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Multi-energy computed tomography (MECT) offers the opportunity for advanced visualization, detection, and quantification of select elements (e.g., iodine) or materials (e.g., fat) beyond the capability of standard single-energy computed tomography (CT). However, the use of MECT requires careful consideration as substantially different hardware and software approaches have been used by manufacturers, including different sets of user-selected or hidden parameters that affect the performance and radiation dose of MECT. Another important consideration when designing MECT protocols is appreciation of the specific tasks being performed; for instance, differentiating between two different materials or quantifying a specific element. For a given task, it is imperative to consider both the radiation dose and task-specific image quality requirements. Development of a quality control (QC) program is essential to ensure the accuracy and reproducibility of these MECT applications. Although standard QC procedures have been well established for conventional single-energy CT, the substantial differences between single-energy CT and MECT in terms of system implementations, imaging protocols, and clinical tasks warrant QC tests specific to MECT. This task group was therefore charged with developing a systematic QC program designed to meet the needs of MECT applications. In this report, we review the various MECT approaches that are commercially available, including information about hardware implementation, MECT image types, image reconstruction, and postprocessing techniques that are unique to MECT. We address the requirements for MECT phantoms, review representative commercial MECT phantoms, and offer guidance regarding homemade MECT phantoms. We discuss the development of MECT protocols, which must be designed carefully with proper consideration of MECT technology, imaging task, and radiation dose. We then outline specific recommended QC tests in terms of general image quality, radiation dose, differentiation and quantification tasks, and diagnostic and therapeutic applications., (© 2024 American Association of Physicists in Medicine.)

Background: Global shortages of iodinated contrast media (ICM) during COVID-19 pandemic forced the imaging community to use ICM more strategically in CT exams., Purpose: The purpose of this work is to provide a quantitative framework for preserving iodine CNR while reducing ICM dosage by either lowering kV in single-energy CT (SECT) or using lower energy virtual monochromatic images (VMI) from dual-energy CT (DECT) in a phantom study., Materials and Methods: In SECT study, phantoms with effective diameters of 9.7, 15.9, 21.1, and 28.5 cm were scanned on SECT scanners of two different manufacturers at a range of tube voltages. Statistical based iterative reconstruction and deep learning reconstruction were used. In DECT study, phantoms with effective diameters of 20, 29.5, 34.6, and 39.7 cm were scanned on DECT scanners from three different manufacturers. VMIs were created from 40 to 140 keV. ICM reduction by lowering kV levels for SECT or switching from SECT to DECT was calculated based on the linear relationship between iodine CNR and its concentration under different scanning conditions., Results: On SECT scanner A, while matching CNR at 120 kV, ICM reductions of 21%, 58%, and 72% were achieved at 100, 80, and 70 kV, respectively. On SECT scanner B, 27% and 80% ICM reduction was obtained at 80 and 100 kV. On the Fast-kV switch DECT, with CNR matched at 120 kV, ICM reductions were 35%, 30%, 23%, and 15% with VMIs at 40, 50, 60, and 68 keV, respectively. On the dual-source DECT, ICM reductions were 52%, 48%, 42%, 33%, and 22% with VMIs at 40, 50, 60, 70, and 80 keV. On the dual-layer DECT, ICM reductions were 74%, 62%, 45%, and 22% with VMIs at 40, 50, 60, and 70 keV., Conclusions: Our work provided a quantitative baseline for other institutions to further optimize their scanning protocols to reduce the use of ICM., (© 2024 The Authors. Journal of Applied Clinical Medical Physics is published by Wiley Periodicals, Inc. on behalf of The American Association of Physicists in Medicine.)

Purpose: Chemical shift-encoded magnetic resonance imaging enables accurate quantification of liver fat content though estimation of proton density fat-fraction (PDFF). Computed tomography (CT) is capable of quantifying fat, based on decreased attenuation with increased fat concentration. Current quantitative fat phantoms do not accurately mimic the CT number of human liver. The purpose of this work was to develop and validate an optimized phantom that simultaneously mimics the MRI and CT signals of fatty liver., Methods: An agar-based phantom containing 12 vials doped with iodinated contrast, and with a granular range of fat fractions was designed and constructed within a novel CT and MR compatible spherical housing design. A four-site, three-vendor validation study was performed. MRI (1.5T and 3T) and CT images were obtained using each vendor's PDFF and CT reconstruction, respectively. An ROI centered in each vial was placed to measure MRI-PDFF (%) and CT number (HU). Mixed-effects model, linear regression, and Bland-Altman analysis were used for statistical analysis., Results: MRI-PDFF agreed closely with nominal PDFF values across both field strengths and all MRI vendors. A linear relationship (slope = -0.54 ± 0.01%/HU, intercept = 37.15 ± 0.03%) with an R 2 of 0.999 was observed between MRI-PDFF and CT number, replicating established in vivo signal behavior. Excellent test-retest repeatability across vendors (MRI: mean = -0.04%, 95% limits of agreement = [-0.24%, 0.16%]; CT: mean = 0.16 HU, 95% limits of agreement = [-0.15HU, 0.47HU]) and good reproducibility using GE scanners (MRI: mean = -0.21%, 95% limits of agreement = [-1.47%, 1.06%]; CT: mean = -0.18HU, 95% limits of agreement = [-1.96HU, 1.6HU]) were demonstrated., Conclusions: The proposed fat phantom successfully mimicked quantitative liver signal for both MRI and CT. The proposed fat phantom in this study may facilitate broader application and harmonization of liver fat quantification techniques using MRI and CT across institutions, vendors and imaging platforms., (© 2021 American Association of Physicists in Medicine.)

Purpose: To describe a large, publicly available dataset comprising computed tomography (CT) projection data from patient exams, both at routine clinical doses and simulated lower doses., Acquisition and Validation Methods: The library was developed under local ethics committee approval. Projection and image data from 299 clinically performed patient CT exams were archived for three types of clinical exams: noncontrast head CT scans acquired for acute cognitive or motor deficit, low-dose noncontrast chest scans acquired to screen high-risk patients for pulmonary nodules, and contrast-enhanced CT scans of the abdomen acquired to look for metastatic liver lesions. Scans were performed on CT systems from two different CT manufacturers using routine clinical protocols. Projection data were validated by reconstructing the data using several different reconstruction algorithms and through use of the data in the 2016 Low Dose CT Grand Challenge. Reduced dose projection data were simulated for each scan using a validated noise-insertion method. Radiologists marked location and diagnosis for detected pathologies. Reference truth was obtained from the patient medical record, either from histology or subsequent imaging., Data Format and Usage Notes: Projection datasets were converted into the previously developed DICOM-CT-PD format, which is an extended DICOM format created to store CT projections and acquisition geometry in a nonproprietary format. Image data are stored in the standard DICOM image format and clinical data in a spreadsheet. Materials are provided to help investigators use the DICOM-CT-PD files, including a dictionary file, data reader, and user manual. The library is publicly available from The Cancer Imaging Archive (https://doi.org/10.7937/9npb-2637)., Potential Applications: This CT data library will facilitate the development and validation of new CT reconstruction and/or denoising algorithms, including those associated with machine learning or artificial intelligence. The provided clinical information allows evaluation of task-based diagnostic performance., (© 2020 American Association of Physicists in Medicine.)

Purpose: To compare radiation dose and image quality for abdominal CTs performed on a spectral detector CT (SDCT) and a comparable single-energy conventional CT scanner for patients of different sizes., Methods: Four semi-anthropomorphic phantoms were scanned on an SDCT (IQon, Philips Healthcare) and a comparable single-energy CT (iCT 256, Philips Healthcare) under matched scan parameters. Image noise and radiation dose were compared. For the HIPAA-compliant, IRB-approved retrospective cohort patient study, radiation dose was compared after adjusting for patient water equivalent diameter. Difference in subjective and objective image quality was assessed on a subset of 50 patients scanned on both scanners by two readers., Results: CTDIvol and noise from SDCT were higher than conventional CT for all phantoms, with a relative difference of 7.8% (range 5.3-14%) for radiation dose and average difference of 9.0% (range 5.5-11%) for noise. 718 SDCT and 937 conventional CT patients were included in the patient study. CTDIvol for SDCT patients tends to be lower for smaller patients (- 2%, 95% confidence interval (- 5%, - 0.2%) for 200 mm water equivalent diameter) and higher for larger patients compared to conventional CT (8%, (6%, 11%) for 400 mm). No difference was seen for subjective image quality, SNR, CNR, or image noise between the two scanners, except for higher image noise in the portal vein and higher signal in the aorta on SDCT., Conclusion: Radiation dose for abdominal CT performed on SDCT is similar to the dose on a conventional CT for average size patients, lower for smaller patients, and slightly higher for larger patients. Image quality is similar between the two scanners.

In x-ray computed tomography (CT), materials with different elemental compositions can have identical CT number values, depending on the mass density of each material and the energy of the detected x-ray beam. Differentiating and classifying different tissue types and contrast agents can thus be extremely challenging. In multienergy CT, one or more additional attenuation measurements are obtained at a second, third or more energy. This allows the differentiation of at least two materials. Commercial dual-energy CT systems (only two energy measurements) are now available either using sequential acquisitions of low- and high-tube potential scans, fast tube-potential switching, beam filtration combined with spiral scanning, dual-source, or dual-layer detector approaches. The use of energy-resolving, photon-counting detectors is now being evaluated on research systems. Irrespective of the technological approach to data acquisition, all commercial multienergy CT systems circa 2020 provide dual-energy data. Material decomposition algorithms are then used to identify specific materials according to their effective atomic number and/or to quantitate mass density. These algorithms are applied to either projection or image data. Since 2006, a number of clinical applications have been developed for commercial release, including those that automatically (a) remove the calcium signal from bony anatomy and/or calcified plaque; (b) create iodine concentration maps from contrast-enhanced CT data and/or quantify absolute iodine concentration; (c) create virtual non-contrast-enhanced images from contrast-enhanced scans; (d) identify perfused blood volume in lung parenchyma or the myocardium; and (e) characterize materials according to their elemental compositions, which can allow in vivo differentiation between uric acid and non-uric acid urinary stones or uric acid (gout) or non-uric acid (calcium pyrophosphate) deposits in articulating joints and surrounding tissues. In this report, the underlying physical principles of multienergy CT are reviewed and each of the current technical approaches are described. In addition, current and evolving clinical applications are introduced. Finally, the impact of multienergy CT technology on patient radiation dose is summarized., (© 2020 American Association of Physicists in Medicine.)

Purpose: Water equivalent diameter (WED) is a robust patient-size descriptor. Localizer-based WED estimation is less sensitive to truncation errors resulting from limited field of view, and produces WED estimates at different locations within one localizer radiograph, prior to the initiation of axial scans. This method is considered difficult to implement by the clinical community due to the necessary calibration between localizer pixel values (LPV) and attenuation, and the unknown stability of calibration results across scanners and over time. We investigated the stability of calibration results across 25 computed tomography (CT) scanners from three medical centers, and their stability over 3 ∼ 29 months for 14 of those scanners., Methods: Localizer and axial images of ACR and body computed tomography dose index phantoms were acquired, using routine clinical techniques (120 kV and lateral localizers) on each of the 25 CT scanners: 8 GE scanners (CT750HD, VCT, and Revolution), 8 Siemens scanners (Definition AS, Force, Flash, and Edge), 5 Canon scanners (Aquilion-One, Aquilion-Prime80, and Aquilion-64), and 4 Philips scanners (iCT 256, iQon, and Ingenuity). By associating axial images with the corresponding localizer lines, the relationship between the scaled water equivalent area (WEA) and averaged LPV were established through regression analysis., Results: Linear relationships between the scaled WEA and the averaged LPV were observed in all 25 CT scanners ( R 2 > 0.999 ). Calibration parameters were similar for CT scanners from the same vendor: the coefficients of variation (COV) were ≤ 1% in all four vendor groups for the calibration slope, and < 7% for the intercept. By analyzing the deviation of WED resulted from errors in the calibration slope or intercept alone, we derived the tolerance ranges for the slope or intercept for a given WED error level. The variation of slope and intercept from different CT scanners of the same vendor introduced <±2.5% error in the estimated WED for subjects of 20 and 30-cm WED. The calibration parameters remained stable over time, with the maximum deviations all within the boundary values that introduce ±2.5% error in the estimated WED for subjects of 20 and 30-cm WED., Conclusions: The stability in calibration results among CT scanners of the same vendor and over time demonstrated the feasibility of implementing WED estimation for routine clinical use., (© 2020 American Association of Physicists in Medicine.)

The dual-energy computed tomography (DECT)-based approach holds promise in reducing the overall uncertainty in proton stopping-power-ratio (SPR) estimation, but cannot be easily implemented with most commercial proton treatment planning systems (TPS). In this study, we revisited the idea of coupling the stoichiometric calibration method with virtual monoenergetic CT datasets (MonoCT) generated by modern DECT scanners, because of its readiness for implementation with the existing TPS. Our objective was to determine the optimal energy of the MonoCT dataset for stoichiometric calibration and estimate the overall uncertainty in SPR estimation at the optimal energy. We performed stoichiometric calibration for MonoCT datasets across the energy range available on a Siemens Force DECT scanner and a Philips IQon DECT scanner in a 10 keV step. We estimated the uncertainties of different sources (imaging, modeling, and inherent uncertainties) for different tissue types (lung, soft, and bone tissues) associated with each energy; these were then combined into a single composite uncertainty for three tumor sites (head-and-neck (HN), lung, and prostate). The optimal energy was eventually selected based on the composite range uncertainty, which turned out to be 160 keV for both DECT scanners. At 160 keV, the total uncertainties (2σ) in SPR estimation were determined to be 3.2%-4.5%, 0.9%, and 1.4%-1.6% for lung, soft, and bony tissues, respectively. These results were comparable to the corresponding values estimated for the DECT approach evaluated in our previous study: 3.8%, 1.2% and 2.0%, for lung, soft, and bony tissues, respectively. The composite range uncertainties (2σ) were estimated as 1.5%, 1.7%, and 1.5% for prostate, lung, and HN, respectively. Our results demonstrated the potential of MonoCT images for reducing proton SPR uncertainty. Further clinical studies are needed to compare this approach with the DECT approach directly on real patient cases.

Background Assessments of the quantitative limitations among the six commercially available dual-energy (DE) CT acquisition schemes used by major CT manufacturers could aid researchers looking to use iodine quantification as an imaging biomarker. Purpose To determine the limits of detection and quantification of DE CT in phantoms by comparing rapid peak kilovoltage switching, dual-source, split-filter, and dual-layer detector systems in six different scanners. Materials and Methods Seven 50-mL iohexol solutions were used, with concentrations of 0.03-2.0 mg iodine per milliliter. The solutions and water sample were scanned five times each in two phantoms (small, 20-cm diameter; large, 30 × 40-cm diameter) with six DE CT systems and a total of 10 peak kilovoltage settings or combinations. Iodine maps were created, and the mean iodine signal in each sample was recorded. The limit of blank (LOB) was defined as the upper limit of the 95% confidence interval of the water sample. The limit of detection (LOD) was defined as the concentration with a 95% chance of having a signal above the LOB. The limit of quantification (LOQ) was defined as the lowest concentration where the coefficient of variation was less than 20%. Results The LOD range was 0.021-0.26 mg/mL in the small phantom and 0.026-0.55 mg/mL in the large phantom. The LOQ range was 0.07-0.50 mg/mL in the small phantom and 0.20-1.0 mg/mL in the large phantom. The dual-source and rapid peak kilovoltage switching systems had the lowest LODs, and the dual-layer detector systems had the highest LODs. Conclusion The iodine limit of detection using dual-energy CT systems varied with scanner and phantom size, but all systems depicted iodine in the small and large phantoms at or below 0.3 and 0.5 mg/mL, respectively, and enabled quantification at concentrations of 0.5 and 1.0 mg/mL, respectively. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Hindman in this issue.

Purpose: To assess the non-inferiority of dual-layer spectral detector CT (SDCT) compared to dual-source dual-energy CT (dsDECT) in discriminating uric acid (UA) from non-UA stones., Methods: Fifty-seven extracted urinary calculi were placed in a cylindrical phantom in a water bath and scanned on a SDCT scanner (IQon, Philips Healthcare) and second- and third-generation dsDECT scanners (Somatom Flash and Force, Siemens Healthcare) under matched scan parameters. For SDCT data, conventional images and virtual monoenergetic reconstructions were created. A customized 3D growing region segmentation tool was used to segment each stone on a pixel-by-pixel basis for statistical analysis. Median virtual monoenergetic ratios (VMRs) of 40/200, 62/92, and 62/100 for each stone were recorded. For dsDECT data, dual-energy ratio (DER) for each stone was recorded from vendor-specific postprocessing software (Syngo Via) using the Kidney Stones Application. The clinical reference standard of X-ray diffraction analysis was used to assess non-inferiority. Area under the receiver-operating characteristic curve (AUC) was used to assess diagnostic performance of detecting UA stones., Results: Six pure UA, 47 pure calcium-based, 1 pure cystine, and 3 mixed struvite stones were scanned. All pure UA stones were correctly separated from non-UA stones using SDCT and dsDECT (AUC = 1). For UA stones, median VMR was 0.95-0.99 and DER 1.00-1.02. For non-UA stones, median VMR was 1.4-4.1 and DER 1.39-1.69., Conclusion: SDCT spectral reconstructions demonstrate similar performance to those of dsDECT in discriminating UA from non-UA stones in a phantom model.

Purpose: Spectral detector computed tomography (SDCT) is a new CT technology that uses a dual-layer detector to perform energy separation. We aim to assess 3 clinical concepts using a phantom model: noise profile across the virtual monoenergetic (VME) spectrum, accuracy of iodine quantification, and virtual noncontrast (VNC) reconstructions' ability to remove iodine contribution to attenuation., Methods: Six vials containing varying concentrations of iodinated contrast (0-6 mg/mL) diluted in water were placed in a water bath and scanned on an SDCT scanner. Virtual monoenergetic (40-200 keV at 10-keV increments), iodine-no-water, and VNC reconstructions were created. Attenuation (in Hounsfield units [HU]), VME noise at each energy level, CT-derived iodine concentration, and VNC attenuation were recorded., Results: Virtual monoenergetic noise was improved at all energies compared with conventional images (conventional, 9.8-11.2; VME, 7.5-9.5). Noise profile showed a slightly higher image noise at 40 keV, but was otherwise relatively flat across the energy spectrum. On iodine-no-water reconstructions, measured varied from actual iodine concentration by ±0.1 mg/mL (SD, 0.16-0.36). Virtual noncontrast attenuation was within 5 HU of water attenuation at all iodine concentrations., Conclusion: Reconstructions of SDCT show lower VME image noise, accurate iodine quantification, and VNC attenuation values within 5 HU of expected in a phantom model.

Objective: The purpose of this study was to assess the feasibility of using dual-energy CT to accurately quantify uric acid and non-uric acid components in urinary stones of mixed composition., Materials and Methods: A total of 24 urinary stones were analyzed with micro CT to serve as the reference standard for uric acid and non-uric acid composition. These stones were placed in water phantoms to simulate body attenuation of slim to obese adults and scanned with a third-generation dual-source CT scanner by use of dual-energy modes adaptively selected on the basis of phantom size. CT number ratio, which is distinct for different materials, was calculated for each pixel of the stones. Each pixel was then classified as uric acid and non-uric acid by comparison of the CT number ratio with preset thresholds ranging from 1.10 to 1.70. Minimal, maximal, and root-mean-square errors were calculated by comparing composition with the reference standard, and the threshold with the minimal root-mean-square error was determined. A paired t test was performed to compare the stone composition determined with dual-energy CT with the reference standard obtained with micro CT., Results: The optimal CT number ratio threshold ranged from 1.27 to 1.55, dependent on phantom size. The root-mean-square error ranged from 9.60% to 12.87% across all phantom sizes. Minimal absolute error ranged from 0.04% to 1.24% and maximal absolute error from 22.05% to 35.46%. Dual-energy CT and the reference micro CT did not differ significantly on uric acid and non-uric acid composition (paired t test, p = 0.20-0.96)., Conclusion: Accurate quantification of uric acid and non-uric acid composition in mixed stones is possible with dual-energy CT.

Purpose: Lack of access to projection data from patient CT scans is a major limitation for development and validation of new reconstruction algorithms. To meet this critical need, this work developed and validated a vendor-neutral format for CT projection data, which will further be employed to build a library of patient projection data for public access., Methods: A digital imaging and communication in medicine (DICOM)-like format was created for CT projection data (CT-PD), named the DICOM-CT-PD format. The format stores attenuation information in the DICOM image data block and stores parameters necessary for reconstruction in the DICOM header under various tags (51 tags to store the geometry and scan parameters and 9 tags to store patient information). To validate the accuracy and completeness of the new format, CT projection data from helical scans of the ACR CT accreditation phantom were acquired from two clinical CT scanners (Somatom Definition Flash, Siemens Healthcare, Forchheim, Germany and Discovery CT750 HD, GE Healthcare, Waukesha, WI). After decoding (by the authors for Siemens, by the manufacturer for GE), the projection data were converted to the DICOM-CT-PD format. Off-line CT reconstructions were performed by internal and external reconstruction researchers using only the information stored in the DICOM-CT-PD files and the DICOM-CT-PD field definitions., Results: Compared with the commercially reconstructed CT images, the off-line reconstructed images created using the DICOM-CT-PD format are similar in terms of CT numbers (differences of 5 HU for the bone insert and -9 HU for the air insert), image noise (±1 HU), and low contrast detectability (6 mm rods visible in both). Because of different reconstruction approaches, slightly different in-plane and cross-plane high contrast spatial resolution were obtained compared to those reconstructed on the scanners (axial plane: GE off-line, 7 lp/cm; GE commercial, 7 lp/cm; Siemens off-line, 8 lp/cm; Siemens commercial, 7 lp/cm. Coronal plane: Siemens off-line, 6 lp/cm; Siemens commercial, 8 lp/cm)., Conclusions: A vendor-neutral extended DICOM format has been developed that enables open sharing of CT projection data from third-generation CT scanners. Validation of the format showed that the geometric parameters and attenuation information in the DICOM-CT-PD file were correctly stored, could be retrieved with use of the provided instructions, and contained sufficient data for reconstruction of CT images that approximated those from the commercial scanner.

Objective: The purpose of this phantom study was to determine the utility of a third-generation dual-source CT scanner with increased dual-energy spectral separation in differentiating urinary stone composition., Materials and Methods: Eighty-seven urinary stones from humans were scanned in 35-, 40-, 45-, and 50-cm wide anthropomorphic phantoms with a third-generation dual-source scanner (system A) with a high-energy beam of 150 kV plus 0.6-mm tin filtration (Sn). The low-energy data were acquired at 70, 80, 90, and 100 kV. A second-generation dual-source scanner (system B) was used to acquire data at 140 kV plus 0.4-mm Sn for the high-energy and 80 or 100 kV for the low-energy images. Volume CT dose index was matched for a given phantom size. CT number ratios were calculated and used to differentiate uric acid from non-uric acid stones and oxalate from apatite stones in an ROC analysis., Results: The area under the curve (AUC) of the ROC curve for uric acid versus non-uric acid stones increased for large phantoms. For example, for imaging of the 45-cm wide phantom with system A at the 100- and 150-kV Sn low- and high-energy combination, the AUC was 0.99, whereas for system B at the 100- and 140-kV Sn combination, the AUC was 0.86. At each phantom size and for all energy combinations, the AUC values for oxalate versus apatite stones were higher for system A than they were for any energy combination for system B., Conclusion: Compared with use of second-generation dual-source CT, use of third-generation dual-source CT at the energy combination of 100 and 150 kV Sn improved classification of urinary stones across a wide range of phantom sizes and increased the ability to differentiate oxalate from apatite stones.

Through this investigation we developed a methodology to evaluate and standardize CT image quality from routine abdomen protocols across different manufacturers and models. The influence of manufacturer-specific automated exposure control systems on image quality was directly assessed to standardize performance across a range of patient sizes. We evaluated 16 CT scanners across our health system, including Siemens, GE, and Toshiba models. Using each practice's routine abdomen protocol, we measured spatial resolution, image noise, and scanner radiation output (CTDIvol). Axial and in-plane spatial resolutions were assessed through slice sensitivity profile (SSP) and modulation transfer function (MTF) measurements, respectively. Image noise and CTDIvol values were obtained for three different phantom sizes. SSP measurements demonstrated a bimodal distribution in slice widths: an average of 6.2  ±  0.2 mm using GE's 'Plus' mode reconstruction setting and 5.0  ±  0.1 mm for all other scanners. MTF curves were similar for all scanners. Average spatial frequencies at 50%, 10%, and 2% MTF values were 3.24  ±  0.37, 6.20  ±  0.34, and 7.84  ±  0.70 lp cm(-1), respectively. For all phantom sizes, image noise and CTDIvol varied considerably: 6.5-13.3 HU (noise) and 4.8-13.3 mGy (CTDIvol) for the smallest phantom; 9.1-18.4 HU and 9.3-28.8 mGy for the medium phantom; and 7.8-23.4 HU and 16.0-48.1 mGy for the largest phantom. Using these measurements and benchmark SSP, MTF, and image noise targets, CT image quality can be standardized across a range of patient sizes.

Purpose: The objective of this work was to investigate focal spot blooming effects on the spatial resolution of CT images and to evaluate an x-ray tube that uses dynamic focal spot control for minimizing focal spot blooming., Methods: The influence of increasing tube current at a fixed tube potential of 80 kV on high-contrast spatial resolution of seven different CT scanner models (scanners A-G), including one scanner that uses dynamic focal spot control to reduce focal spot blooming (scanner A), was evaluated. Spatial resolution was assessed using a wire phantom for the modulation transfer function (MTF) calculation and a copper disc phantom for measuring the slice sensitivity profile (SSP). The impact of varying the tube potential was investigated on two scanner models (scanners A and B) by measuring the MTF and SSP and also by using the resolution bar pattern module of the ACR CT phantom. The phantoms were scanned at 70-150 kV on scanner A and 80-140 kV on scanner B, with tube currents from 100 mA up to the maximum tube current available on each scanner. The images were reconstructed using a slice thickness of 0.6 mm with both smooth and sharp kernels. Additionally, focal spot size at varying tube potentials and currents was directly measured using pinhole and slit camera techniques., Results: Evaluation of the MTF and SSP data from the 7 CT scanner models evaluated demonstrated decreased focal spot blooming for newer scanners, as evidenced by decreasing deviations in MTF and SSP as tube current varied. For scanners A and B, where focal spot blooming effects as a function of tube potential were assessed, the spatial resolution variation in the axial plane was much smaller on scanner A compared to scanner B as tube potential and current changed. On scanner A, the 50% MTF never decreased by more than 2% from the 50% MTF measured at 100 mA. On scanner B, the 50% MTF decreased by as much as 19% from the 50% MTF measured at 100 mA. Assessments of the SSP, the bar patterns in the ACR phantom and the pinhole and slit camera measurements were consistent with the MTF calculations., Conclusions: Focal spot blooming has a noticeable effect on spatial resolution in CT imaging. The focal spot shaping technology of scanner A greatly reduced blooming effects.

Objective: The purpose of this article is to assess the effect of radiation dose reduction in dual-energy CT (DECT) on the performance of renal stone characterization using a patient cohort., Materials and Methods: CT data from 39 unenhanced DECT examinations performed for stone characterization were retrospectively analyzed in this study. Reduced-dose images were simulated at 75%, 50%, and 25% of the routine dose using a previously validated noise-insertion algorithm. Differentiation between uric acid (UA) and non-UA stones was performed using a fixed cutoff value for the dual-energy ratio. ROC analysis was performed to determine optimal cutoff values and the associated sensitivity and specificity., Results: Of the 206 stones found, 43 were UA and 163 were non-UA. The mean (± SD) volume CT dose index (CTDIvol) was 16.0 ± 4.0 mGy at the 100% dose level. The mean noise in 100-kV images increased from 40.9 ± 6.8 HU at 100% dose to 46.8 ± 8.8 HU, 57.7 ± 12.5 HU, and 85.4 ± 22.9 HU at 75%, 50%, and 25% dose levels, respectively. Using the default cutoff value, for stones 10 mm(3) or larger, the sensitivity/specificity were 100.0%/98.8%, 82.8%/98.8%, and 89.3%/98.7%, at 75%, 50%, and 25% dose levels, respectively. ROC analysis showed varying optimal cutoff values at different dose levels. The sensitivity and specificity improved with use of these optimal cutoff values. Differentiation capability decreased for stones smaller than 10 mm(3)., Conclusion: At 75% of the 16-mGy routine dose, the sensitivity and specificity for differentiating UA from non-UA stones were minimally affected for stones 10 mm(3) or larger. The use of optimal cutoff values for dual-energy ratio as dose decreased (and noise increased) provided improved performance.

Purpose: To develop software to automatically calculate size-specific dose estimates (SSDEs) and to assess the effect of variations in water-equivalent diameter (Dw) along the z-axis on SSDE for computed tomographic (CT) examinations of the torso., Materials and Methods: In this institutional review board-approved, HIPAA-compliant, retrospective study, a software program was used to calculate Dw at each image position in 102 consecutive CT examinations of the combined chest, abdomen, and pelvis. SSDE was calculated by multiplying the size-dependent conversion factor and volume CT dose index (CTDIvol) at each image position. The variations in Dw along the z-axis were determined for six hypothetical scanning ranges: chest alone; abdomen alone; pelvis alone; chest and abdomen; abdomen and pelvis; and chest, abdomen, and pelvis. Mean SSDE was calculated in two ways: (a) from the SSDE at each position and (b) from the mean CTDIvol over each scan range and the conversion factor corresponding to Dw at the middle of the scan range. Linear regression analysis was performed to determine the correlation between SSDE values calculated in these two ways., Results: Across patients, for scan ranges 1-6, the mean of the difference between maximal and minimal Dw within a given patient was 5.2, 4.9, 2.5, 6.0, 5.6, and 6.5 cm, respectively. The mean SSDE values calculated by using the two methods were in close agreement, with root mean square differences of 0.9, 0.5, 0.5, 1.4, 1.0, and 1.1 mGy or 6%, 3%, 2%, 9%, 4%, and 6%, for the scan ranges of chest; abdomen; pelvis; chest and abdomen; abdomen and pelvis; and chest, abdomen, and pelvis, respectively., Conclusion: Using the mean CTDIvol from the whole scan range and Dw from the image at the center of the scan range provided an easily obtained estimate of SSDE for the whole scan range that agreed well with values from an image-by-image approach, with a root mean square difference less than 1.4 mGy (9%).

Rationale and Objectives: Nephrosclerosis occurs with aging and is characterized by increased kidney subcapsular surface irregularities at autopsy. Assessments of cortical roughness in vivo could provide an important measure of nephrosclerosis. The purpose of this study was to develop and validate an image-processing algorithm for quantifying renal cortical surface roughness in vivo and determine its association with age., Materials and Methods: Renal cortical surface roughness was measured on contrast-enhanced abdominal computed tomography (CT) images of potential living kidney donors. A roughness index was calculated based on geometric curvature of each kidney from three-dimensional images and compared to visual observation scores. Cortical roughness was compared between the oldest and youngest donors, and its interaction with cortical volume and age assessed., Results: The developed quantitative roughness index identified significant differences in kidneys with visual surface roughness scores of 0 (minimal), 1 (mild), and 2 (moderate; P < .001) in a random sample of 200 potential kidney donors. Cortical roughness was significantly higher in the 94 oldest (64-75 years) versus 91 youngest (18-25 years) potential kidney donors (P < .001). Lower cortical volume was associated with older age but not with roughness (r = -0.03, P = .75). The association of oldest age group with roughness (odds ratio [OR] = 1.8 per standard deviation [SD] of roughness index) remained significant after adjustment for total cortex volume (OR = 2.0 per SD of roughness index)., Conclusions: A new algorithm to measure renal cortical surface roughness from CT scans detected rougher surface in older compared to younger kidneys, independent of cortical volume loss. This novel index may allow quantitative evaluation of nephrosclerosis in vivo using contrast-enhanced CT., (Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.)

Chest computed tomography (CT) is a useful tool for diagnosing various thoracic conditions and has become the diagnostic imaging modality of choice for many diseases. Recent discussions about the radiation dose from CT have attracted the attention of medical professionals and the general public. This article discusses radiation dose with an emphasis on chest CT and on current techniques for patient dose reduction. Four techniques are discussed in detail, including tube current modulation, automatic exposure control, automatic tube voltage selection, and iterative image reconstruction. Adopting these techniques in routine clinical practice can dramatically reduce radiation dose levels., (Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.)

Objective: The objective of our study was to evaluate in phantoms the differences in CT image noise and artifact level between two types of commercial CT detectors: one with distributed electronics (conventional) and one with integrated electronics intended to decrease system electronic noise., Materials and Methods: Cylindric water phantoms of 20, 30, and 40 cm in diameter were scanned using two CT scanners, one equipped with integrated detector electronics and one with distributed detector electronics. All other scanning parameters were identical. Scans were acquired at four tube potentials and 10 tube currents. Semianthropomorphic phantoms were scanned to mimic the shoulder and abdominal regions. Images of two patients were also selected to show the clinical values of the integrated detector., Results: Reduction of image noise with the integrated detector depended on phantom size, tube potential, and tube current. Scans that had low detected signal had the greatest reductions in noise, up to 40% for a 30-cm phantom scanned using 80 kV. This noise reduction translated into up to 50% in dose reduction to achieve equivalent image noise. Streak artifacts through regions of high attenuation were reduced by up to 45% on scans obtained using the integrated detector. Patient images also showed superior image quality for the integrated detector., Conclusion: For the same applied radiation level, the use of integrated electronics in a CT detector showed a substantially reduced level of electronic noise, resulting in reductions in image noise and artifacts, compared with detectors having distributed electronics.

Purpose: We differentiated calcium oxalate monohydrate and calcium oxalate dihydrate kidney stones using micro and clinical computerized tomography images., Materials and Methods: A total of 22 calcium oxalate monohydrate and 15 calcium oxalate dihydrate human kidney stones were scanned using a commercial micro-computerized tomography scanner with a pixel size of 7 to 23 μm. Under an institutional review board approved protocol, image data on 10 calcium oxalate monohydrate and 9 calcium oxalate dihydrate stones greater than 5 mm were retrieved from a total of 80 patients who underwent clinical dual energy computerized tomography for clinical indications and had stones available for infrared spectroscopic compositional analysis. Micro and clinical computerized tomography images were processed using in-house software, which quantified stone surface morphology with curvature based calculations. A shape index was generated as a quantitative shape metric to differentiate calcium oxalate monohydrate from calcium oxalate dihydrate stones. Statistical tests were used to test the performance of the shape index., Results: On micro-computerized tomography images the shape index of calcium oxalate monohydrate and calcium oxalate dihydrate stones significantly differed (ROC curve AUC 0.92, p <0.0001). At the optimal cutoff sensitivity was 0.93 and specificity was 0.91. On clinical computerized tomography images a significant morphological difference was also detected (p = 0.007). AUC, sensitivity and specificity were 0.90, 1 and 0.73, respectively., Conclusions: On micro and clinical computerized tomography images a morphological difference was detectable in calcium oxalate monohydrate and calcium oxalate dihydrate stones larger than 5 mm. The shape index is a highly promising method that can distinguish calcium oxalate monohydrate and calcium oxalate dihydrate stones with reasonable accuracy., (Copyright © 2013 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.)

Objective: To evaluate the ability of 100/Sn140 kV (Sn, tin filter) dual-energy computed tomography (CT) to differentiate urinary stone types in a patient cohort with a wide range of body sizes., Methods: Eighty human urinary stones were categorised into four groups (uric acid; cystine; struvite, oxalate and brushite together; and apatite) and imaged in 30-50-cm-wide water tanks using clinical 100/Sn140 kV protocols. The CT number ratio (CTR) between the low- and high-energy images was calculated. Thresholds for differentiating between stone groups were determined using receiver operating characteristics (ROC) analysis. Additionally, 86 stones from 66 patients were characterised using the size-adaptive CTR thresholds determined in the phantom study., Results: In phantoms, the area under the ROC curve for differentiating between stone groups ranged from 0.71 to 1.00, depending on phantom size. In patients, body width ranged from 28.5 to 50.0 cm, and 79.1 % of stones were correctly characterised. Sensitivity and specificity for correctly identifying the stone category were 100 % and 100 % (group 1), 100 % and 95.3 % (group 2), 85.7 % and 60.9 % (group 3), and 52.6 % and 92.5 % (group 4)., Conclusion: Dual-energy CT can provide in vivo urinary stone characterisation for patients over a wide range of body sizes., Key Points: • Dual-energy CT helps assessment of urinary stone composition in vivo. • 100/Sn140 kV DECT differentiates among four stone types with 79.1 % accuracy. • In vivo diagnostic test achievable in patients with many body sizes.

Objective: To develop a dual-energy CT (DECT) method for differentiating uric acid (UA) from non-UA stones in the presence of iodinated contrast medium., Methods: Thirty UA and 45 non-UA stones were selected after infra-red spectroscopic analysis and independently placed in a 1.5-ml vial, which was filled first with saline and then with increasing concentrations of iodine. For each condition, tubes were put in a 35-cm water phantom and examined using a dual-source CT system at 100 and 140 kV. Virtual unenhanced images created from CT data sets of the stones in iodine-containing solutions provided position and volume information. This map was used to calculate a CT number ratio to differentiate stone type. A region-growing method was developed to improve the ability to differentiate between UA and non-UA stones with iodinated contrast medium., Results: The sensitivity for detecting UA stones was 100 % for unenhanced images but fell to 18 % with 20 mgI/ml iodine solution and 0 % for higher concentrations. With region growing, the sensitivity for detecting UA stones was increased to 100 %, 82 %, 57 %, 50 % and 21 % for iodine solutions of 20, 40, 60, 80 and 100 mgI/ml., Conclusion: The region-growing method improves differentiation of UA from non-UA stones on contrast-enhanced DECT urograms., Key Points: Computed tomography is widely used to assess renal tract calculi. Dual-energy CT can assess stone composition and provide virtual unenhanced images. However, iodinated contrast medium affects the volume estimation for urinary stones. CTR of stones is altered by the surrounding iodine in CT urograms. The region-growing method improves the identification of uric acid stones.

Purpose: To estimate attenuation using cross sectional CT images and scanned projection radiograph (SPR) images in a series of thorax and abdomen phantoms., Methods: Attenuation was quantified in terms of a water cylinder with cross sectional area of A(w) from both the CT and SPR images of abdomen and thorax phantoms, where A(w) is the area of a water cylinder that would absorb the same dose as the specified phantom. SPR and axial CT images were acquired using a dual-source CT scanner operated at 120 kV in single-source mode. To use the SPR image for estimating A(w), the pixel values of a SPR image were calibrated to physical water attenuation using a series of water phantoms. A(w) and the corresponding diameter D(w) were calculated using the derived attenuation-based methods (from either CT or SPR image). A(w) was also calculated using only geometrical dimensions of the phantoms (anterior-posterior and lateral dimensions or cross sectional area)., Results: For abdomen phantoms, the geometry-based and attenuation-based methods gave similar results for D(w). Using only geometric parameters, an overestimation of D(w) ranging from 4.3% to 21.5% was found for thorax phantoms. Results for D(w) using the CT image and SPR based methods agreed with each other within 4% on average in both thorax and abdomen phantoms., Conclusions: Either the cross sectional CT or SPR images can be used to estimate patient attenuation in CT. Both are more accurate than use of only geometrical information for the task of quantifying patient attenuation. The SPR based method requires calibration of SPR pixel values to physical water attenuation and this calibration would be best performed by the scanner manufacturer.

Purpose: For the purpose of size-specific dose estimation, information regarding patient attenuation is required. The purpose of this work is to describe a method for measuring patient attenuation and expressing the results in terms of a water cylinder, with cross sectional area A(w), which would absorb the same average dose as the irradiated patient. The ability to calculate A(w) directly from the CT image was validated with Monte Carlo simulations and an analytical model., Methods: A series of virtual cylinders were created with diameters ranging from 10 to 40 cm and lengths of 40 cm. The cylinders were given an atomic number equal to that of water; the density of the cylinders was varied from 0.26 to 1.2 g∕cm(3). The average dose to the cylinders from an axial scan at the longitudinal center position was calculated using Monte Carlo simulation and an analytical model. The relationship between phantom cross sectional area and calculated dose was determined for each density value to determine the dependence of A(w) on object attenuation. In addition, A(w) was estimated from the virtual CT images based on two derived models expressing the potential dependence of A(w) on object attenuation, one model assuming a linear dependence and the other assuming a quadratic dependence. Model results were compared with those from the Monte Carlo simulation and the analytical dose calculation approach. Virtual thorax and abdomen phantoms of adult and pediatric sizes were created, and A(w) was estimated using geometrical size parameters or the derived models. The accuracy of each approach for estimating A(w) was determined by comparing the average dose to the virtual phantom calculated using Monte Carlo simulation to the average dose to a water equivalent phantom of cross sectional area A(w)., Results: In the absence of a bowtie filter, both the Monte Carlo simulation and analytical model showed that (A(w)∕A) had a quadratic dependence on (μ∕μ(w)). However, including a bowtie filter in the Monte Carlo simulation altered the relationship, such that A(w)∕A was linearly dependent on μ∕μ(w). Using this relationship, the dose absorbed by a water cylinder of area A(w) agreed with the dose absorbed by adult and pediatric, thorax and abdomen phantoms to within 6% (mean difference = 0.5 ± 4.8%). Estimates of A(w) (or the water equivalent diameter D(w)) using only anterior-posterior and lateral phantom dimensions led to dose estimates that agreed with Monte Carlo-derived dose values within 3% and 6% for the abdomen adult and pediatric phantoms, respectively. However, because of density differences between lung and tissue, larger differences in dose relative to Monte Carlo-derived values were observed in the thorax adult and pediatric phantoms (15% and 11%, respectively) when only geometrical parameters were used to estimate D(w)., Conclusions: Patient attenuation can be quantified in terms of the diameter of a water cylinder that absorbs same average dose as the irradiated cross section of the patient. The linear dependence of A(w) on object attenuation makes it straightforward to calculate A(w) from a CT image on most operator consoles or clinical workstations.

Purpose: We propose a method to improve the accuracy of volume estimation of kidney stones from computerized tomography images., Materials and Methods: The proposed method consisted of 2 steps. A threshold equal to the average of the computerized tomography number of the object and the background was first applied to determine full width at half maximum volume. Correction factors were then applied, which were precalculated based on a model of a sphere and a 3-dimensional gaussian point spread function. The point spread function was measured in a computerized tomography scanner to represent the response of the scanner to a point-like object. Method accuracy was validated using 6 small cylindrical phantoms with 2 volumes of 21.87 and 99.9 mm(3), and 3 attenuations, respectively, and 76 kidney stones with a volume range of 6.3 to 317.4 mm(3). Volumes estimated by the proposed method were compared with full width at half maximum volumes., Results: The proposed method was significantly more accurate than full width at half maximum volume (p <0.0001). The magnitude of improvement depended on stone volume with smaller stones benefiting more from the method. For kidney stones 10 to 20 mm(3) in volume the average improvement in accuracy was the greatest at 19.6%., Conclusions: The proposed method achieved significantly improved accuracy compared with threshold methods. This may lead to more accurate stone management., (Copyright © 2012 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.)

Purpose: To compare the dose and image quality of three methods for reducing the radiation dose to the eye at head computed tomography (CT): bismuth shielding, organ-based tube current modulation (TCM), and global reduction of the tube current., Materials and Methods: An anthropomorphic head phantom was scanned under six conditions: (a) without any dose reduction techniques (reference scanning); (b) with one bismuth eye shield; (c) with organ-based TCM; (d) with reduced tube current to yield the same dose reduction as one bismuth shield; (e) with two layers of bismuth shields; and (f) with organ-based TCM and one bismuth shield. Dose to the eye, image noise, and CT numbers in the brain region were measured and compared. The effect of increasing distance between the bismuth shield and eye lens was also investigated., Results: Relative to the reference scan, the dose to the eye was reduced by 26.4% with one bismuth shield, 30.4% with organ-based TCM, and 30.2% with a global reduction in tube current. A combination of organ-based TCM with one bismuth shield reduced the dose by 47.0%. Image noise in the brain region was slightly increased for all dose reduction methods. CT numbers were increased whenever the bismuth shield was used. Increasing the distance between the bismuth shield and the eye lens helped reduce CT number errors, but the increase in noise remained., Conclusion: Organ-based TCM provided superior image quality to that with bismuth shielding while similarly reducing dose to the eye. Simply reducing tube current globally by about 30% provides the same dose reduction to the eye as bismuth shielding; however, CT number accuracy is maintained and dose is reduced to all parts of the head., (© RSNA, 2012.)

Iron deposits secondary to microbleeds often co-exist with calcium in coronary plaques. The purpose of this study was to quantify iron in the presence of calcium in an ex vivo porcine arterial plaque model using a clinical dual-energy CT (DECT) scanner. A material decomposition method to quantify the mass fractions of iron and calcium within a mixture using DECT was developed. Mixture solutions of known iron and calcium concentrations were prepared to calibrate and validate the DECT-based algorithm. Simulated plaques with co-existing iron and calcium were created by injecting the mixture solutions into the vessel wall of porcine carotid arteries and aortas. These vessel regions were harvested and scanned using a clinical DECT system and iron mass fraction was calculated for each sample. Iron- and calcium-specific staining was conducted on 5 µm thick histological sections of vessel samples to confirm the co-existence of iron and calcium in the simulated plaques. The proposed algorithm accurately quantified iron and calcium amounts in mixture solutions. Maps of iron mass fraction of 60 artery segments were obtained from CT images at two energies. The sensitivity for detecting the presence of iron was 83% and the specificity was 92% using a threshold at an iron mass fraction of 0.25%. Histological analysis confirmed the co-localization of iron and calcium within the simulated plaques. Iron quantification in the presence of calcium was feasible in excised arteries at an iron mass fraction of around 1.5% or higher using current clinical DECT scanners.

Purpose: The purpose of this work was to evaluate dose performance and image quality in thoracic CT using three techniques to reduce dose to the breast: bismuth shielding, organ-based tube current modulation (TCM) and global tube current reduction., Methods: Semi-anthropomorphic thorax phantoms of four different sizes (15, 30, 35, and 40 cm lateral width) were used for dose measurement and image quality assessment. Four scans were performed on each phantom using 100 or 120 kV with a clinical CT scanner: (1) reference scan; (2) scan with bismuth breast shield of an appropriate thickness; (3) scan with organ-based TCM; and (4) scan with a global reduction in tube current chosen to match the dose reduction from bismuth shielding. Dose to the breast was measured with an ion chamber on the surface of the phantom. Image quality was evaluated by measuring the mean and standard deviation of CT numbers within the lung and heart regions., Results: Compared to the reference scan, dose to the breast region was decreased by about 21% for the 15-cm phantom with a pediatric (2-ply) shield and by about 37% for the 30, 35, and 40-cm phantoms with adult (4-ply) shields. Organ-based TCM decreased the dose by 12% for the 15-cm phantom, and 34-39% for the 30, 35, and 40-cm phantoms. Global lowering of the tube current reduced breast dose by 23% for the 15-cm phantom and 39% for the 30, 35, and 40-cm phantoms. In phantoms of all four sizes, image noise was increased in both the lung and heart regions with bismuth shielding. No significant increase in noise was observed with organ-based TCM. Decreasing tube current globally led to similar noise increases as bismuth shielding. Streak and beam hardening artifacts, and a resulting artifactual increase in CT numbers, were observed for scans with bismuth shields, but not for organ-based TCM or global tube current reduction., Conclusions: Organ-based TCM produces dose reduction to the breast similar to that achieved with bismuth shielding for both pediatric and adult phantoms. However, organ-based TCM does not affect image noise or CT number accuracy, both of which are adversely affected by bismuth shielding. Alternatively, globally decreasing the tube current can produce the same dose reduction to the breast as bismuth shielding, with a similar noise increase, yet without the streak artifacts and CT number errors caused by the bismuth shields. Moreover, globally decreasing the tube current reduces the dose to all tissues scanned, not simply to the breast.

Objective: The purpose of this study was to evaluate in phantoms the dose reduction to anterior surfaces and image quality with organ-based tube-current modulation in head and thoracic CT., Materials and Methods: Organ-based tube-current modulation is designed to reduce radiation dose to superficial radiosensitive organs, such as the lens of the eye, thyroid, and breast, by decreasing the tube current when the tube passes closest to these organs. Dose and image quality were evaluated in phantoms for clinical head and thorax examination protocols with and without organ-based tube-current modulation. Surface dose reduction as a function of position was measured using a 32-cm CT dose index (CTDI) phantom, an anthropomorphic adult phantom, and ion chambers. Surface dose reduction as a function of patient size was investigated using three semianthropomorphic phantoms with posteroanterior dimensions of 14, 25, and 31 cm. Image noise (the SD of CT numbers in regions of interest) was evaluated for the anthropomorphic and the semianthropomorphic phantoms., Results: For equivalent scanner output (volume CTDI), the dose to the midline of the anterior surface was reduced by 27-50%, depending on the anatomic region (head or thorax) and phantom size, and the dose to the posterior surface was correspondingly increased. Image noise was not significantly different between scans with and without organ-based tube-current modulation (p = 0.85)., Conclusion: Organ-based tube-current modulation can reduce the dose to the anterior surface of patients without increasing image noise by commensurately increasing the dose to the posterior surface. This technique can reduce the dose to anterior radiosensitive organs for head and thoracic CT scans.

Purpose: In diagnostic CT imaging, multiple important applications depend on the knowledge of the x-ray spectrum, including Monte Carlo dose calculations and dual-energy material decomposition analysis. Due to the high photon flux involved, it is difficult to directly measure spectra from the x-ray tube of a CT scanner. One potential method for indirect measurement involves estimating the spectrum from transmission measurements. The expectation maximization (EM) method is an accurate and robust method to solve this problem. In this article, this method was evaluated in a commercial CT scanner., Methods: Two step-wedges (polycarbonate and aluminum) were used to produce different attenuation levels. Transmission measurements were performed on the scanner and the measured data from the scanner were exported to an external computer to calculate the spectra. The EM method was applied to solve the equations that represent the attenuation processes of polychromatic x-ray photons. Estimated spectra were compared to the spectra simulated using a software provided by the manufacturer of the scanner. To test the accuracy of the spectra, a verification experiment was performed using a phantom containing different depths of water. The measured transmission data were compared to the transmission values calculated using the estimated spectra., Results: Spectra of 80, 100, 120, and 140 kVp from a dual-source CT scanner were estimated. The estimated and simulated spectra were well matched. The differences of mean energies were less than 1 keV. In the verification experiment, the measured and calculated transmission values were in excellent agreement., Conclusions: Spectrum estimation using transmission data and the EM method is a quantitatively accurate and robust technique to estimate the spectrum of a CT system. This method could benefit studies relying on accurate knowledge of the x-ray spectra from CT scanner.