Quantitative Emphysema Distribution in Anatomic and Non-anatomic Lung Regions.

PURPOSE: The change of emphysema distribution with increasing COPD severity is not yet assessed. Especially, involvement of the upper aspect of the lower lobe is unknown. The primary aim was to quantitatively determine regional distribution of emphysema in anatomically (lung lobes) and non-anatomically defined lung regions (upper/lower lung halves as well as core and rind regions) in a cohort covering equally all COPD severity stages using CT. MATERIAL AND METHODS: Basically 100 CT data sets were quantitatively evaluated for regional distribution of emphysema. Emphysema characteristics (emphysema index, mean lung density and 15th percentile of the attenuation values of lung voxels) were compared (t-test) in: upper lobes vs. upper halves, lower lobes vs. lower halves, core vs. rind region. RESULTS: In patients with ≤ GOLD II, a significantly higher emphysema burden was found in the upper lobes as compared to upper halves. In subjects with GOLD III/IV the differences were not significant for all emphysema characteristics. A high difference between lobes and halves in subjects with ≤ GOLD II was found, in contrast to low difference in higher GOLD stages. CONCLUSIONS: Lobar segmentation provides improved characterization of cranio-caudal emphysema distribution compared to a non-anatomic approach in subjects up to GOLD stage II.

PET-based delineation of tumour volumes in lung cancer: comparison with pathological findings.

PURPOSE: The objective of the study was to validate an adaptive, contrast-oriented thresholding algorithm (COA) for tumour delineation in (18)F-fluorodeoxyglucose (FDG) positron emission tomography (PET) for non-small cell lung cancer (NSCLC) in comparison with pathological findings. The impact of tumour localization, tumour size and uptake heterogeneity on PET delineation results was also investigated. METHODS: PET tumour delineation by COA was compared with both CT delineation and pathological findings in 15 patients to investigate its validity. Correlations between anatomical volume, metabolic volume and the pathology reference as well as between the corresponding maximal diameters were determined. Differences between PET delineations and pathological results were investigated with respect to tumour localization and uptake heterogeneity. RESULTS: The delineated volumes and maximal diameters measured on PET and CT images significantly correlated with the pathology reference (both r > 0.95, p < 0.0001). Both PET and CT contours resulted in overestimation of the pathological volume (PET 32.5 ± 26.5%, CT 46.6 ± 27.4%). CT volumes were larger than those delineated on PET images (CT 60.6 ± 86.3 ml, PET 48.3 ± 61.7 ml). Maximal tumour diameters were similar for PET and CT (51.4 ± 19.8 mm for CT versus 53.4 ± 19.1 mm for PET), slightly overestimating the pathological reference (mean difference CT 4.3 ± 3.2 mm, PET 6.2 ± 5.1 mm). PET volumes of lung tumours located in the lower lobe were significantly different from those determined from pathology (p = 0.037), whereas no significant differences were observed for tumours located in the upper lobe (p = 0.066). Only minor correlation was found between pathological tumour size and PET heterogeneity (r = -0.24). CONCLUSION: PET tumour delineation by COA showed a good correlation with pathological findings. Tumour localization had an influence on PET delineation results. The impact of tracer uptake heterogeneity on PET delineation should be considered carefully and individually in each patient. Altogether, PET tumour delineation by COA for NSCLC patients is feasible and reliable with the potential for routine clinical application.

Computer-aided segmentation and volumetry of artificial ground-glass nodules at chest CT.

OBJECTIVE: The purpose of this study was to investigate a new software program for semiautomatic measurement of the volume and mass of ground-glass nodules (GGNs) in a chest phantom and to investigate the influence of CT scanner, reconstruction filter, tube voltage, and tube current. MATERIALS AND METHODS: We used an anthropomorphic chest phantom with eight artificial GGNs with two different CT attenuations and four different volumes. CT scans were obtained with four models of CT scanner at 120 kVp and 25 mAs with a soft and a sharp reconstruction filter. On the 256-MDCT scanner, the tube current-exposure time product and tube voltage settings were varied. GGNs were measured with software that automatically segmented the nodules. Absolute percentage error (APE) was calculated for volume, mass, and density. Wilcoxon signed rank, Mann-Whitney U, and Kruskal-Wallis tests were used for analysis. RESULTS: Volume and mass did not differ significantly from the true values. When measurements were expressed as APE, the error range was 2-36% for volume and 5-46% for mass, which was significantly different from no error. We did not find significant differences in APE between CT scanners with filters for lower tube current for volume or lower tube voltage for mass. CONCLUSION: Computer-aided segmentation and mass and volume measurements of GGNs with the prototype software had promising results in this study.

Relationship and prognostic value of modified coronary artery calcium score, FEV1, and emphysema in lung cancer screening population: the MILD trial.

PURPOSE: To assess the relationship between a modified coronary artery calcium (mCAC) score and both forced expiratory volume in 1 second (FEV1) and pulmonary emphysema and the associations of such factors with all-cause mortality and cardiovascular events (CVEs) in a lung cancer computed tomographic (CT) screening trial. MATERIALS AND METHODS: In this institutional review board-approved study, both clinical and low-dose CT data were evaluated in a cohort of heavy smokers consecutively recruited by the Multicentric Italian Lung Detection, or MILD, trial. Low-dose CT images were analyzed by using software that allowed quantification of mCAC, mean lung attenuation (MLA), and total extent of emphysema. The correlations between mCAC, percentage predicted FEV1, MLA, and emphysema extent were tested by using the Pearson correlation coefficient. Adjusted multiple logistic regression models were applied to assess the relationships between mCAC, FEV1, MLA, and emphysema extent and all-cause mortality and CVEs. RESULTS: The final study cohort consisted of 1159 smokers. There were no significant correlations between mCAC score and FEV1 (r=-0.03, P=.4), MLA (r=-0.01, P=.7), or emphysema extent (r=0.02, P=.6). An mCAC score greater than 400 was the only factor that was independently associated with both all-cause mortality (odds ratio [OR]: 3.73; 95% confidence interval [CI]: 1.05, 13.32; P=.04) and CVEs (OR: 2.87; 95% CI: 1.13, 7.27; P=.03). CONCLUSION: mCAC is a better predictor of CVE and all-cause mortality than FEV1 and emphysema extent and may contribute to the identification of high-risk individuals in a lung cancer screening setting.

Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge.

The Cerebral Aneurysm Detection and Analysis (CADA) challenge was organized to support the development and benchmarking of algorithms for detecting, analyzing, and risk assessment of cerebral aneurysms in X-ray rotational angiography (3DRA) images. 109 anonymized 3DRA datasets were provided for training, and 22 additional datasets were used to test the algorithmic solutions. Cerebral aneurysm detection was assessed using the F2 score based on recall and precision, and the fit of the delivered bounding box was assessed using the distance to the aneurysm. The segmentation quality was measured using the Jaccard index and a combination of different surface distance measures. Systematic errors were analyzed using volume correlation and bias. Rupture risk assessment was evaluated using the F2 score. 158 participants from 22 countries registered for the CADA challenge. The U-Net-based detection solutions presented by the community show similar accuracy compared to experts (F2 score 0.92), with a small number of missed aneurysms with diameters smaller than 3.5 mm. In addition, the delineation of these structures, based on U-Net variations, is excellent, with a Jaccard score of 0.92. The rupture risk estimation methods achieved an F2 score of 0.71. The performance of the detection and segmentation solutions is equivalent to that of human experts. The best results are obtained in rupture risk estimation by combining different image-based, morphological, and computational fluid dynamic parameters using machine learning methods. Furthermore, we evaluated the best methods pipeline, from detecting and delineating the vessel dilations to estimating the risk of rupture. The chain of these methods achieves an F2-score of 0.70, which is comparable to applying the risk prediction to the ground-truth delineation (0.71).

Impact of rigid and nonrigid registration on the determination of 18F-FDG PET-based tumour volume and standardized uptake value in patients with lung cancer.

PURPOSE: Assessment of the metabolically active tumour tissue by FDG PET is evolving for use in the diagnosis of non-small-cell lung cancer (NSCLC), in the planning of radiotherapy, and in follow-up and response evaluation. For exact evaluation accurate registration of PET and CT data is required. The registration process is usually based on rigid algorithms; however, nonrigid algorithms are increasingly being used. The influence of the registration method on FDG PET-based standardized uptake value (SUVmax) and metabolic tumour volume (MTV) definition has not yet been evaluated. We compared intra- and interindividual differences in SUV and MTV between rigid- and nonrigid-registered PET and CT acquired during different breathing manoeuvres. METHODS: The study group comprised 28 radiotherapy candidates with histologically proven NSCLC who underwent FDG PET acquisition and three CT acquisitions (expiration - EXP, inspiration - INS, mid-breath-hold - MID). All scans were registered with both a rigid (R) and a nonrigid (NR) procedure resulting in six fused datasets: R-INS, R-EXP, R-MID, NR-INS, NR-EXP and NR-MID. For the delineation of MTVs a contrast-oriented contouring algorithm developed in-house was used. To accelerate the delineation a semiautomatic software prototype was utilized. RESULTS: Tumour mean SUVmax did not differ for R and NR registration (R 17.5 ± 7, NR 17.4 ± 7; p=0.2). The mean MTV was higher by 3 ± 12 ml (p=0.02) in the NR group than in the R group, as was the mean tumour diameter (by 0.1 ± 0.2 cm; p<0.01). With respect to the three different breathing manoeuvres, there were no differences in MTV in the R group (p > 0.7). In intraindividual comparison there were no significant differences in MTVs concerning the registration pairs R-EXP (68 ± 88 ml) vs. NR-EXP (69 ± 85 ml) und R-MID (68 ± 86 ml) vs. NR-MID (69 ± 83 ml) (both p > 0.4). However, the MTVs were larger after NR registration during inspiration (R-INS 68 ± 82 vs. NR-INS 78 ± 93 ml; p=0.02). CONCLUSION: The use of nonrigid algorithms may lead to a change in MTV, whose extent is influenced by the breathing manoeuvre on CT. Nonrigid registration methods cannot be recommended for the definition of MTV if the CT scan is performed during inspiration. The choice of registration algorithm has no significant impact on SUVmax.

The International Radiomics Platform - An Initiative of the German and Austrian Radiological Societies - First Application Examples. / Die Internationale Radiomics-Plattform ­ eine Initiative der Deutschen und Österreichischen Röntgengesellschaften ­ Erste Anwendungsbeispiele.

PURPOSE: The DRG-ÖRG IRP (Deutsche Röntgengesellschaft-Österreichische Röntgengesellschaft international radiomics platform) represents a web-/cloud-based radiomics platform based on a public-private partnership. It offers the possibility of data sharing, annotation, validation and certification in the field of artificial intelligence, radiomics analysis, and integrated diagnostics. In a first proof-of-concept study, automated myocardial segmentation and automated myocardial late gadolinum enhancement (LGE) detection using radiomic image features will be evaluated for myocarditis data sets. MATERIALS AND METHODS: The DRG-ÖRP IRP can be used to create quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis and is characterized by the following performance criteria: Possibility of using multicentric networked data, automatically calculated quality parameters, processing of annotation tasks, contour recognition using conventional and artificial intelligence methods and the possibility of targeted integration of algorithms. In a first study, a neural network pre-trained using cardiac CINE data sets was evaluated for segmentation of PSIR data sets. In a second step, radiomic features were applied for segmental detection of LGE of the same data sets, which were provided multicenter via the IRP. RESULTS: First results show the advantages (data transparency, reliability, broad involvement of all members, continuous evolution as well as validation and certification) of this platform-based approach. In the proof-of-concept study, the neural network demonstrated a Dice coefficient of 0.813 compared to the expert's segmentation of the myocardium. In the segment-based myocardial LGE detection, the AUC was 0.73 and 0.79 after exclusion of segments with uncertain annotation.The evaluation and provision of the data takes place at the IRP, taking into account the FAT (fairness, accountability, transparency) and FAIR (findable, accessible, interoperable, reusable) criteria. CONCLUSION: It could be shown that the DRG-ÖRP IRP can be used as a crystallization point for the generation of further individual and joint projects. The execution of quantitative analyses with artificial intelligence methods is greatly facilitated by the platform approach of the DRG-ÖRP IRP, since pre-trained neural networks can be integrated and scientific groups can be networked.In a first proof-of-concept study on automated segmentation of the myocardium and automated myocardial LGE detection, these advantages were successfully applied.Our study shows that with the DRG-ÖRP IRP, strategic goals can be implemented in an interdisciplinary way, that concrete proof-of-concept examples can be demonstrated, and that a large number of individual and joint projects can be realized in a participatory way involving all groups. KEY POINTS: · The DRG-ÖRG IRP is a web/cloud-based radiomics platform based on a public-private partnership.. · The DRG-ÖRG IRP can be used for the creation of quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis.. · First results show the applicability of left ventricular myocardial segmentation using a neural network and segment-based LGE detection using radiomic image features.. · The DRG-ÖRG IRP offers the possibility of integrating pre-trained neural networks and networking of scientific groups.. CITATION FORMAT: · Overhoff D, Kohlmann P, Frydrychowicz A et al. The International Radiomics Platform - An Initiative of the German and Austrian Radiological Societies. Fortschr Röntgenstr 2021; 193: 276 - 287.

Bronchial diverticula in smokers on thin-section CT.

The objective was to determine the prevalence of bronchial diverticula in smokers on thin-section CT and the relationship to clinical and other morphological features on CT. Thin-section CT images of 503 cigarette smokers were assessed for the profusion and location of diverticula in the major airways. The extent of the bronchial diverticula was recorded as follows: grade 0, none; grade 1, one to three diverticula; grade 2, more than three diverticula. The extent of emphysema, bronchial wall thickness, clinical features, and pulmonary function were compared in the sub-groups stratified according to the extent of bronchial diverticula. A total of 229/503 (45.5%) smokers had bronchial diverticula, with 168/503 (33.3%) and 61/503 (12.2%) having grade 1 and 2 bronchial diverticula respectively. Subjects with grade 2 bronchial diverticula were heavier smokers, reported a history of coughing more frequently, and showed more severe functional impairment, greater extent of emphysema and more severe bronchial wall thickening compared with subjects with grade 1 and those individuals without bronchial diverticula (P < 0.05). Multivariate regression analysis revealed that only bronchial wall thickness predicted the extent of the bronchial diverticula (P < 0.0001). Bronchial diverticula are a frequent finding in the major airways of smokers, and they are associated with other markers of smoking-related damage.

Quantitative CT analysis in patients with pulmonary emphysema: is lung function influenced by concomitant unspecific pulmonary fibrosis?

Purpose: Quantitative analysis of CT scans has proven to be a reproducible technique, which might help to understand the pathophysiology of chronic obstructive pulmonary disease (COPD) and combined pulmonary fibrosis and emphysema. The aim of this retrospective study was to find out if the lung function of patients with COPD with Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages III or IV and pulmonary emphysema is measurably influenced by high attenuation areas as a correlate of concomitant unspecific fibrotic changes of lung parenchyma. Patients and methods: Eighty-eight patients with COPD GOLD stage III or IV underwent CT and pulmonary function tests. Quantitative CT analysis was performed to determine low attenuation volume (LAV) and high attenuation volume (HAV), which are considered to be equivalents of fibrotic (HAV) and emphysematous (LAV) changes of lung parenchyma. Both parameters were determined for the whole lung, as well as peripheral and central lung areas only. Multivariate regression analysis was used to correlate HAV with different parameters of lung function. Results: Unlike LAV, HAV did not show significant correlation with parameters of lung function. Even in patients with a relatively high HAV of more than 10%, in contrast to HAV (p=0.786) only LAV showed a significantly negative correlation with forced expiratory volume in 1 second (r=-0.309, R2=0.096, p=0.003). A severe decrease of DLCO% was associated with both larger HAV (p=0.045) and larger LAV (p=0.001). Residual volume and FVC were not influenced by LAV or HAV. Conclusion: In patients with COPD GOLD stage III-IV, emphysematous changes of lung parenchyma seem to have such a strong influence on lung function, which is a possible effect of concomitant unspecific fibrosis is overwhelmed.

Localization and quantification of regional and segmental air trapping in asthma.

OBJECTIVE: To assess the feasibility of volumetric and densitometric software to localize and quantify signs of regional air trapping after methacholine bronchoprovocations in asthma. METHODS: Eight atopic subjects with mild-to-moderate asthma using short-acting beta2-agonists only, with hyperresponsiveness to methacholine, were evaluated. Low-dose baseline expiratory 16-slice multidetector computed tomography scans before and after a methacholine bronchoprovocation were acquired. MeVisPULMO3D software (Bremen, Germany) was applied to the scans, providing quantitative information on volume and density measures of the total lung and each lobe. RESULTS: After methacholine, the expiratory scan showed a median (interquartile range) increase in volume of 534 mL (357-1279 mL), a decrease in lung density (mean and 15th percentile) of 52 Hounsfield Units (HU) (116-39 HU) and 34 HU (78-25 HU), respectively, and an increase in percentage low attenuation areas of 3% (2%-6%) for the total lung, with similar patterns in individual lung lobes. The right and left lower lung lobes showed the largest increases in air trapping, 211 mL (117-363 mL) and 229 mL (155-315 mL), respectively, versus a volume increase of 70 mL (20-249 mL), 26 mL (-16-92 mL), and 91 mL (-28-241 mL) for the right upper, middle, and left upper lobes, respectively. Volume changes in the lower lobes were associated with baseline forced expiratory flow between 25% and 75% of forced vital capacity, whereas low attenuation areas changes in the lower lobes were not. CONCLUSIONS: This study suggests that multidetector computed tomography scans are able to localize and quantify regional air trapping in asthma after methacholine bronchoprovocations. Volumetric measurements of the lobes as compared to densitometric measurements are superior in detecting local air trapping in gravity-dependent areas of the lung.

Comparison of four freely available frameworks for image processing and visualization that use ITK.

Most image processing and visualization applications allow users to configure computation parameters and manipulate the resulting visualizations. SCIRun, VolView, MeVisLab, and the Medical Interaction Toolkit (MITK) are four image processing and visualization frameworks that were built for these purposes. All frameworks are freely available and all allow the use of the ITK C++ library. In this paper, the benefits and limitations of each visualization framework are presented to aid both application developers and users in the decision of which framework may be best to use for their application. The analysis is based on more than 50 evaluation criteria, functionalities, and example applications. We report implementation times for various steps in the creation of a reference application in each of the compared frameworks. The data-flow programming frameworks, SCIRun and MeVisLab, were determined to be best for developing application prototypes, while VolView was advantageous for nonautomatic end-user applications based on existing ITK functionalities, and MITK was preferable for automated end-user applications that might include new ITK classes specifically designed for the application.

Computed tomography quantification of tracheal abnormalities in COPD and their influence on airflow limitation.

PURPOSE: To present a method to automatically quantify tracheal morphology changes during breathing and investigate its contribution to airflow impairment when adding CT measures of emphysema, airway wall thickness, air trapping and ventilation. METHODS: Because tracheal abnormalities often occur localized, a method is presented that automatically determines the most abnormal trachea section based on automatically computed sagittal and coronal lengths. In this most abnormal section, trachea morphology is encoded using four equiangular rays from the center of the trachea and the normalized lengths of these rays are used as features in a classification scheme. Consequently, trachea measurements are used as input for classification into GOLD stages in addition to emphysema, air trapping and ventilation. A database of 200 subjects distributed across all GOLD stages is used to evaluate the classification with a k nearest neighbour algorithm. Performance is assessed in two experimental settings: (a) when only inspiratory scans are taken; (b) when both inspiratory and expiratory scans are available. RESULTS: Given only an inspiratory CT scan, measuring tracheal shape provides complementary information only to emphysema measurements. The best performing set in the inspiratory setting was a combination of emphysema and bronchial measurements. The best performing feature set in the inspiratory-expiratory setting includes measurements of emphysema, ventilation, air trapping, and trachea. Inspiratory and inspiratory-expiratory settings showed similar performance. CONCLUSIONS: The fully automated system presented in this study provides information on trachea shape at inspiratory and expiratory CT. Addition of tracheal morphology features improves the ability of emphysema and air trapping CT-derived measurements to classify COPD patients into GOLD stages and may be relevant when investigating different aspects of COPD.

Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans.

Volumetric growth assessment of pulmonary lesions is crucial to both lung cancer screening and oncological therapy monitoring. While several methods for small pulmonary nodules have previously been presented, the segmentation of larger tumors that appear frequently in oncological patients and are more likely to be complexly interconnected with lung morphology has not yet received much attention. We present a fast, automated segmentation method that is based on morphological processing and is suitable for both small and large lesions. In addition, the proposed approach addresses clinical challenges to volume assessment such as variations in imaging protocol or inspiration state by introducing a method of segmentation-based partial volume analysis (SPVA) that follows on the segmentation procedure. Accuracy and reproducibility studies were performed to evaluate the new algorithms. In vivo interobserver and interscan studies on low-dose data from eight clinical metastasis patients revealed that clinically significant volume change can be detected reliably and with negligible computation time by the presented methods. In addition, phantom studies were conducted. Based on the segmentation performed with the proposed method, the performance of the SPVA volumetry method was compared with the conventional technique on a phantom that was scanned with different dosages and reconstructed with varying parameters. Both systematic and absolute errors were shown to be reduced substantially by the SPVA method. The method was especially successful in accounting for slice thickness and reconstruction kernel variations, where the median error was more than halved in comparison to the conventional approach.

Follow-up of CT-derived airway wall thickness: Correcting for changes in inspiration level improves reliability.

OBJECTIVES: Airway wall thickness (AWT) is affected by changes in lung volume. This study evaluated whether correcting AWT on computed tomography (CT) for differences in inspiration level improves measurement agreement, reliability, and power to detect changes over time. METHODS: Participants of the Dutch-Belgian lung cancer screening trial who underwent 3-month repeat CT for an indeterminate pulmonary nodule were included. AWT on CT was calculated by the square root of the wall area at a theoretical airway with an internal perimeter of 10mm (Pi10). The scan with the highest lung volume was labelled as the reference scan and the scan with the lowest lung volume was labelled as the comparison scan. Pi10 derived from the comparison scan was corrected by multiplying it with the ratio of CT lung volume of the comparison scan to CT lung volume on the reference scan. Agreement of uncorrected and corrected Pi10 was studied with the Bland-Altman method, reliability with intra-class correlation coefficients (ICC), and power to detect changes over time was calculated. RESULTS: 315 male participants were included. Limit of agreement and reliability for Pi10 was -0.61 to 0.57mm (ICC=0.87), which improved to -0.38 to 0.37mm (ICC=0.94) after correction for inspiration level. To detect a 15% change over 3 months, 71 subjects are needed for Pi10 and 26 subjects for Pi10 adjusted for inspiration level. CONCLUSIONS: Correcting Pi10 for differences in inspiration level improves reliability, agreement, and power to detect changes over time.

Algorithm Variability in the Estimation of Lung Nodule Volume From Phantom CT Scans: Results of the QIBA 3A Public Challenge.

RATIONALE AND OBJECTIVES: Quantifying changes in lung tumor volume is important for diagnosis, therapy planning, and evaluation of response to therapy. The aim of this study was to assess the performance of multiple algorithms on a reference data set. The study was organized by the Quantitative Imaging Biomarker Alliance (QIBA). MATERIALS AND METHODS: The study was organized as a public challenge. Computed tomography scans of synthetic lung tumors in an anthropomorphic phantom were acquired by the Food and Drug Administration. Tumors varied in size, shape, and radiodensity. Participants applied their own semi-automated volume estimation algorithms that either did not allow or allowed post-segmentation correction (type 1 or 2, respectively). Statistical analysis of accuracy (percent bias) and precision (repeatability and reproducibility) was conducted across algorithms, as well as across nodule characteristics, slice thickness, and algorithm type. RESULTS: Eighty-four percent of volume measurements of QIBA-compliant tumors were within 15% of the true volume, ranging from 66% to 93% across algorithms, compared to 61% of volume measurements for all tumors (ranging from 37% to 84%). Algorithm type did not affect bias substantially; however, it was an important factor in measurement precision. Algorithm precision was notably better as tumor size increased, worse for irregularly shaped tumors, and on the average better for type 1 algorithms. Over all nodules meeting the QIBA Profile, precision, as measured by the repeatability coefficient, was 9.0% compared to 18.4% overall. CONCLUSION: The results achieved in this study, using a heterogeneous set of measurement algorithms, support QIBA quantitative performance claims in terms of volume measurement repeatability for nodules meeting the QIBA Profile criteria.

Informatics in radiology (infoRAD): new tools for computer assistance in thoracic CT part 2. Therapy monitoring of pulmonary metastases.

Owing to the rapid development of scanner technology, thoracic computed tomography (CT) offers new possibilities but also faces enormous challenges with respect to the quality of computer-assisted diagnosis and therapy planning. In the framework of the Virtual Institute for Computer Assistance in Clinical Radiology cooperative research project, a software application was developed to assist the radiologist in the analysis of thoracic CT data for the purpose of evaluating the response to tumor therapy. The application provides follow-up support for monitoring of tumor therapy by means of volumetric quantification of tumors and temporal registration. In addition, anatomically adequate three-dimensional visualization techniques for convenient examination of large data sets are included. With close cooperation between computer scientists and radiologists, the application was tested and optimized to achieve a high degree of usability. Several clinical studies were carried out, the results of which indicated that the application improves therapy monitoring with respect to accuracy and time required.

Informatics in radiology (infoRAD): new tools for computer assistance in thoracic CT. Part 1. Functional analysis of lungs, lung lobes, and bronchopulmonary segments.

Owing to the rapid development of scanner technology, thoracic computed tomography (CT) offers new possibilities but also faces enormous challenges with respect to the quality of computer-assisted diagnosis and therapy planning. In the framework of the Virtual Institute for Computer Assistance in Clinical Radiology cooperative research project, a prototypical software application was developed to assist the radiologist in functional analysis of thoracic CT data. By identifying the anatomic compartments of the lungs, the software application enables assessment of established functional CT parameters for each individual lung, pulmonary lobe, and pulmonary segment. Such region-based assessment allows a more localized diagnosis of lung diseases such as emphysema and more accurate estimation of regional lung function from CT data. With close cooperation between computer scientists and radiologists, the software application was tested and optimized to achieve a high degree of usability. Several clinical studies were carried out, the results of which indicated that the software application improves quantification in diagnosis, therapy planning, and therapy monitoring with respect to accuracy and time required.

Automatic lung segmentation method for MRI-based lung perfusion studies of patients with chronic obstructive pulmonary disease.

PURPOSE: A novel fully automatic lung segmentation method for magnetic resonance (MR) images of patients with chronic obstructive pulmonary disease (COPD) is presented. The main goal of this work was to ease the tedious and time-consuming task of manual lung segmentation, which is required for region-based volumetric analysis of four-dimensional MR perfusion studies which goes beyond the analysis of small regions of interest. METHODS: The first step in the automatic algorithm is the segmentation of the lungs in morphological MR images with higher spatial resolution than corresponding perfusion MR images. Subsequently, the segmentation mask of the lungs is transferred to the perfusion images via nonlinear registration. Finally, the masks for left and right lungs are subdivided into a user-defined number of partitions. Fourteen patients with two time points resulting in 28 perfusion data sets were available for the preliminary evaluation of the developed methods. RESULTS: Resulting lung segmentation masks are compared with reference segmentations from experienced chest radiologists, as well as with total lung capacity (TLC) acquired by full-body plethysmography. TLC results were available for thirteen patients. The relevance of the presented method is indicated by an evaluation, which shows high correlation between automatically generated lung masks with corresponding ground-truth estimates. CONCLUSION: The evaluation of the developed methods indicates good accuracy and shows that automatically generated lung masks differ from expert segmentations about as much as segmentations from different experts.

Inter-Method Performance Study of Tumor Volumetry Assessment on Computed Tomography Test-Retest Data.

RATIONALE AND OBJECTIVES: Tumor volume change has potential as a biomarker for diagnosis, therapy planning, and treatment response. Precision was evaluated and compared among semiautomated lung tumor volume measurement algorithms from clinical thoracic computed tomography data sets. The results inform approaches and testing requirements for establishing conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Computed Tomography Volumetry Profile. MATERIALS AND METHODS: Industry and academic groups participated in a challenge study. Intra-algorithm repeatability and inter-algorithm reproducibility were estimated. Relative magnitudes of various sources of variability were estimated using a linear mixed effects model. Segmentation boundaries were compared to provide a basis on which to optimize algorithm performance for developers. RESULTS: Intra-algorithm repeatability ranged from 13% (best performing) to 100% (least performing), with most algorithms demonstrating improved repeatability as the tumor size increased. Inter-algorithm reproducibility was determined in three partitions and was found to be 58% for the four best performing groups, 70% for the set of groups meeting repeatability requirements, and 84% when all groups but the least performer were included. The best performing partition performed markedly better on tumors with equivalent diameters greater than 40 mm. Larger tumors benefitted by human editing but smaller tumors did not. One-fifth to one-half of the total variability came from sources independent of the algorithms. Segmentation boundaries differed substantially, not ony in overall volume but also in detail. CONCLUSIONS: Nine of the 12 participating algorithms pass precision requirements similar to what is indicated in the QIBA Profile, with the caveat that the present study was not designed to explicitly evaluate algorithm profile conformance. Change in tumor volume can be measured with confidence to within ±14% using any of these nine algorithms on tumor sizes greater than 10 mm. No partition of the algorithms was able to meet the QIBA requirements for interchangeability down to 10 mm, although the partition comprising best performing algorithms did meet this requirement for a tumor size of greater than approximately 40 mm.