Lobe-wise assessment of lung volume and density distribution in lung transplant patients and value for early detection of bronchiolitis obliterans syndrome.

PURPOSE: To evaluate quantitative computed tomography (CT) measurements of the lung parenchyma in lung transplant (LTx) patients for early detection of the bronchiolitis obliterans syndrome (BOS). MATERIALS AND METHODS: 359 CT scans of 122 lung transplant patients were evaluated. Measurements of lung volume and density were performed for the whole lung and separately for each lobe. For longitudinal analysis the difference between the baseline at 6 months after LTx and follow-up examinations was calculated. Patients with and without BOS (matched 1:2) were compared at two different time points, the last examination before the BOS onset and the first examination within one year after BOS onset. RESULTS: 30 patients developed BOS during the follow-up period. Longitudinal changes in the lung volume and lung density measured on CT differed significantly between those patients with and without early BOS, in particular the difference of the inspiratory and expiratory lung volume (p < 0.001), the ratio of the expiratory and inspiratory lung volume (p < 0.001-p = 0.001) and MLD (p < 0.001-p = 0.001), the volume on expiration (p < 0.001-p = 0.007), the MLD on expiration (p < 0.001-p = 0.007), and the percentiles on expiration (p < 0.001-p = 0.002) with an increase of lung volume and a decrease of lung density. Changes were pronounced in the lower lobes. Before BOS onset, patients with and without future development of BOS showed no significant differences. CONCLUSION: Longitudinal changes of lung volume and lung density measured on CT start markedly at BOS onset with increased lung volume and decreased lung density indicating increased inflation levels. Even though this method may help to diagnose BOS at onset it is not useful as a predictor for BOS before disease onset.

Fast interactive segmentation of the pulmonary lobes from thoracic computed tomography data.

Automated lung lobe segmentation methods often fail for challenging and clinically relevant cases with incomplete fissures or substantial amounts of pathology. We present a fast and intuitive method to interactively correct a given lung lobe segmentation or to quickly create a lobe segmentation from scratch based on a lung mask. A given lobar boundary is converted into a mesh by principal component analysis of 3D lobar boundary markers to obtain a plane where nodes correspond to the position of the markers. An observer can modify the mesh by drawing on 2D slices in arbitrary orientations. After each drawing, the mesh is immediately adapted in a 3D region around the user interaction. For evaluation we participated in the international lung lobe segmentation challenge LObe and lung analysis 2011 (LOLA11). Two observers applied the method to correct a given lung lobe segmentation obtained by a fully automatic method for all 55 CT scans of LOLA11. On average observer 1/2 required 8 ± 4/25 ± 12 interactions per case and took 1:30 ± 0:34/3:19 ± 1:29 min. The average distances to the reference segmentation were improved from an initial 2.68 ± 14.71 mm to 0.89 ± 1.63/0.74 ± 1.51 mm. In addition, one observer applied the proposed method to create a segmentation from scratch. This took 3:44 ± 0:58 minutes on average per case, applying an average of 20 ± 3 interactions to reach an average distance to the reference of 0.77 ± 1.14 mm. Thus, both the interactive corrections and the creation of a segmentation from scratch were feasible in a short time with excellent results and only little interaction. Since the mesh adaptation is independent of image features, the method can successfully handle patients with severe pathologies, provided that the human operator is capable of correctly indicating the lobar boundaries.

Lung Volume Reduction in Pulmonary Emphysema from the Radiologist's Perspective.

UNLABELLED: Pulmonary emphysema causes decrease in lung function due to irreversible dilatation of intrapulmonary air spaces, which is linked to high morbidity and mortality. Lung volume reduction (LVR) is an invasive therapeutical option for pulmonary emphysema in order to improve ventilation mechanics. LVR can be carried out by lung resection surgery or different minimally invasive endoscopical procedures. All LVR-options require mandatory preinterventional evaluation to detect hyperinflated dysfunctional lung areas as target structures for treatment. Quantitative computed tomography can determine the volume percentage of emphysematous lung and its topographical distribution based on the lung's radiodensity. Modern techniques allow for lobebased quantification that facilitates treatment planning. Clinical tests still play the most important role in post-interventional therapy monitoring, but CT is crucial in the detection of postoperative complications and foreshadows the method's high potential in sophisticated experimental studies. Within the last ten years, LVR with endobronchial valves has become an extensively researched minimally-invasive treatment option. However, this therapy is considerably complicated by the frequent occurrence of functional interlobar shunts. The presence of "collateral ventilation" has to be ruled out prior to valve implantations, as the presence of these extraanatomical connections between different lobes may jeopardize the success of therapy. Recent experimental studies evaluated the automatic detection of incomplete lobar fissures from CT scans, because they are considered to be a predictor for the existence of shunts. To date, these methods are yet to show acceptable results. KEY POINTS: Today, surgical and various minimal invasive methods of lung volume reduction are in use. Radiological and nuclear medical examinations are helpful in the evaluation of an appropriate lung area. Imaging can detect periinterventional complications. Reduction of lung volume has not yet been conclusively proven to be effective and is a therapeutical option with little scientific evidence.

Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans.

The malignancy of lung nodules is most often detected by analyzing changes of the nodule diameter in follow-up scans. A recent study showed that comparing the volume or the mass of a nodule over time is much more significant than comparing the diameter. Since the survival rate is higher when the disease is still in an early stage it is important to detect the growth rate as soon as possible. However manual segmentation of a volume is time-consuming. Whereas there are several well evaluated methods for the segmentation of solid nodules, less work is done on subsolid nodules which actually show a higher malignancy rate than solid nodules. In this work we present a fast, semi-automatic method for segmentation of subsolid nodules. As minimal user interaction the method expects a user-drawn stroke on the largest diameter of the nodule. First, a threshold-based region growing is performed based on intensity analysis of the nodule region and surrounding parenchyma. In the next step the chest wall is removed by a combination of a connected component analyses and convex hull calculation. Finally, attached vessels are detached by morphological operations. The method was evaluated on all nodules of the publicly available LIDC/IDRI database that were manually segmented and rated as non-solid or part-solid by four radiologists (Dataset 1) and three radiologists (Dataset 2). For these 59 nodules the Jaccard index for the agreement of the proposed method with the manual reference segmentations was 0.52/0.50 (Dataset 1/Dataset 2) compared to an inter-observer agreement of the manual segmentations of 0.54/0.58 (Dataset 1/Dataset 2). Furthermore, the inter-observer agreement using the proposed method (i.e. different input strokes) was analyzed and gave a Jaccard index of 0.74/0.74 (Dataset 1/Dataset 2). The presented method provides satisfactory segmentation results with minimal observer effort in minimal time and can reduce the inter-observer variability for segmentation of subsolid nodules in clinical routine.

Pulmonary lymphangioleiomyomatosis: analysis of disease manifestation by region-based quantification of lung parenchyma.

PURPOSE: Lymphangioleiomyomatosis (LAM) is characterized by proliferation of smooth muscle tissue that causes bronchial obstruction and secondary cystic destruction of lung parenchyma. The aim of this study was to evaluate the typical distribution of cystic defects in LAM with quantitative volumetric chest computed tomography (CT). MATERIALS AND METHODS: CT examinations of 20 patients with confirmed LAM were evaluated with region-based quantification of lung parenchyma. Additionally, 10 consecutive patients were identified who had recently undergone CT imaging of the lung at our institution, in which no pathologies of the lung were found, to serve as a control group. Each lung was divided into three regions (upper, middle and lower thirds) with identical number of slices. In addition, we defined a "peel" and "core" of the lung comprising the 2 cm subpleural space and the remaining inner lung area. Computerized detection of lung volume and relative emphysema was performed with the PULMO 3D software (v3.42, Fraunhofer MEVIS, Bremen, Germany). This software package enables the quantification of emphysematous lung parenchyma by calculating the pixel index, which is defined as the ratio of lung voxels with a density <-950HU to the total number of voxels in the lung. RESULTS: Cystic changes accounted for 0.1-39.1% of the total lung volume in patients with LAM. Disease manifestation in the central lung was significantly higher than in peripheral areas (peel median: 15.1%, core median: 20.5%; p=0.001). Lower thirds of lung parenchyma showed significantly less cystic changes than upper and middle lung areas combined (lower third: median 13.4, upper and middle thirds: median 19.0, p=0.001). CONCLUSION: The distribution of cystic lesions in LAM is significantly more pronounced in the central lung compared to peripheral areas. There is a significant predominance of cystic changes in apical and intermediate lung zones compared to the lung bases.

Sex differences in emphysema phenotype in smokers without airflow obstruction.

Data on sex differences in emphysema are limited to chronic obstructive pulmonary disease. We aimed to verify whether such differences also exist in smokers without airflow obstruction, weighting their influence on the relationship between emphysema and clinical features. We evaluated both clinical and multidetector computed tomography (MDCT) data of 1,011 heavy smokers recruited by a lung cancer screening project. MDCT scans were analysed with software allowing lobar quantification of emphysema features. For these measures, multiple regression models were applied to assess the effect of patients sex, after allowance for age, body mass index (BMI), smoking history, forced expiratory volume in 1 s (FEV(1)) and forced vital capacity. The final study cohort consisted of 957 smokers without airflow obstruction. Compared with males, females exhibited an emphysema phenotype less extensive in each pulmonary lobe, characterised by smaller emphysematous areas and less concentrated in the core of the lung. However, in females, the increase of emphysema with age was more pronounced and displayed a more significant relationship with FEV(1)% decline; conversely, in males there was a stronger association with the decrease in BMI. Males and females respond differently to the type and location of lung damage due to tobacco exposure. In smokers, sex influences the relationship between emphysema and clinical features.

[Generating statements at whole-body imaging with a workflow-optimized software tool--first experiences with multireader analysis]. / Befunderstellung bei der Ganzkörperbildgebung mittels einer workflowoptimierten Befundungssoftware--Erste Erfahrungen einer Multireader-Analyse.

INTRODUCTION: Due to technical innovations in sectional diagram methods, whole-body imaging has increased in importance for clinical radiology, particularly for the diagnosis of systemic tumor disease. Large numbers of images have to be evaluated in increasingly shorter time periods. The aim was to create and evaluate a new software tool to assist and automate the process of diagnosing whole-body datasets. MATERIAL AND METHODS: Thirteen whole-body datasets were evaluated by 3 readers using the conventional system and the new software tool. The times for loading the datasets, examining 5 different regions (head, neck, thorax, abdomen and pelvis/skeletal system) and retrieving a relevant finding for demonstration were acquired. Additionally a Student T-Test was performed. For qualitative analysis the 3 readers used a scale from 0 - 4 (0 = bad, 4 = very good) to assess dataset loading convenience, lesion location assistance, and ease of use. Additionally a kappa value was calculated. RESULTS: The average loading time was 39.7 s (+/- 5.5) with the conventional system and 6.5 s (+/- 1.4) (p < 0.01) with the new software tool. For the different regions (conventional system/new software tool), the time reduction for readers 1, 2, and 3 were as follows: in the head region 35.9 % (p < 0.01)/49.9 % (p < 0.01)/54.3 % (p < 0,01), in the neck region 48.5 % (p < 0.01)/52.6 % (p < 0.01)/59.4 % (p < 0.05), in the thorax region 59.1 % (p < 0.01)/56.2 % (p < 0.05)/62.1 % (p < 0.05), in the abdominal region 61.9 % (p < 0.01)/62.7 % (p < 0.05)/47.9 % (p < 0.01) and in the pelvis region 73.1 % (p < 0.01)/63.7 % (p < 0.05)/55 % (p < 0.01), respectively. 148.2 s (+/- 94.8) compared to 2.5 s (+/- 0.5) were required to retrieve a previously described finding (p < 0.01). With and without the new software tool the same number of metastases was found (p < 0.01, k > 0.9). The qualitative analysis showed a significant advantage with respect to convenience (p < 0.01, k > 0.9). CONCLUSION: Use of the new software can achieve a significant time savings when working with whole-body datasets with a constant quality of findings and a significant advantage with respect to convenience. As a result, the problem of evaluating examinations with thousands of images can be approached systematically.

Interobserver-variability of lung nodule volumetry considering different segmentation algorithms and observer training levels.

OBJECTIVE: The aim of this study was to investigate the interobserver variability of CT based diameter and volumetric measurements of artificial pulmonary nodules. A special interest was the consideration of different measurement methods, observer experience and training levels. MATERIALS AND METHODS: For this purpose 46 artificial small solid nodules were examined in a dedicated ex-vivo chest phantom with multislice-spiral CT (20 mAs, 120 kV, collimation 16 mm x 0.75 mm, table feed 15 mm, reconstructed slice thickness 1mm, reconstruction increment 0.7 mm, intermediate reconstruction kernel). Two observer groups of different radiologic experience (0 and more than 5 years of training, 3 observers each) analysed all lesions with digital callipers and 2 volumetry software packages (click-point depending and robust volumetry) in a semi-automatic and manually corrected mode. For data analysis the variation coefficient (VC) was calculated in per cent for each group and a Wilcoxon test was used for analytic statistics. RESULTS: Click-point robust volumetry showed with a VC of <0.01% in both groups the smallest interobserver variability. Between experienced and un-experienced observers interobserver variability was significantly different for diameter measurements (p=0.023) but not for semi-automatic and manual corrected volumetry. A significant training effect was revealed for diameter measurements (p=0.003) and semi-automatic measurements of click-point depending volumetry (p=0.007) in the un-experienced observer group. CONCLUSIONS: Compared to diameter measurements volumetry achieves a significantly smaller interobserver variance and advanced volumetry algorithms are independent of observer experience.

[Quantification of pulmonary emphysema in multislice-CT using different software tools]. / Quantifizierung des Lungenemphysems in der Mehrschicht-CT mittels verschiedener Softwareverfahren.

PURPOSE: The data records of thin-section MSCT of the lung with approx. 300 images are difficult to use in manual evaluation. A computer-assisted pre-diagnosis can help with reporting. Furthermore, post-processing techniques, for instance, for quantification of emphysema on the basis of three-dimensional anatomical information might be improved and the workflow might be further automated. MATERIALS AND METHODS: The results of 4 programs (Pulmo, Volume, YACTA and PulmoFUNC) for the quantitative analysis of emphysema (lung and emphysema volume, mean lung density and emphysema index) of 30 consecutive thin-section MSCT datasets with different emphysema severity levels were compared. The classification result of the YACTA program for different types of emphysema was also analyzed. RESULTS: Pulmo and Volume have a median operating time of 105 and 59 minutes respectively due to the necessity for extensive manual correction of the lung segmentation. The programs PulmoFUNC and YACTA, which are automated to a large extent, have a median runtime of 26 and 16 minutes, respectively. The evaluation with Pulmo and Volume using 2 different datasets resulted in implausible values. PulmoFUNC crashed with 2 other datasets in a reproducible manner. Only with YACTA could all graphic datasets be evaluated. The lung volume, emphysema volume, emphysema index and mean lung density determined by YACTA and PulmoFUNC are significantly larger than the corresponding values of Volume and Pulmo (differences: Volume: 119 cm(3)/65 cm(3)/1 %/17 HU, Pulmo: 60 cm(3)/96 cm(3)/1 %/37 HU). Classification of the emphysema type was in agreement with that of the radiologist in 26 panlobular cases, in 22 paraseptalen cases and in 15 centrilobular emphysema cases. CONCLUSION: The substantial expenditure of time obstructs the employment of quantitative emphysema analysis in the clinical routine. The results of YACTA and PulmoFUNC are affected by the dedicated exclusion of the tracheobronchial system. These fully automatic tools enable not only fast quantification without manual interaction, but also a reproducible measurement without user dependence.

[CT-based software-supported prediction of the postoperative lung function after partial resection of the lung]. / CT-basierte softwaregestützte Vorhersage der postoperativen Lungenfunktion nach Lungenteilresektion.

PURPOSE: The predicted postoperative forced exspiratory volume in one second (FEV (1)) is an important functional factor for predicting the operability of patients with bronchial carcinoma. A software tool that uses a preoperative chest MSCT and pulmonary function test (PFT) for largely automated prediction of the FEV (1) was evaluated. MATERIALS AND METHODS: Fifteen patients with surgically treated lung cancer were examined with a preoperative chest MSCT (1.25 mm slice thickness, 0.8 mm reconstruction increment) and PFT before and after surgery. CT scans were analyzed by the prototype software MeVisPulmo (MeVis gGmbH, Bremen) that predicted the postoperative FEV (1) as a percentage of the preoperative values measured by PFT. The automated segmentation and volumetry of lung lobes were performed either without or with minimal user interaction. Patients underwent lobectomy in twelve cases (6 upper lobes, 1 middle lobe, 5 lower lobes) and pneumectomy in three cases. The predicted FEV (1) values were compared to the observed postoperative values as a standard of reference. The additional functional parameters "total lung capacity" (TLC) and "forced vital capacity" (FVC) were compared to the FEV (1) results. RESULTS: Automated calculation of predicted postoperative lung function was successful in all cases. Due to an implausible PFT, two of the 15 patients were excluded from the collective. A mean postoperative FEV (1) value of 75 % (SD +/- 12 %) of the preoperative FEV (1) was calculated and 74 % (SD +/- 12 %) was actually measured. The deviations of the predicted value from the measured postoperative FEV (1) ranged between - 289 ml (-12 % of the measured postoperative FEV (1)) and + 294 ml (+ 15 % of the postoperative FEV (1)). The mean deviation (absolute value) was 137 +/- 77 ml/s. This corresponds to 7 +/- 3 % of the measured postoperative FEV (1). Bland-Altman-Statistics showed the 95 % "limits of agreement" for the predicted FEV (1) values to be between - 341 ml and + 301 ml, corresponding to - 17.5 % and + 15.8 of the measured postoperative FEV (1) value. Analysis of the TLC and FVC yielded similar results. CONCLUSION: In the present pilot study the software-assisted prediction of the postoperative FEV (1) using a preoperative MSCT and pulmonary function test corresponded satisfactorily with the observed postoperative values. The introduced approach may make it possible to obtain additional information for the prediction of functional operability prior to performing lung cancer surgery.