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.

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.

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 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.

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.

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.

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.

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.

Image preprocessing for a picture archiving and communication system.

OBJECTIVES AND RATIONALE: In a picture archiving and communication system (PACS), images are acquired from multiple modalities and displayed on an electronic workstation. Each modality has different image characteristics. This variability must be addressed before the image is displayed. METHODS: The authors developed methods to automatically process magnetic resonance (MR), computed tomographic (CT), and computed radiography (CR) images before display and subjectively evaluated their effectiveness. RESULTS: Unwanted background successfully was automatically removed from 89.5% of 615 CR images. Of 803 chest, abdomen, and hand images 93% were automatically rotated to the correct orientation. CONCLUSIONS: Automated preprocessing of PACS images can be performed successfully, improving speed and convenience for the radiologist interpreting images at an electronic workstation.

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.

A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: preliminary results.

The purpose of this research is to characterize solitary pulmonary nodules as benign or malignant based on quantitative measures extracted from high resolution CT (HRCT) images. High resolution CT images of 31 patients with solitary pulmonary nodules and definitive diagnoses were obtained. The diagnoses of these 31 cases (14 benign and 17 malignant) were determined from either radiologic follow-up or pathological specimens. Software tools were developed to perform the classification task. On the HRCT images, solitary nodules were identified using semiautomated contouring techniques. From the resulting contours, several quantitative measures were extracted related to each nodule's size, shape, attenuation, distribution of attenuation, and texture. A stepwise discriminant analysis was performed to determine which combination of measures were best able to discriminate between the benign and malignant nodules. A linear discriminant analysis was then performed using selected features to evaluate the ability of these features to predict the classification for each nodule. A jackknifed procedure was performed to provide a less biased estimate of the linear discriminator's performance. The preliminary discriminant analysis identified two different texture measures--correlation and difference entropy--as the top features in discriminating between benign and malignant nodules. The linear discriminant analysis using these features correctly classified 28/31 cases (90.3%) of the training set. A less biased estimate, using jackknifed training and testing, yielded the same results (90.3% correct). The preliminary results of this approach are very promising in characterizing solitary nodules using quantitative measures extracted from HRCT images. Future work involves including contrast enhancement and three-dimensional measures extracted from volumetric CT scans, as well as the use of several pattern classifiers.

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.

Feature selection in the pattern classification problem of digital chest radiograph segmentation.

In pattern classification problems, the choice of variables to include in the feature vector is a difficult one. The authors have investigated the use of stepwise discriminant analysis as a feature selection step in the problem of segmenting digital chest radiographs. In this problem, locally calculated features are used to classify pixels into one of several anatomic classes. The feature selection step was used to choose a subset of features which gave performance equivalent to the entire set of candidate features, while utilizing less computational resources. The impact of using the reduced/selected feature set on classifier performance is evaluated for two classifiers: a linear discriminator and a neural network. The results from the reduced/selected feature set were compared to that of the full feature set as well as a randomly selected reduced feature set. The results of the different feature sets were also compared after applying an additional postprocessing step which used a rule-based spatial information heuristic to improve the classification results. This work shows that, in the authors' pattern classification problem, using a feature selection step reduced the number of features used, reduced the processing time requirements, and gave results comparable to the full set of features.

Computer-assisted phalangeal analysis in skeletal age assessment.

A computerized approach to the problem of the assessment of skeletal maturity in pediatric radiology is presented. A CR (computed radiography) hand image to be analyzed is first standardized to obtain a left hand, upright, PA view. Then the phalangeal region of interest is defined and thresholded. After the separation of the third finger, the lengths of the distal, middle, and proximal phalanx are measured automatically. Using the standard phalangeal length table, the skeletal age is estimated. The assessed age has been compared to the estimates obtained by a radiologist using the atlas matching method as well as the chronological age.

Method for segmenting chest CT image data using an anatomical model: preliminary results.

We present an automated, knowledge-based method for segmenting chest computed tomography (CT) datasets. Anatomical knowledge including expected volume, shape, relative position, and X-ray attenuation of organs provides feature constraints that guide the segmentation process. Knowledge is represented at a high level using an explicit anatomical model. The model is stored in a frame-based semantic network and anatomical variability is incorporated using fuzzy sets. A blackboard architecture permits the data representation and processing algorithms in the model domain to be independent of those in the image domain. Knowledge-constrained segmentation routines extract contiguous three-dimensional (3-D) sets of voxels, and their feature-space representations are posted on the blackboard. An inference engine uses fuzzy logic to match image to model objects based on the feature constraints. Strict separation of model and image domains allows for systematic extension of the knowledge base. In preliminary experiments, the method has been applied to a small number of thoracic CT datasets. Based on subjective visual assessment by experienced thoracic radiologists, basic anatomic structures such as the lungs, central tracheobronchial tree, chest wall, and mediastinum were successfully segmented. To demonstrate the extensibility of the system, knowledge was added to represent the more complex anatomy of lung lesions in contact with vessels or the chest wall. Visual inspection of these segmented lesions was also favorable. These preliminary results suggest that use of expert knowledge provides an increased level of automation compared with low-level segmentation techniques. Moreover, the knowledge-based approach may better discriminate between structures of similar attenuation and anatomic contiguity. Further validation is required.

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.

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.

Electron-beam CT: the effect of using a correction function on coronary artery calcium quantitation.

PURPOSE: The purpose of the study was to determine if use of a correction function derived from a phantom with known calcium density reduces variation in coronary artery calcium scores measured with electron-beam computed tomography (CT) and to evaluate the influence of body girth, as measured by body mass index (BMI), on coronary artery calcium-score variability in a clinical screening cohort. MATERIALS AND METHODS: Two hundred fifty consecutive patients underwent two electron-beam CT examinations with a standardized phantom. Region-of-interest (ROI) measurements of the phantom were correlated with the patient BMI. In a subset of 28 patients with low but positive coronary artery calcium scores, ROI values of the phantom were used as a correction for signal attenuation. RESULTS: Patient BMI was not significantly correlated to signal attenuation. Application of a correction function resulted in a small but insignificant decrease in interscan coronary artery calcium-score variation. The interscan coronary artery calcium score variation was 24.5% +/- 23.9; it decreased to 24.3% +/- 21.6 with use of the 75 mg/cm3 portion of the phantom and to 22.2% +/- 21.4 with use of the 150 mg/cm3 portion of the phantom. CONCLUSION: The use of a coronary artery calcium phantom does not reduce interscan variation sufficiently to warrant regular clinical usage. Interscan variation in the coronary artery calcium score is not significantly influenced by patient BMI.

Coronary artery calcium: alternate methods for accurate and reproducible quantitation.

RATIONALE AND OBJECTIVES: The aim of this study was to determine a more precise and accurate method of quantitating coronary artery calcium (CAC) detected with electron-beam computed tomography (CT) in patients with low CAC scores. MATERIALS AND METHODS: Two 40-section, 3-mm-collimation, electrocardiographically gated electron-beam CT examinations of the heart were performed in each patient. Fifty patients with average scores between 2 and 100, as determined with the conventional scoring algorithm, were selected. The modified conventional scoring algorithm was compared with two techniques: calculated calcium volume and approximated calcium mass. RESULTS: The percentage difference between scans ranged from 37.2% for the conventional scoring method to 28.2% and 28.4% for volume- and mass-based methods, respectively. Increasing lesion size thresholds does not improve quantitative precision and reduces accuracy in patients with small amounts of CAC. CONCLUSION: Quantification methods based on calcification volume or mass decrease score variation compared with the conventional scoring method, and increased size threshold does not improve accuracy.