Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets.

Radiomic features are being increasingly studied for clinical applications. We aimed to assess the agreement among radiomic features when computed by several groups by using different software packages under very tightly controlled conditions, which included standardized feature definitions and common image data sets. Ten sites (9 from the NCI's Quantitative Imaging Network] positron emission tomography-computed tomography working group plus one site from outside that group) participated in this project. Nine common quantitative imaging features were selected for comparison including features that describe morphology, intensity, shape, and texture. The common image data sets were: three 3D digital reference objects (DROs) and 10 patient image scans from the Lung Image Database Consortium data set using a specific lesion in each scan. Each object (DRO or lesion) was accompanied by an already-defined volume of interest, from which the features were calculated. Feature values for each object (DRO or lesion) were reported. The coefficient of variation (CV), expressed as a percentage, was calculated across software packages for each feature on each object. Thirteen sets of results were obtained for the DROs and patient data sets. Five of the 9 features showed excellent agreement with CV < 1%; 1 feature had moderate agreement (CV < 10%), and 3 features had larger variations (CV ≥ 10%) even after attempts at harmonization of feature calculations. This work highlights the value of feature definition standardization as well as the need to further clarify definitions for some features.

Variability in CT lung-nodule quantification: Effects of dose reduction and reconstruction methods on density and texture based features.

PURPOSE: To investigate the effects of dose level and reconstruction method on density and texture based features computed from CT lung nodules. METHODS: This study had two major components. In the first component, a uniform water phantom was scanned at three dose levels and images were reconstructed using four conventional filtered backprojection (FBP) and four iterative reconstruction (IR) methods for a total of 24 different combinations of acquisition and reconstruction conditions. In the second component, raw projection (sinogram) data were obtained for 33 lung nodules from patients scanned as a part of their clinical practice, where low dose acquisitions were simulated by adding noise to sinograms acquired at clinical dose levels (a total of four dose levels) and reconstructed using one FBP kernel and two IR kernels for a total of 12 conditions. For the water phantom, spherical regions of interest (ROIs) were created at multiple locations within the water phantom on one reference image obtained at a reference condition. For the lung nodule cases, the ROI of each nodule was contoured semiautomatically (with manual editing) from images obtained at a reference condition. All ROIs were applied to their corresponding images reconstructed at different conditions. For 17 of the nodule cases, repeat contours were performed to assess repeatability. Histogram (eight features) and gray level co-occurrence matrix (GLCM) based texture features (34 features) were computed for all ROIs. For the lung nodule cases, the reference condition was selected to be 100% of clinical dose with FBP reconstruction using the B45f kernel; feature values calculated from other conditions were compared to this reference condition. A measure was introduced, which the authors refer to as Q, to assess the stability of features across different conditions, which is defined as the ratio of reproducibility (across conditions) to repeatability (across repeat contours) of each feature. RESULTS: The water phantom results demonstrated substantial variability among feature values calculated across conditions, with the exception of histogram mean. Features calculated from lung nodules demonstrated similar results with histogram mean as the most robust feature (Q ≤ 1), having a mean and standard deviation Q of 0.37 and 0.22, respectively. Surprisingly, histogram standard deviation and variance features were also quite robust. Some GLCM features were also quite robust across conditions, namely, diff. variance, sum variance, sum average, variance, and mean. Except for histogram mean, all features have a Q of larger than one in at least one of the 3% dose level conditions. CONCLUSIONS: As expected, the histogram mean is the most robust feature in their study. The effects of acquisition and reconstruction conditions on GLCM features vary widely, though trending toward features involving summation of product between intensities and probabilities being more robust, barring a few exceptions. Overall, care should be taken into account for variation in density and texture features if a variety of dose and reconstruction conditions are used for the quantification of lung nodules in CT, otherwise changes in quantification results may be more reflective of changes due to acquisition and reconstruction conditions than in the nodule itself.

Varying kVp as a means of reducing CT breast dose to pediatric patients.

We investigated the possibility of reducing radiation dose to the breast tissue of pediatric females by using multiple tube voltages within a single CT examination. The peak kilovoltage (kVp) was adjusted when the x-ray beam was directly exposing the representative breast tissue of a 5-year-old, 10-year-old, and an adult female anthropomorphic phantom; this strategy was called kVp splitting and was emulated by using a different kVp over the anterior and posterior tube angles. Dose savings from kVp splitting were calculated relative to using a fixed kVp over all tube angles and the results indicated savings in all three phantoms when using 80 kVp over the posterior tube angles regardless of the anterior kVp. Monte Carlo (MC) simulations with and without kVp splitting were performed to estimate absorbed breast dose in voxelized models constructed from the CT images of pediatric female patients; 80 kVp was used over the posterior tube angles. The MC simulations revealed breast dose savings of between 9.8% and 33% from using kVp splitting compared to simulations using a fixed kVp protocol with the anterior technique. Before this strategy could be implemented clinically, the development of suitable image reconstruction algorithms and the image quality of scans with kVp splitting would need further study.

MO-D-BRA-01: Limits of Dose Reduction in CT: Where are They and How Will We Know When We Get There?

Radiation Dose continues to be a concern with respect to all diagnostic imaging using ionizing radiation, but especially so with CT imaging. We have always known how to reduce radiation dose in CT - for example, simply turning down the system output (e.g. reduce mAs). What we have not been able to do is to simultaneously reduce dose and maintain "diagnostic image quality". Many recent technical developments have appeared, and will continue to appear, that will allow users to reduce radiation dose in CT while "maintaining image quality". However, this last term is ill-defined and current metrics of image quality are not very applicable to actual clinical practice. The purpose of this symposium is to: (a) describe several current and possible future radiation dose reduction methods and the magnitude of their potential for dose reduction, (b) some description of what "diagnostic image quality" means, the effects that dose reductions methods have on this property, description of some metrics that may help us assess this property quantitatively and this information can be used to guide how low radiation doses can be reduced. LEARNING OBJECTIVES: 1. Understand both conventional and emerging radiation dose reduction methods in CT. 2. Understand the implications on diagnostic image quality for each radiation dose reduction method. 3. Understand some of the issues in evaluating how much radiation dose can be reduced and still accomplish a diagnostic imaging task.

MO-A-BRA-01: State of the Art in Quantitative Imaging in CT, PET and MRI.

Diagnostic Imaging is evolving from a modality where the emphasis is on the acquisition and interpretation of image data by radiologists to one where imaging devices may be used as measurement devices that are able to produce quantitative results. Some examples of quantitative measured values are already in clinical practice, including coronary artery calcium scores from CT, Standard Uptake Values (SUV) in PET imaging and Diffusion Weighted Imaging (DWI) in MRI. Clinical and clinical research applications of quantitative anatomical and functional imaging biomarkers, including those focused on treatment assessment, have continued to dramatically expand. Studies at single centers have clearly demonstrated the potential of such applications. However, sources of bias and variance of quantitative imaging biomarkers have not previously been adequately investigated, thus limiting the implementation of robust methods to mitigate their effects. Therefore, when it comes to applications of such techniques across vendor platforms, centers, and time, challenges arise due to lack of standards, appropriate phantoms, and protocols. During the past few years, several quantitative imaging initiatives have been instigated. This symposium presentation will review selected applications of quantitative imaging biomarkers, illustrate some of the current challenges in broadening the use of such biomarkers, and discuss some of the current initiatives of various scientific and federal organizations that are focused on the standardization, qualification, and validation of quantitative imaging biomarkers. LEARNING OBJECTIVES: 1. Understand selected applications of quantitative imaging biomarkers. 2. Understand the factors that currently limit widespread acceptance and use of such quantitative imaging biomarkers, including sources of bias and variance. 3. Understand some of the current initiatives focused on the standardization, qualification, and validation of selected quantitative imaging biomarkers. LEARNING OBJECTIVES: 1. Understand selected applications of quantitative imaging biomarkers. 2. Understand the factors that currently limit widespread acceptance and use of such quantitative imaging biomarkers, including sources of bias and variance. 3. Understand some of the current initiatives focused on the standardization, qualification, and validation of selected quantitative imaging biomarkers.

Data sets for the qualification of volumetric CT as a quantitative imaging biomarker in lung cancer.

The drug development industry is faced with increasing costs and decreasing success rates. New ways to understand biology as well as the increasing interest in personalized treatments for smaller patient segments requires new capabilities for the rapid assessment of treatment responses. Deployment of qualified imaging biomarkers lags apparent technology capabilities. The lack of consensus methods and qualification evidence needed for large-scale multi-center trials, as well as the standardization that allows them, are widely acknowledged to be the limiting factors. The current fragmentation in imaging vendor offerings, coupled with the independent activities of individual biopharmaceutical companies and their contract research organizations (CROs), may stand in the way of the greater opportunity were these efforts to be drawn together. A preliminary report, "Volumetric CT: a potential biomarker of response," of the Quantitative Imaging Biomarkers Alliance (QIBA) activity was presented at the Medical Imaging Continuum: Path Forward for Advancing the Uses of Medical Imaging in the Development of New Biopharmaceutical Products meeting of the Extended Pharmaceutical Research and Manufacturers of America (PhRMA) Imaging Group sponsored by the Drug Information Agency (DIA) in October 2008. The clinical context in Lung Cancer and a methodology for approaching the qualification of volumetric CT as a biomarker has since been reported [Acad. Radiol. 17, 100-106, 107-115 (2010)]. This report reviews the effort to collect and utilize publicly available data sets to provide a transparent environment in which to pursue the qualification activities in such a way as to allow independent peer review and verification of results. This article focuses specifically on our role as stewards of image sets for developing new tools.

Computed tomography dose assessment for a 160 mm wide, 320 detector row, cone beam CT scanner.

Computed tomography (CT) dosimetry should be adapted to the rapid developments in CT technology. Recently a 160 mm wide, 320 detector row, cone beam CT scanner that challenges the existing Computed Tomography Dose Index (CTDI) dosimetry paradigm was introduced. The purpose of this study was to assess dosimetric characteristics of this cone beam scanner, to study the appropriateness of existing CT dose metrics and to suggest a pragmatic approach for CT dosimetry for cone beam scanners. Dose measurements with a small Farmer-type ionization chamber and with 100 mm and 300 mm long pencil ionization chambers were performed free in air to characterize the cone beam. According to the most common dose metric in CT, namely CTDI, measurements were also performed in 150 mm and 350 mm long CT head and CT body dose phantoms with 100 mm and 300 mm long pencil ionization chambers, respectively. To explore effects that cannot be measured with ionization chambers, Monte Carlo (MC) simulations of the dose distribution in 150 mm, 350 mm and 700 mm long CT head and CT body phantoms were performed. To overcome inconsistencies in the definition of CTDI100 for the 160 mm wide cone beam CT scanner, doses were also expressed as the average absorbed dose within the pencil chamber (D100). Measurements free in air revealed excellent correspondence between CTDI300air and D100air, while CTDI100air substantially underestimates CTDI300air. Results of measurements in CT dose phantoms and corresponding MC simulations at centre and peripheral positions were weighted and revealed good agreement between CTDI300w, D100w and CTDI600w, while CTDI100w substantially underestimates CTDI300w. D100w provides a pragmatic metric for characterizing the dose of the 160 mm wide cone beam CT scanner. This quantity can be measured with the widely available 100 mm pencil ionization chamber within 150 mm long CT dose phantoms. CTDI300w measured in 350 mm long CT dose phantoms serves as an appropriate standard of reference for characterizing the dose of this CT scanner. A CT dose descriptor that is based on an integration length smaller than the actual beam width is preferably expressed as an (average) dose, such as D100 for the 160 mm wide cone beam CT scanner, and not as CTDI100.

The Reference Image Database to Evaluate Response to therapy in lung cancer (RIDER) project: a resource for the development of change-analysis software.

Critical to the clinical evaluation of effective novel therapies for lung cancer is the early and accurate determination of tumor response, which requires an understanding of the sources of uncertainty in tumor measurement and subsequent attempts to minimize their effects on the assessment of the therapeutic agent. The Reference Image Database to Evaluate Response (RIDER) project seeks to develop a consensus approach to the optimization and benchmarking of software tools for the assessment of tumor response to therapy and to provide a publicly available database of serial images acquired during lung cancer drug and radiation therapy trials. Images of phantoms and patient images acquired under situations in which tumor size or biology is known to be unchanged also will be provided. The RIDER project will create standardized methods for benchmarking software tools to reduce sources of uncertainty in vital clinical assessments such as whether a specific tumor is responding to therapy.

Application of the noise power spectrum in modern diagnostic MDCT: part I. Measurement of noise power spectra and noise equivalent quanta.

Dose reduction efforts in diagnostic CT have brought the tradeoff of dose versus image quality to the forefront. The need for meaningful characterization of image noise beyond that offered by pixel standard deviation is becoming increasingly important. This work aims to study the implementation of the noise power spectrum (NPS) and noise equivalent quanta (NEQ) on modern, multislice diagnostic CT scanners. The details of NPS and NEQ measurement are outlined and special attention is paid to issues unique to multislice CT. Aliasing, filter design and effects of acquisition geometry are investigated. While it was found that both metrics can be implemented in modern CT, it was discovered that NEQ cannot be aptly applied with certain non-traditional reconstruction filters or in helical mode. NPS and NEQ under a variety of conditions are examined. Extensions of NPS and NEQ to uses in protocol standardization are also discussed.

Application of the noise power spectrum in modern diagnostic MDCT: part II. Noise power spectra and signal to noise.

Balancing dose and image quality requires signal-to-noise (SNR) metrics which incorporate both the variance and the spatial frequency characteristics of noise. In this study, the non-prewhitening matched filter SNR metric is calculated for 2 mm slices of a 1 cm diameter sphere under three different conditions: (1) constant pixel standard deviation, (2) constant dose and (3) constant reconstruction filter. For the constant pixel standard deviation condition, an increase of 260% in SNR was found with increasing filter sharpness. For constant dose, the SNR remained level for smooth to medium filters, then declined by up to 55% with increasing filter sharpness. For a constant reconstruction filter, the SNR increased with dose, but not as high as photon statistics would predict. However, when structured noise was removed from the noise power spectrum, the SNR did vary with quanta statistics. These results offer protocol design guidance for low-frequency-dominated objects.

Estimating radiation doses from multidetector CT using Monte Carlo simulations: effects of different size voxelized patient models on magnitudes of organ and effective dose.

The purpose of this work is to examine the effects of patient size on radiation dose from CT scans. To perform these investigations, we used Monte Carlo simulation methods with detailed models of both patients and multidetector computed tomography (MDCT) scanners. A family of three-dimensional, voxelized patient models previously developed and validated by the GSF was implemented as input files using the Monte Carlo code MCNPX. These patient models represent a range of patient sizes and ages (8 weeks to 48 years) and have all radiosensitive organs previously identified and segmented, allowing the estimation of dose to any individual organ and calculation of patient effective dose. To estimate radiation dose, every voxel in each patient model was assigned both a specific organ index number and an elemental composition and mass density. Simulated CT scans of each voxelized patient model were performed using a previously developed MDCT source model that includes scanner specific spectra, including bowtie filter, scanner geometry and helical source path. The scan simulations in this work include a whole-body scan protocol and a thoracic CT scan protocol, each performed with fixed tube current. The whole-body scan simulation yielded a predictable decrease in effective dose as a function of increasing patient weight. Results from analysis of individual organs demonstrated similar trends, but with some individual variations. A comparison with a conventional dose estimation method using the ImPACT spreadsheet yielded an effective dose of 0.14 mSv mAs(-1) for the whole-body scan. This result is lower than the simulations on the voxelized model designated 'Irene' (0.15 mSv mAs(-1)) and higher than the models 'Donna' and 'Golem' (0.12 mSv mAs(-1)). For the thoracic scan protocol, the ImPACT spreadsheet estimates an effective dose of 0.037 mSv mAs(-1), which falls between the calculated values for Irene (0.042 mSv mAs(-1)) and Donna (0.031 mSv mAs(-1)) and is higher relative to Golem (0.025 mSv mAs(-1)). This work demonstrates the ability to estimate both individual organ and effective doses from any arbitrary CT scan protocol on individual patient-based models and to provide estimates of the effect of patient size on these dose metrics.

A Monte Carlo based method to estimate radiation dose from multidetector CT (MDCT): cylindrical and anthropomorphic phantoms.

The purpose of this work was to extend the verification of Monte Carlo based methods for estimating radiation dose in computed tomography (CT) exams beyond a single CT scanner to a multidetector CT (MDCT) scanner, and from cylindrical CTDI phantom measurements to both cylindrical and physical anthropomorphic phantoms. Both cylindrical and physical anthropomorphic phantoms were scanned on an MDCT under the specified conditions. A pencil ionization chamber was used to record exposure for the cylindrical phantom, while MOSFET (metal oxide semiconductor field effect transistor) detectors were used to record exposure at the surface of the anthropomorphic phantom. Reference measurements were made in air at isocentre using the pencil ionization chamber under the specified conditions. Detailed Monte Carlo models were developed for the MDCT scanner to describe the x-ray source (spectra, bowtie filter, etc) and geometry factors (distance from focal spot to isocentre, source movement due to axial or helical scanning, etc). Models for the cylindrical (CTDI) phantoms were available from the previous work. For the anthropomorphic phantom, CT image data were used to create a detailed voxelized model of the phantom's geometry. Anthropomorphic phantom material compositions were provided by the manufacturer. A simulation of the physical scan was performed using the mathematical models of the scanner, phantom and specified scan parameters. Tallies were recorded at specific voxel locations corresponding to the MOSFET physical measurements. Simulations of air scans were performed to obtain normalization factors to convert results to absolute dose values. For the CTDI body (32 cm) phantom, measurements and simulation results agreed to within 3.5% across all conditions. For the anthropomorphic phantom, measured surface dose values from a contiguous axial scan showed significant variation and ranged from 8 mGy/100 mAs to 16 mGy/100 mAs. Results from helical scans of overlapping pitch (0.9375) and extended pitch (1.375) were also obtained. Comparisons between the MOSFET measurements and the absolute dose value derived from the Monte Carlo simulations demonstrate agreement in terms of absolute dose values as well as the spatially varying characteristics. This work demonstrates the ability to extend models from a single detector scanner using cylindrical phantoms to an MDCT scanner using both cylindrical and anthropomorphic phantoms. Future work will be extended to voxelized patient models of different sizes and to other MDCT scanners.

A Monte Carlo-based method to estimate radiation dose from spiral CT: from phantom testing to patient-specific models.

The purpose of this work is to develop and test a method to estimate the relative and absolute absorbed radiation dose from axial and spiral CT scans using a Monte Carlo approach. Initial testing was done in phantoms and preliminary results were obtained from a standard mathematical anthropomorphic model (MIRD V) and voxelized patient data. To accomplish this we have modified a general purpose Monte Carlo transport code (MCNP4B) to simulate the CT x-ray source and movement, and then to calculate absorbed radiation dose in desired objects. The movement of the source in either axial or spiral modes was modelled explicitly while the CT system components were modelled using published information about x-ray spectra as well as information provided by the manufacturer. Simulations were performed for single axial scans using the head and body computed tomography dose index (CTDI) polymethylmethacrylate phantoms at both central and peripheral positions for all available beam energies and slice thicknesses. For comparison, corresponding physical measurements of CTDI in phantom were made with an ion chamber. To obtain absolute dose values, simulations and measurements were performed in air at the scanner isocentre for each beam energy. To extend the verification, the CT scanner model was applied to the MIRD V model and compared with published results using similar technical factors. After verification of the model, the generalized source was simulated and applied to voxelized models of patient anatomy. The simulated and measured absolute dose data in phantom agreed to within 2% for the head phantom and within 4% for the body phantom at 120 and 140 kVp; this extends to 8% for the head and 9% for the body phantom across all available beam energies and positions. For the head phantom, the simulated and measured absolute dose data agree to within 2% across all slice thicknesses at 120 kVp. Our results in the MIRD phantom agree within 11% of all the different organ dose values published by the UK's ImPACT group for a scan using an equivalent scanner, kVp, collimation, pitch and mAs. The CT source model was shown to calculate both a relative and absolute radiation dose distribution throughout the entire volume in a patient-specific matrix geometry. Results of initial testing are promising and application to patient models was shown to be feasible.

Spiral versus electron-beam CT for coronary artery calcium scoring.

PURPOSE: To determine differences in coronary artery calcium detection, quantification, and reproducibility, as measured at electron-beam computed tomography (CT) and subsecond spiral CT with retrospective electrocardiogram gating in an asymptomatic adult population. MATERIALS AND METHODS: Seventy subjects asymptomatic for coronary heart disease underwent both electron-beam CT and subsecond spiral CT. In all subjects, two images each were obtained with both scanners. Two experienced readers using three different algorithms scored each of the four scans: one score for the electron-beam CT images and two scores for the spiral CT images. RESULTS: With a 130-HU threshold for the quantification of calcium, there were no significant differences in interscan and interobserver variation in calcium scores between the electron-beam CT and spiral CT images. There was greater interobserver (P <.001) and interscan (P <.03) variation in scores when a 90-HU threshold was used for spiral CT images. With a 130-HU threshold, when calcium scores were used for clinical risk stratification, there was a significant difference between the results obtained with electron-beam CT and those obtained with spiral CT (P <.05). CONCLUSION: Spiral CT has not yet proved to be a feasible alternative to electron-beam CT for coronary artery calcium quantification. There are systematic differences between calcium scores obtained with single-detector array subsecond spiral CT and those obtained with electron-beam CT.

Knowledge-based segmentation of pediatric kidneys in CT for measurement of parenchymal volume.

PURPOSE: The purpose of this work was to develop an automated method for segmenting pediatric kidneys in helical CT images and measuring their volume. METHOD: An automated system was developed to segment the kidneys. Parametric features of anatomic structures were used to guide segmentation and labeling of image regions. Kidney volumes were calculated by summing included voxels. For validation, the kidney volumes of four swine were calculated using our approach and compared with the "true" volumes measured after harvesting the kidneys. Automated volume calculations were also performed in a cohort of nine children. RESULTS: The mean difference between the calculated and measured values in the swine kidneys was 1.38 ml. For the pediatric cases, calculated volumes ranged from 41.7 to 252.1 ml/kidney, and the mean ratio of right to left kidney volume was 0.96. CONCLUSION: These results demonstrate the accuracy of a volumetric technique that may in the future provide an objective assessment of renal damage.

Problem-oriented prefetching for an integrated clinical imaging workstation.

Prefetching methods have traditionally been used to restore archived images from picture archiving and communication systems to diagnostic imaging workstations prior to anticipated need, facilitating timely comparison of historical studies and patient management. The authors describe a problem-oriented prefetching scheme, detailing 1) a mechanism supporting selection of patients for prefetching via characterizations of clinical problems, using multiple data sources (picture archiving and communication systems, hospital information systems, and radiology information systems), classifying patients into cohorts on the basis of their medical conditions (e.g., lung cancer); and 2) prefetching of multimedia data (imaging, laboratory, and medical reports) from clinical databases to enable the viewing of an integrated patient record. Preliminary evaluation of the prefetching algorithm using classic information retrieval measures showed that the system had high recall (100 percent), correctly identifying and retrieving data for all patients belonging to a target cohort, but low precision (50 percent). A key finding during testing was that the recall of the system was increased through the use of multiple data sources (compared with one data source), because of better patient descriptors. Medical problems and patient cohorts were more specifically defined by combining information from heterogeneous databases.

Patient-specific models for lung nodule detection and surveillance in CT images.

The purpose of this work is to develop patient-specific models for automatically detecting lung nodules in computed tomography (CT) images. It is motivated by significant developments in CT scanner technology and the burden that lung cancer screening and surveillance imposes on radiologists. We propose a new method that uses a patient's baseline image data to assist in the segmentation of subsequent images so that changes in size and/or shape of nodules can be measured automatically. The system uses a generic, a priori model to detect candidate nodules on the baseline scan of a previously unseen patient. A user then confirms or rejects nodule candidates to establish baseline results. For analysis of follow-up scans of that particular patient, a patient-specific model is derived from these baseline results. This model describes expected features (location, volume and shape) of previously segmented nodules so that the system can relocalize them automatically on follow-up. On the baseline scans of 17 subjects, a radiologist identified a total of 36 nodules, of which 31 (86%) were detected automatically by the system with an average of 11 false positives (FPs) per case. In follow-up scans 27 of the 31 nodules were still present and, using patient-specific models, 22 (81%) were correctly relocalized by the system. The system automatically detected 16 out of a possible 20 (80%) of new nodules on follow-up scans with ten FPs per case.

Knowledge-based segmentation of thoracic computed tomography images for assessment of split lung function.

The assessment of differential left and right lung function is important for patients under consideration for lung resection procedures such as single lung transplantation. We developed an automated, knowledge-based segmentation algorithm for purposes of deriving functional information from dynamic computed tomography (CT) image data. Median lung attenuation (HU) and area measurements were automatically calculated for each lung from thoracic CT images acquired during a forced expiratory maneuver as indicators of the amount and rate of airflow. The accuracy of these derived measures from fully automated segmentation was validated against those from segmentation using manual editing by an expert observer. A total of 1313 axial images were analyzed from 49 patients. The images were segmented using our knowledge-based system that identifies the chest wall, mediastinum, trachea, large airways and lung parenchyma on CT images. The key components of the system are an anatomical model, an inference engine and image processing routines, and segmentation involves matching objects extracted from the image to anatomical objects described in the model. The segmentation results from all images were inspected by the expert observer. Manual editing was required to correct 183 (13.94%) of the images, and the sensitivity, specificity, and accuracy of the knowledge-based segmentation were greater than 98.55% in classifying pixels as lung or nonlung. There was no significant difference between median lung attenuation or area values from automated and edited segmentations (p > 0.70). Using the knowledge-based segmentation method we can automatically derive indirect quantitative measures of single lung function that cannot be obtained using conventional pulmonary function tests.

Cardiac electron-beam CT in children undergoing surgical repair for pulmonary atresia.

PURPOSE: To study whether electron-beam computed tomography (CT) is as accurate as conventional angiocardiography for the characterization of the true pulmonary arteries and the aortopulmonary collateral vessels in children undergoing surgical correction for pulmonary atresia. MATERIALS AND METHODS: Twenty-three children with pulmonary atresia underwent 48 cardiac-triggered dynamic contrast material-enhanced electron-beam CT studies. Correlation was made with surgical findings in all patients and with 34 cineangiocardiograms. Data from reconstructed electron-beam CT images and cineangiocardiograms were reviewed for the presence, caliber, and origin of true pulmonary arteries and aortopulmonary collateral vessels; for stenosis; for thrombosis; and for the patency of vascular conduits and shunts. RESULTS: Electron-beam CT was more sensitive than angiography in the identification of hypoplastic and/or nonconfluent branch pulmonary arteries, coronary anomalies, conduit and shunt thrombosis, and other postoperative complications, but it was less sensitive in the demonstration of stenoses at collateral vascular origins and anastomoses. Overall test parameters for electron-beam CT and angiography to characterize pulmonary vascularity were similar (sensitivity, 0.94 vs 0.90; specificity, 0.99 for both; accuracy, 0.97 vs 0.95). Three-dimensional reconstructions, although they were helpful in conveying electron-beam CT findings to referring cardiologists and surgeons, did not add diagnostic information to that displayed on images of the transverse sections. CONCLUSION: Electron-beam CT complements conventional diagnostic angiocardiography in preoperative evaluation, especially in the detection of hypoplastic pulmonary arteries. It is well suited for postoperative shunt surveillance.

Automated measurement of single and total lung volume from CT.

PURPOSE: The goal of this work was to develop an automated method for calculating single (SLV) and total (TLV) lung volumes from CT images. METHOD: Patients underwent volumetric CT scanning through the entire chest in a single breath-hold, as well as pulmonary function tests. An automated, knowledge-based system was developed to segment the lungs in the CT images. Image-processing routines were used to extract sets of voxels from the image data that were identified by matching them to anatomical objects defined in a model. SLV and TLV were calculated by summing included voxels. RESULTS: For 43 patients analyzed, TLV from CT and total lung capacity from body plethysmography were strongly correlated (r = 0.90). On average, the CT-derived volume of the left lung accounted for 47.2% of the total. CONCLUSION: A knowledge-based approach to segmentation of the lungs in CT can be used to automatically estimate SLV and TLV.