, and 2-4 mm diameter was 9, 34, 134, and 797, respectively. Number of vessel segments divided by the subject's lung volume was 206 vessels/L with arteries and veins contributing almost equally. In women this vessel density was about 15% higher than in men. Median arterial and venous volumes were 1.52 and 1.54% of the lung volume, respectively. Tortuosity was best described with the sum-of-angles metric and was 142.1 rad/m (138.3-144.5 rad/m). In conclusion, our fully-automatic artery/vein separation algorithm provided reliable measures of pulmonary arteries and veins with respect to age and gender. There was a large variation between subjects in all readouts. No relevant dependence on age, gender, or vessel type was observed. These data may provide reference values for morphometric analysis of lung vessels

Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.Rudyanto, RD.; Kerkstra, S.; Van Rikxoort, EM.; Fetita, C.; Brillet, P.; Lefevre, C.; Xue, W.... (2014). Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study. Medical Image Analysis. 18(7):1217-1232. doi:10.1016/j.media.2014.07.003S1217123218

Reading pulmonary vascular pressure tracings: How to handle the problems of zero leveling and respiratory swings

The accuracy of pulmonary vascular pressure measurements is of great diagnostic and prognostic relevance. However, there is variability of zero leveling procedures, and the current recommendation of end-expiratory reading may not always be adequate. A review of physiological and anatomical data, supported by recent imaging, leads to the practical recommendation of zero leveling at the cross-section of three transthoracic planes, which are, respectively midchest frontal, transverse through the fourth intercostal space, and midsagittal. As for the inevitable respiratory pressure swings, end-expiratory reading at functional residual capacity allows for minimal influence of elastic lung recoil on pulmonary pressure reading. However, hyperventilation is associated with changes in end-expiratory lung volume and increased intrathoracic pressure, eventually exacerbated by expiratory muscle contraction and dynamic hyperinflation, all increasing pulmonary vascular pressures. This problem is amplified in patients with obstructed airways. With the exception of dynamic hyperinflation states, it is reasonable to assume that negative inspiratory and positive expiratory intrathoracic pressures cancel each other out, so averaging pulmonary vascular pressures over several respiratory cycles is most often preferable. This recommendation may be generalized for the purpose of consistency and makes sense, as pulmonary blood flow measurements are not corrected for phasic inspiratory and expiratory changes in clinical practice.SCOPUS: re.jinfo:eu-repo/semantics/publishe

Correlation of fractal dimension with oxygen exchange parameters.

<p>Correlation of 3D fractal dimension with arterio-venous difference in oxygen content (AVDO₂, A), arterial (art SO₂, B) and venous (ven SO₂, C) oxygen saturation (R =  linear correlation coefficient, ρ =  Spearman correlation coefficient, * p<0.05, ns - not significant).</p

Correlation of distance metric with patient clinical parameters.

<p>Correlation of distance metric with (A) mean pulmonary arterial pressure (mPAP), and (B) pulmonary vascular resistance (PVR; R =  linear correlation coefficient, r =  Spearman correlation coefficient, ** p<0.01, *** p<0.001). (C) Receiver-operating curve for DM determining mPAP >25 mmHg (AUC: area under the curve). (D) Distribution of distance metric according to the WHO classification of the patients. (solid lines represent mean and standard error of mean; p value shows significant difference between WHO class II and III).</p

Correlations with clinical parameters (Spearman r and p-value) for n = 24 patients.

<p>mPAP: mean pulmonary arterial pressure, PVR: pulmonary vascular resistance, AVDO₂: arterial-venous difference in oxygen content, art SO₂: arterial oxygen saturation, ven SO₂: venous oxygen saturation, BSA: body surface area after Dubois and Dubois.</p

Flowchart of the automatic vessel extraction algorithm.

<p>(top) Sample CT image, (2^nd row) lung, airway segmentation and the vessel enhancement filter response superimposed on the CT image, (3^rd row) vessel enhancement filter response restricted to the region of interest, (bottom row, left) connected centerlines, (bottom row, right) 3D rendering of the lung vessel centerlines. Inset shows the computation of distance metric (DM). The sum of distances along the 3D points of the vessel is divided by the length of the straight path between the two endpoints (first and last 3D point of the vessel segment).</p

Patient characteristics.

<p>Data are presented as mean ± SD (range). The significance was tested with t-test.</p><p>PH: pulmonary hypertension, BSA: body surface area after Dubois and Dubois, mPAP: mean pulmonary arterial pressure, PAWP: pulmonary artery wedge pressure, CO: cardiac output, PVR: pulmonary vascular resistance, AVDO₂: arterial-venous difference in oxygen content, art SO₂: arterial oxygen saturation, ven SO₂: venous oxygen saturation, */**/***: significant difference between PH and non-PH patients (p<0.05/0.01/0.001).</p