Variability in Radiation Dose From Repeat Identical CT Examinations: Longitudinal Analysis of 2851 Patients Undergoing 12,635 Thoracoabdominal CT Scans in an Academic Health System.

OBJECTIVE: The purpose of this study was to conduct longitudinal analyses of radiation dose data from adult patients undergoing clinically indicated, repeat identical thoracoabdominal CT examinations. MATERIALS AND METHODS: Radiation dose data were electronically collected from 2851 subjects undergoing 12,635 repeat identical CT scans (mean number of scans per patient, 4.8; range, 2-33) in one health system. Included CT protocols were chest-abdomen-pelvis with contrast administration (n = 4621 CT studies of 1064 patients), abdomen-pelvis with contrast administration (n = 876 CT studies of 261 patients), renal stone (n = 1053 CT studies of 380 patients), and chest (n = 6085 CT studies of 1146 patients) without contrast administration. A radiation-tracking software infrastructure was adopted to extract data from DICOM headers in PACS. Size-specific dose estimate (SSDE) was calculated. RESULTS: A trend was observed toward global reduction in SSDE values with all protocols investigated (chest-abdomen-pelvis slope, -1.78; abdomen-pelvis slope, -0.82; renal stone slope, -0.83; chest slope, -0.47; p < 0.001 for all comparisons). The intraindividual analyses of radiation dose distribution showed widespread variability in SSDE values across the four protocols investigated (chest-abdomen-pelvis mean coefficient of variance, 14.02 mGy; abdomen-pelvis mean coefficient of variance, 10.26 mGy; renal stone mean coefficient of variance, 34.18 mGy; chest mean coefficient of variance, 6.74 mGy). CONCLUSION: Although there is a trend toward global reduction in radiation doses, this study showed widespread variability in the radiation dose that each patient undergoing identical repeat thoracoabdominal CT protocols absorbs. These data may provide a foundation for the future development of best-practice guidelines for patient-specific radiation dose monitoring.

Effects of automatic tube potential selection on radiation dose index, image quality, and lesion detectability in pediatric abdominopelvic CT and CTA: a phantom study.

OBJECTIVES: To assess the effect of automatic tube potential selection (ATPS) on radiation dose, image quality, and lesion detectability in paediatric abdominopelvic CT and CT angiography (CTA). METHODS: A paediatric modular phantom with contrast inserts was examined with routine pitch (1.4) and high pitch (3.0) using a standard abdominopelvic protocol with fixed 120 kVp, and ATPS with variable kVp in non-contrast, contrast-enhanced, and CTA mode. The volume CT dose index (CTDIvol), contrast-to-noise ratio (CNR) and lesion detectability index (d') were compared between the standard protocol and ATPS examinations. RESULTS: CTDIvol was reduced in all routine pitch ATPS examinations, with dose reductions of 27-52 % in CTA mode (P < 0.0001), 15-33 % in contrast-enhanced mode (P = 0.0003) and 8-14 % in non-contrast mode (P = 0.03). Iodine and soft tissue insert CNR and d' were improved or maintained in all ATPS examinations. kVp and dose were reduced in 25 % of high pitch ATPS examinations and in none of the full phantom examinations obtained after a single full phantom localizer. CONCLUSIONS: ATPS reduces radiation dose while maintaining image quality and lesion detectability in routine pitch paediatric abdominopelvic CT and CTA, but technical factors such as pitch and imaging range must be considered to optimize ATPS benefits. KEY POINTS: ATPS automatically individualizes CT scan technique for each patient. ATPS lowers radiation dose in routine pitch pediatric abdominopelvic CT and CTA. There is no loss of image quality or lesion detectability with ATPS. Pitch and scan range impact the effectiveness of ATPS dose reduction.

Automated Technique to Measure Noise in Clinical CT Examinations.

OBJECTIVE: The purpose of this study was to develop and validate an automated method to measure noise in clinical CT examinations. MATERIALS AND METHODS: An automated algorithm was developed to measure noise in CT images. To assess its validity, the global noise level was compared with image noise measured using an image subtraction technique in an anthropomorphic phantom. The global noise level was further compared with image noise values from clinical patient CT images obtained by an observer study. Finally, the clinical utility of the global noise level was shown by assessing variability of image noise across scanner models for abdominopelvic CT examinations performed in 2358 patients. RESULTS: The global noise level agreed well with the phantom-based and clinical image-based noise measurements, with an average difference of 3.4% and 4.7% from each of these measures, respectively. No significant difference was detected between the global noise level and the validation dataset in either case. It further indicated differences across scanners, with the median global noise level varying significantly between different scanner models (15-35%). CONCLUSION: The global noise level provides an accurate, robust, and automated method to measure CT noise in clinical examinations for quality assurance programs. The significant difference in noise across scanner models indicates the unexploited potential to efficiently assess and subsequently improve protocol consistency. Combined with other automated characterization of imaging performance (e.g., dose monitoring), the global noise level may offer a promising platform for the standardization and optimization of CT protocols.

Evaluation of Low-Contrast Detectability of Iterative Reconstruction across Multiple Institutions, CT Scanner Manufacturers, and Radiation Exposure Levels.

PURPOSE: To compare image resolution from iterative reconstruction with resolution from filtered back projection for low-contrast objects on phantom computed tomographic (CT) images across vendors and exposure levels. MATERIALS AND METHODS: Randomized repeat scans of an American College of Radiology CT accreditation phantom (module 2, low contrast) were performed for multiple radiation exposures, vendors, and vendor iterative reconstruction algorithms. Eleven volunteers were presented with 900 images by using a custom-designed graphical user interface to perform a task created specifically for this reader study. Results were analyzed by using statistical graphics and analysis of variance. RESULTS: Across three vendors (blinded as A, B, and C) and across three exposure levels, the mean correct classification rate was higher for iterative reconstruction than filtered back projection (P < .01): 87.4% iterative reconstruction and 81.3% filtered back projection at 20 mGy, 70.3% iterative reconstruction and 63.9% filtered back projection at 12 mGy, and 61.0% iterative reconstruction and 56.4% filtered back projection at 7.2 mGy. There was a significant difference in mean correct classification rate between vendor B and the other two vendors. Across all exposure levels, images obtained by using vendor B's scanner outperformed the other vendors, with a mean correct classification rate of 74.4%, while the mean correct classification rate for vendors A and C was 68.1% and 68.3%, respectively. Across all readers, the mean correct classification rate for iterative reconstruction (73.0%) was higher compared with the mean correct classification rate for filtered back projection (67.0%). CONCLUSION: The potential exists to reduce radiation dose without compromising low-contrast detectability by using iterative reconstruction instead of filtered back projection. There is substantial variability across vendor reconstruction algorithms.

An Improved Index of Image Quality for Task-based Performance of CT Iterative Reconstruction across Three Commercial Implementations.

PURPOSE: To develop and validate a metric of computed tomographic (CT) image quality that incorporates the noise texture and resolution properties of an image. MATERIALS AND METHODS: Images of the American College of Radiology CT quality assurance phantom were acquired by using three commercial CT systems at seven dose levels with filtered back projection (FBP) and iterative reconstruction (IR). Image quality was characterized by the contrast-to-noise ratio (CNR) and a detectability index (d') that incorporated noise texture and spatial resolution. The measured CNR and d' were compared with a corresponding observer study by using the Spearman rank correlation coefficient to determine how well each metric reflects the ability of an observer to detect subtle lesions. Statistical significance of the correlation between each metric and observer performance was determined by using a Student t distribution; P values less than .05 indicated a significant correlation. Additionally, each metric was used to estimate the dose reduction potential of IR algorithms while maintaining image quality. RESULTS: Across all dose levels, scanner models, and reconstruction algorithms, the d' correlated strongly with observer performance in the corresponding observer study (ρ = 0.95; P < .001), whereas the CNR correlated weakly with observer performance (ρ = 0.31; P = .21). Furthermore, the d' showed that the dose-reduction capabilities differed between clinical implementations (range, 12%-35%) and were less than those predicted from the CNR (range, 50%-54%). CONCLUSION: The strong correlation between the observer performance and the d' indicates that the d' is superior to the CNR for the evaluation of CT image quality. Moreover, the results of this study indicate that the d' improves less than the CNR with the use of IR, which indicates less potential for IR dose reduction than previously thought.

Assessment of volumetric noise and resolution performance for linear and nonlinear CT reconstruction methods.

PURPOSE: For nonlinear iterative image reconstructions (IR), the computed tomography (CT) noise and resolution properties can depend on the specific imaging conditions, such as lesion contrast and image noise level. Therefore, it is imperative to develop a reliable method to measure the noise and resolution properties under clinically relevant conditions. This study aimed to develop a robust methodology to measure the three-dimensional CT noise and resolution properties under such conditions and to provide guidelines to achieve desirable levels of accuracy and precision. METHODS: The methodology was developed based on a previously reported CT image quality phantom. In this methodology, CT noise properties are measured in the uniform region of the phantom in terms of a task-based 3D noise-power spectrum (NPStask). The in-plane resolution properties are measured in terms of the task transfer function (TTF) by applying a radial edge technique to the rod inserts in the phantom. The z-direction resolution properties are measured from a supplemental phantom, also in terms of the TTF. To account for the possible nonlinearity of IR, the NPStask is measured with respect to the noise magnitude, and the TTF with respect to noise magnitude and edge contrast. To determine the accuracy and precision of the methodology, images of known noise and resolution properties were simulated. The NPStask and TTF were measured on the simulated images and compared to the truth, with criteria established to achieve NPStask and TTF measurements with <10% error. To demonstrate the utility of this methodology, measurements were performed on a commercial CT system using five dose levels, two slice thicknesses, and three reconstruction algorithms (filtered backprojection, FBP; iterative reconstruction in imaging space, IRIS; and sinogram affirmed iterative reconstruction with strengths of 5, SAFIRE5). RESULTS: To achieve NPStask measurements with <10% error, the number of regions of interest needed to be greater than 65. To achieve TTF measurements with <10% error, the contrast-to-noise ratio of the edge needed to be ≥15, achievable by averaging multiple slices across the same edge. The NPStask measured on a commercial CT system showed IR's reduced noise (IRIS, 30% and SAFIRE5, 55%) and "waxier" texture (peak frequencies: FBP, 0.25 mm(-1); IRIS, 0.23 mm(-1); and SAFIRE5, 0.16 mm(-1)). The TTF measured within the axial plane showed improved in-plane resolution with SAFIRE5 at the TTF 50% frequency, f50 (FBP, 0.36-0.41 mm(-1); SAFIRE5, 0.37-0.46 mm(-1)). The TTF measured along the axial direction showed improved z-direction resolution with thinner slice thickness (f50: 0.6 mm, 0.35-0.79 mm(-1); 1.5 mm, 0.22-0.3 mm(-1)) and with SAFIRE5 (f50: FBP, 0.35-0.52 mm(-1); SAFIRE5, 0.42-0.79 mm(-1)). Both in-plane and z-direction resolution of SAFIRE5 showed strong dependency on contrast, reflecting SAFIRE5's nonlinearity. CONCLUSIONS: A methodology was developed to measure three-dimensional CT noise and resolution properties for iterative reconstruction, especially at challenging measurement conditions with low contrast and high image noise. The methodology also demonstrated its utility for evaluating commercial CT systems.

Improved nuclear medicine uniformity assessment with noise texture analysis.

UNLABELLED: Because γ cameras are generally susceptible to environmental conditions and system vulnerabilities, they require routine evaluation of uniformity performance. The metrics for such evaluations are commonly pixel value-based. Although these metrics are typically successful at identifying regional nonuniformities, they often do not adequately reflect subtle periodic structures; therefore, additional visual inspections are required. The goal of this project was to develop, test, and validate a new uniformity analysis metric capable of accurately identifying structures and patterns present in nuclear medicine flood-field uniformity images. METHODS: A new uniformity assessment metric, termed the structured noise index (SNI), was based on the 2-dimensional noise power spectrum (NPS). The contribution of quantum noise was subtracted from the NPS of a flood-field uniformity image, resulting in an NPS representing image artifacts. A visual response filter function was then applied to both the original NPS and the artifact NPS. A single quantitative score was calculated on the basis of the magnitude of the artifact. To verify the validity of the SNI, an observer study was performed with 5 expert nuclear medicine physicists. The correlation between the SNI and the visual score was assessed with Spearman rank correlation analysis. The SNI was also compared with pixel value-based assessment metrics modeled on the National Electrical Manufacturers Association standard for integral uniformity in both the useful field of view (UFOV) and the central field of view (CFOV). RESULTS: The SNI outperformed the pixel value-based metrics in terms of its correlation with the visual score (ρ values for the SNI, integral UFOV, and integral CFOV were 0.86, 0.59, and 0.58, respectively). The SNI had 100% sensitivity for identifying both structured and nonstructured nonuniformities; for the integral UFOV and CFOV metrics, the sensitivities were only 62% and 54%, respectively. The overall positive predictive value of the SNI was 87%; for the integral UFOV and CFOV metrics, the positive predictive values were only 67% and 50%, respectively. CONCLUSION: The SNI accurately identified both structured and nonstructured flood-field nonuniformities and correlated closely with expert visual assessment. Compared with traditional pixel value-based analysis, the SNI showed superior performance in terms of its correlation with visual perception. The SNI method is effective for detecting and quantifying visually apparent nonuniformities and may reduce the need for more subjective visual analyses.

DQE of wireless digital detectors: comparative performance with differing filtration schemes.

PURPOSE: Wireless flat panel detectors are gaining increased usage in portable medical imaging. Two such detectors were evaluated and compared with a conventional flat-panel detector using the formalism of the International Electrotechnical Commission (IEC 62220-1) for measuring modulation transfer function (MTF), normalized noise power spectrum (NNPS), and detective quantum efficiency (DQE) using two different filtration schemes. METHODS: Raw images were acquired for three image receptors (DRX-1C and DRX-1, Carestream Health; Inc., Pixium 4600, Trixell) using a radiographic system with a well-characterized output (Philips Super80 CP, Philips Healthcare). Free in-air exposures were measured using a calibrated radiation meter (Unfors Mult-O-Meter Type 407, Unfors Instruments AB). Additional aluminum filtration and a new alternative combined copper-aluminum filtration were used to conform the x ray output to IEC-specified beam quality definitions RQA5 and RQA9. Using the IEC 62220-1 formalism, each detector was evaluated at XN∕2, XN, and 2XN, where the normal exposure level to the detector surface (XN) was set to 8.73 µGy (1.0 mR). The prescribed edge test device was used to evaluate the MTF, while the NNPS was measured using uniform images. The DQE was then calculated from the MTF and NNPS and compared across detectors, exposures, and filtration schemes. RESULTS: The three DR systems had largely comparable MTFs with DRX-1 demonstrating lower values above 1.0 cycles∕mm. At each exposure, DRX-1C and Pixium detectors demonstrated better noise performance than that of DRX-1. Zero-frequency DQEs for DRX-1C, Pixium, and DRX-1 detectors were approximately 74%, 63%, and 38% for RQA5 and 50%, 42%, and 28% for RQA9, respectively. CONCLUSIONS: DRX-1C detector exhibited superior DQE performance compared to Pixium and DRX-1. In terms of filtration, the alternative filtration was found to provide comparable performance in terms of rank ordering of different detectors with the added convenience of being less bulky for in-the-field measurements.

A methodology for image quality evaluation of advanced CT systems.

PURPOSE: This work involved the development of a phantom-based method to quantify the performance of tube current modulation and iterative reconstruction in modern computed tomography (CT) systems. The quantification included resolution, HU accuracy, noise, and noise texture accounting for the impact of contrast, prescribed dose, reconstruction algorithm, and body size. METHODS: A 42-cm-long, 22.5-kg polyethylene phantom was designed to model four body sizes. Each size was represented by a uniform section, for the measurement of the noise-power spectrum (NPS), and a feature section containing various rods, for the measurement of HU and the task-based modulation transfer function (TTF). The phantom was scanned on a clinical CT system (GE, 750HD) using a range of tube current modulation settings (NI levels) and reconstruction methods (FBP and ASIR30). An image quality analysis program was developed to process the phantom data to calculate the targeted image quality metrics as a function of contrast, prescribed dose, and body size. RESULTS: The phantom fabrication closely followed the design specifications. In terms of tube current modulation, the tube current and resulting image noise varied as a function of phantom size as expected based on the manufacturer specification: From the 16- to 37-cm section, the HU contrast for each rod was inversely related to phantom size, and noise was relatively constant (<5% change). With iterative reconstruction, the TTF exhibited a contrast dependency with better performance for higher contrast objects. At low noise levels, TTFs of iterative reconstruction were better than those of FBP, but at higher noise, that superiority was not maintained at all contrast levels. Relative to FBP, the NPS of iterative reconstruction exhibited an ~30% decrease in magnitude and a 0.1 mm(-1) shift in the peak frequency. CONCLUSIONS: Phantom and image quality analysis software were created for assessing CT image quality over a range of contrasts, doses, and body sizes. The testing platform enabled robust NPS, TTF, HU, and pixel noise measurements as a function of body size capable of characterizing the performance of reconstruction algorithms and tube current modulation techniques.

Automated size-specific CT dose monitoring program: assessing variability in CT dose.

PURPOSE: The potential health risks associated with low levels of ionizing radiation have created a movement in the radiology community to optimize computed tomography (CT) imaging protocols to use the lowest radiation dose possible without compromising the diagnostic usefulness of the images. Despite efforts to use appropriate and consistent radiation doses, studies suggest that a great deal of variability in radiation dose exists both within and between institutions for CT imaging. In this context, the authors have developed an automated size-specific radiation dose monitoring program for CT and used this program to assess variability in size-adjusted effective dose from CT imaging. METHODS: The authors radiation dose monitoring program operates on an independent health insurance portability and accountability act compliant dosimetry server. Digital imaging and communication in medicine routing software is used to isolate dose report screen captures and scout images for all incoming CT studies. Effective dose conversion factors (k-factors) are determined based on the protocol and optical character recognition is used to extract the CT dose index and dose-length product. The patient's thickness is obtained by applying an adaptive thresholding algorithm to the scout images and is used to calculate the size-adjusted effective dose (ED(adj)). The radiation dose monitoring program was used to collect data on 6351 CT studies from three scanner models (GE Lightspeed Pro 16, GE Lightspeed VCT, and GE Definition CT750 HD) and two institutions over a one-month period and to analyze the variability in ED(adj) between scanner models and across institutions. RESULTS: No significant difference was found between computer measurements of patient thickness and observer measurements (p = 0.17), and the average difference between the two methods was less than 4%. Applying the size correction resulted in ED(adj) that differed by up to 44% from effective dose estimates that were not adjusted by patient size. Additionally, considerable differences were noted in ED(adj) distributions between scanners, with scanners employing iterative reconstruction exhibiting significantly lower ED(adj) (range: 9%-64%). Finally, a significant difference (up to 59%) in ED(adj) distributions was observed between institutions, indicating the potential for dose reduction. CONCLUSIONS: The authors developed a robust automated size-specific radiation dose monitoring program for CT. Using this program, significant differences in ED(adj) were observed between scanner models and across institutions. This new dose monitoring program offers a unique tool for improving quality assurance and standardization both within and across institutions.

Quantitative comparison of noise texture across CT scanners from different manufacturers.

PURPOSE: To quantitatively compare noise texture across computed tomography (CT) scanners from different manufacturers using the noise power spectrum (NPS). METHODS: The American College of Radiology CT accreditation phantom (Gammex 464, Gammex, Inc., Middleton, WI) was imaged on two scanners: Discovery CT 750HD (GE Healthcare, Waukesha, WI), and SOMATOM Definition Flash (Siemens Healthcare, Germany), using a consistent acquisition protocol (120 kVp, 0.625∕0.6 mm slice thickness, 250 mAs, and 22 cm field of view). Images were reconstructed using filtered backprojection and a wide selection of reconstruction kernels. For each image set, the 2D NPS were estimated from the uniform section of the phantom. The 2D spectra were normalized by their integral value, radially averaged, and filtered by the human visual response function. A systematic kernel-by-kernel comparison across manufacturers was performed by computing the root mean square difference (RMSD) and the peak frequency difference (PFD) between the NPS from different kernels. GE and Siemens kernels were compared and kernel pairs that minimized the RMSD and |PFD| were identified. RESULTS: The RMSD (|PFD|) values between the NPS of GE and Siemens kernels varied from 0.01 mm(2) (0.002 mm(-1)) to 0.29 mm(2) (0.74 mm(-1)). The GE kernels "Soft," "Standard," "Chest," and "Lung" closely matched the Siemens kernels "B35f," "B43f," "B41f," and "B80f" (RMSD < 0.05 mm(2), |PFD| < 0.02 mm(-1), respectively). The GE "Bone," "Bone+," and "Edge" kernels all matched most closely with Siemens "B75f" kernel but with sizeable RMSD and |PFD| values up to 0.18 mm(2) and 0.41 mm(-1), respectively. These sizeable RMSD and |PFD| values corresponded to visually perceivable differences in the noise texture of the images. CONCLUSIONS: It is possible to use the NPS to quantitatively compare noise texture across CT systems. The degree to which similar texture across scanners could be achieved varies and is limited by the kernels available on each scanner.