Lifelike PixelPrint phantoms for assessing clinical image quality and dose reduction capabilities of a deep learning CT reconstruction algorithm.

Deep learning CT reconstruction (DLR) has become increasingly popular as a method for improving image quality and reducing radiation exposure. Due to their nonlinear nature, these algorithms result in resolution and noise performance which are object-dependent. Therefore, traditional CT phantoms, which lack realistic tissue morphology, have become inadequate for assessing clinical imaging performance. We propose to utilize 3D-printed PixelPrint phantoms, which exhibit lifelike attenuation profiles, textures, and structures, as a better tool for evaluating DLR performance. In this study, we evaluate a DLR algorithm (Precise Image (PI), Philips Healthcare) using a custom PixelPrint lung phantom and perform head-to-head comparisons between DLR, iterative reconstruction, and filtered back projection (FBP) with scans acquired at a broad range of radiation exposures (CTDIvol: 0.5, 1, 2, 4, 6, 9, 12, 15, 19, and 20 mGy). We compared the performance of each resultant image using noise, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature-based similarity index (FSIM), information theoretic-based statistic similarity measure (ISSM) and universal image quality index (UIQ). Iterative reconstruction at 9 mGy matches the image quality of FBP at 12 mGy (diagnostic reference level) for all metrics, demonstrating a dose reduction capability of 25%. Meanwhile, DLR matches the image quality of diagnostic reference level FBP images at doses between 4 - 9 mGy, demonstrating dose reduction capabilities between 25% and 67%. This study shows that DLR allows for reduced radiation dose compared to both FBP and iterative reconstruction without compromising image quality. Furthermore, PixelPrint phantoms offer more realistic testing conditions compared to traditional phantoms in the evaluation of novel CT technologies. This, in turn, promotes the translation of new technologies, such as DLR, into clinical practice.

Impact of scatter radiation on spectral quantification performance of first- and second-generation dual-layer spectral computed tomography.

OBJECTIVE: To assess the impact of scatter radiation on quantitative performance of first and second-generation dual-layer spectral computed tomography (DLCT) systems. METHOD: A phantom with two iodine inserts (1 and 2 mg/mL) configured to intentionally introduce high scattering conditions was scanned with a first- and second-generation DLCT. Collimation widths (maximum of 4 cm for first generation and 8 cm for second generation) and radiation dose levels were varied. To evaluate the performance of both systems, the mean CT numbers of virtual monoenergetic images (MonoEs) at different energies were calculated and compared to expected values. MonoEs at 50  versus 150 keV were plotted to assess material characterization of both DLCTs. Additionally, iodine concentrations were determined, plotted, and compared against expected values. For each experimental scenario, absolute errors were reported. RESULTS: An experimental setup, including a phantom design, was successfully implemented to simulate high scatter radiation imaging conditions. Both CT scanners illustrated high spectral accuracy for small collimation widths (1 and 2 cm). With increased collimation (4 cm), the second-generation DLCT outperformed the earlier DLCT system. Further, the spectral performance of the second-generation DLCT at an 8 cm collimation width was comparable to a 4 cm collimation on the first-generation DLCT. A comparison of the absolute errors between both systems at lower energy MonoEs illustrates that, for the same acquisition parameters, the second-generation DLCT generated results with decreased errors. Similarly, the maximum error in iodine quantification was less with second-generation DLCT (0.45  and 0.33 mg/mL for the first and second-generation DLCT, respectively). CONCLUSION: The implementation of a two-dimensional anti-scatter grid in the second-generation DLCT improves the spectral quantification performance. In the clinical routine, this improvement may enable additional clinical benefits, for example, in lung imaging.

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm.

Objective. Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels.Method. The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different size extension rings to mimic a small- and medium-sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error, structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image.Results.DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25%-83% in the small phantom and by 50%-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR.Conclusion. DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose, which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.

Diagnostic accuracy of virtual non-contrast CT for aortic valve stenosis severity evaluation.

BACKGROUND: Computed tomography aortic valve calcium (AVC) score has accepted value for diagnosing and predicting outcomes in aortic stenosis (AS). Multi-energy CT (MECT) allows virtual non-contrast (VNC) reconstructions from contrast scans. We aim to compare the VNC-AVC score to the true non-contrast (TNC)-AVC score for assessing AS severity. METHODS: We prospectively included patients undergoing a MECT for transcatheter aortic valve replacement (TAVR) planning. TNC-AVC was acquired before contrast, and VNC-AVC was derived from a retrospectively gated contrast-enhanced scan. The Agatston scoring method was used for quantification, and linear regression analysis to derive adjusted-VNC values. RESULTS: Among 109 patients (55% female) included, 43% had concordant severe and 14% concordant moderate AS. TNC scan median dose-length product was 116 â€‹mGy∗cm. The median TNC-AVC was 2,107 AU (1,093-3,372), while VNC-AVC was 1,835 AU (1293-2,972) after applying the coefficient (1.46) and constant (743) terms. A strong correlation was demonstrated between methods (r â€‹= â€‹0.93; p â€‹< â€‹0.001). Using accepted thresholds (>1,300 AU for women and >2,000 AU for men), 65% (n â€‹= â€‹71) of patients had severe AS by TNC-AVC and 67% (n â€‹= â€‹73) by adjusted-VNC-AVC. After estimating thresholds for adjusted-VNC (>1,564 AU for women and >2,375 AU for men), 56% (n â€‹= â€‹61) had severe AS, demonstrating substantial agreement with TNC-AVC (κ â€‹= â€‹0.77). CONCLUSIONS: MECT-derived VNC-AVC showed a strong correlation with TNC-AVC. After adjustment, VNC-AVC demonstrated substantial agreement with TNC-AVC, potentially eliminating the requirement for an additional scan and enabling reductions in both radiation exposure and acquisition time.

Spectral performance evaluation of a second-generation spectral detector CT.

PURPOSE: The aim of this study was to characterize a second-generation wide-detector dual-layer spectral computed tomography (CT) system for material quantification accuracy, acquisition parameter and patient size dependencies, and tissue characterization capabilities. METHODS: A phantom with multiple tissue-mimicking and material-specific inserts was scanned with a dual-layer spectral detector CT using different tube voltages, collimation widths, radiation dose levels, and size configurations. Accuracy of iodine density maps and virtual monoenergetic images (MonoE) were investigated. Additionally, differences between conventional and MonoE 70 keV images were calculated to evaluate acquisition parameter and patient size dependencies. To demonstrate material quantification and differentiation, liver-mimicking inserts with adipose and iron were analyzed with a two-base decomposition utilizing MonoE 50 and 150 keV, and root mean square error (RMSE) for adipose and iron content was reported. RESULTS: Measured inserts exhibited quantitative accuracy across a wide range of MonoE levels. MonoE 70 keV images demonstrated reduced dependence compared to conventional images for phantom size (1 vs. 27 HU) and acquisition parameters, particularly tube voltage (4 vs. 37 HU). Iodine density quantification was successful with errors ranging from -0.58 to 0.44 mg/mL. Similarly, inserts with different amounts of adipose and iron were differentiated, and the small deviation in values within inserts corresponded to a RMSE of 3.49 ± 1.76% and 1.67 ± 0.84 mg/mL for adipose and iron content, respectively. CONCLUSION: The second-generation dual-layer CT enables acquisition of quantitatively accurate spectral data without compromises from differences in patient size and acquisition parameters.

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm.

Objective: Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels. Approach: The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different sized extension rings to mimic a small and medium sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error (RMSE), structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image. Main Results: DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25-83% in the small phantom and by 50-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR with a non-anatomical physics phantom. Significance: DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.

Phantom-based quantification of the spectral accuracy in dual-layer spectral CT for pediatric imaging at 100 kVp.

Background: To determine the spectral accuracy in detector-based dual-energy CT (DECT) at 100 kVp and wide (8 cm) collimation width for dose levels and object sizes relevant to pediatric imaging. Methods: A spectral CT phantom containing tissue-equivalent materials and iodine inserts of varying concentrations was scanned on the latest generation detector-based DECT system. Two 3D-printed extension rings were used to mimic varying pediatric patient sizes. Scans were performed at 100 and 120 kVp, 4 and 8 cm collimation widths, and progressively reduced radiation dose levels, down to 0.9 mGy CTDIvol. Virtual mono-energetic, iodine density, effective atomic number, and electron density results were quantified and compared to their expected values for all acquisition settings and phantom sizes. Results: DECT scans at 100 kVp provided highly accurate spectral results; however, a size dependence was observed for iodine quantification. For the medium phantom configuration (15 cm diameter), measurement errors in iodine density, effective atomic number, and electron density (ED) were below 0.3 mg/mL, 0.2 and 1.8 %EDwater, respectively. The average accuracy was slightly different from scans at 120 kVp; however, not statistically significant for all configurations. Collimation width had no substantial impact. Spectral results were accurate and reliable for radiation exposures down to 0.9 mGy CTDIvol. Conclusions: Detector-based DECT at 100 kVp can provide on-demand or retrospective spectral information with high accuracy even at extremely low doses, thereby making it an attractive solution for pediatric imaging.

Virtual Noncontrast Images From Portal Venous Phase Spectral-Detector CT Acquisitions for Adrenal Lesion Characterization.

OBJECTIVE: The aim of this study was to investigate if Hounsfield unit (HU) values from virtual noncontrast (VNC) images derived from portal venous phase spectral-detector computed tomography can help to differentiate adrenal adenomas and metastases. METHODS: Spectral-detector computed tomography datasets of 33 patients with presence of adrenal lesions and standard of reference for lesion origin by follow-up/prior examinations or dedicated magnetic resonance imaging were included. Conventional and VNC images were reconstructed from the same scan. Region of interest-based image analysis was performed in adrenal lesions and contralateral healthy adrenal tissue. RESULTS: The 33 lesions consisted of 23 adenomas and 10 metastases. Hounsfield unit values of all lesions in VNC images were significantly lower compared with conventional images (18.2 ± 12.6 HU vs 59.6 ± 21.7 HU, P < 0.001). Hounsfield unit values in adenomas were significantly lower in VNC images (11.3 ± 6.5 HU vs 34.1 ± 9.1 HU, P < 0.001). CONCLUSIONS: Virtual noncontrast HU values differed significantly between adrenal adenomas and metastases and can therefore be used for improved characterization of incidental adrenal lesions and definition of adrenal adenomas.

The Effect of Age on Fat Distribution in the Neck Using Volumetric Computed Tomography.

BACKGROUND: Neck fat distribution plays an important role in aging, yet how fat distribution changes with age is largely unknown. This study used volumetric computed tomography in live patients to characterize neck fat volume and distribution in young and elderly women. METHODS: A retrospective analysis was conducted of head and neck computed tomographic angiographs of 20 young (aged 20 to 35 years) and 20 old (aged 65 to 89 years) women. Fat volume in the supraplatysmal and subplatysmal planes was quantified. Distribution of fat volume was assessed by dividing each supraplatysmal and subplatysmal compartment into upper, middle, and lower thirds. RESULTS: Total supraplatysmal fat volume was greater than subplatysmal in all patients. Young patients had more total supraplatysmal fat than old patients (p < 0.0001). No difference was found between age groups in subplatysmal fat (p > 0.05). No difference was found between upper/middle/lower third supraplatysmal fat volumes in young patients. When comparing supraplatysmal thirds within the elderly population, the middle third fat volume (28.58 ± 20.01 cm3) was greater than both upper (18.93 ± 10.35 cm3) and lower thirds (15.46 ± 11.57 cm3) (p < 0.01). CONCLUSIONS: This study suggests that total supraplatysmal fat volume decreases with age. Older patients had more fat volume in the upper and middle thirds compared with the lower third of the supraplatysmal fat compartment, whereas young patients had more evenly distributed fat. These results suggest that fat deposition and redistribution in the neck occur with age and may be a contributing factor to the obtuse cervicomandibular angle of the elderly.

Principles and applications of multienergy CT: Report of AAPM Task Group 291.

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.

High correlation between radiation dose estimates for 256-slice CT obtained by highly parallelized hybrid Monte Carlo computation and solid-state metal-oxide semiconductor field-effect transistor measurements in physical anthropomorphic phantoms.

PURPOSE: Accurate, patient-specific radiation dosimetry for CT scanning is critical to optimize radiation doses and balance dose against image quality. While Monte Carlo (MC) simulation is often used to estimate doses from CT, comparison of estimates to experimentally measured values is lacking for advanced CT scanners incorporating novel design features. We aimed to compare radiation dose estimates from MC simulation to doses measured in physical anthropomorphic phantoms using metal-oxide semiconductor field-effect transistors (MOSFETs) in a 256-slice CT scanner. METHODS: Fifty MOSFETs were placed in organs within tissue-equivalent anthropomorphic adult and pediatric radiographic phantoms, which were scanned using a variety of chest, cardiac, abdomen, brain, and whole-body protocols on a 256-slice system. MC computations were performed on voxelized CT reconstructions of the phantoms using a highly parallel MC tool developed specifically for diagnostic X-ray energies and rapid computation. Doses were compared between MC estimates and physical measurements. RESULTS: The average ratio of MOSFET to MC dose in the in-field region was close to 1 (range, 0.96-1.12; mean ± SD, 1.01 ± 0.04), indicating outstanding agreement between measured and simulated doses. The difference between measured and simulated doses tended to increase with distance from the in-field region. The error in the MC simulations due to the limited number of simulated photons was less than 1%. The errors in the MOSFET dose determinations in the in-field region for a single scan were mainly due to the calibration method and were typically about 6% (8% if the error in the reading of the ionization chamber that was used for the MOSFET calibration was included). CONCLUSIONS: Radiation dose estimation using a highly parallelized MC method is strongly correlated with experimental measurements in physical adult and infant anthropomorphic phantoms for a wide range of scans performed on a 256-slice CT scanner. Incorporation into CT scanners of radiation-dose distribution estimation, employing the scanner's reconstructed images of the patient, may offer the potential for accurate patient-specific CT dosimetry.

Cardiac-Specific Conversion Factors to Estimate Radiation Effective Dose From Dose-Length Product in Computed Tomography.

OBJECTIVES: This study sought to determine updated conversion factors (k-factors) that would enable accurate estimation of radiation effective dose (ED) for coronary computed tomography angiography (CTA) and calcium scoring performed on 12 contemporary scanner models and current clinical cardiac protocols and to compare these methods to the standard chest k-factor of 0.014 mSv·mGy-1cm-1. BACKGROUND: Accurate estimation of ED from cardiac CT scans is essential to meaningfully compare the benefits and risks of different cardiac imaging strategies and optimize test and protocol selection. Presently, ED from cardiac CT is generally estimated by multiplying a scanner-reported parameter, the dose-length product, by a k-factor which was determined for noncardiac chest CT, using single-slice scanners and a superseded definition of ED. METHODS: Metal-oxide-semiconductor field-effect transistor radiation detectors were positioned in organs of anthropomorphic phantoms, which were scanned using all cardiac protocols, 120 clinical protocols in total, on 12 CT scanners representing the spectrum of scanners from 5 manufacturers (GE, Hitachi, Philips, Siemens, Toshiba). Organ doses were determined for each protocol, and ED was calculated as defined in International Commission on Radiological Protection Publication 103. Effective doses and scanner-reported dose-length products were used to determine k-factors for each scanner model and protocol. RESULTS: k-Factors averaged 0.026 mSv·mGy-1cm-1 (95% confidence interval: 0.0258 to 0.0266) and ranged between 0.020 and 0.035 mSv·mGy-1cm-1. The standard chest k-factor underestimates ED by an average of 46%, ranging from 30% to 60%, depending on scanner, mode, and tube potential. Factors were higher for prospective axial versus retrospective helical scan modes, calcium scoring versus coronary CTA, and higher (100 to 120 kV) versus lower (80 kV) tube potential and varied among scanner models (range of average k-factors: 0.0229 to 0.0277 mSv·mGy-1cm-1). CONCLUSIONS: Cardiac k-factors for all scanners and protocols are considerably higher than the k-factor currently used to estimate ED of cardiac CT studies, suggesting that radiation doses from cardiac CT have been significantly and systematically underestimated. Using cardiac-specific factors can more accurately inform the benefit-risk calculus of cardiac-imaging strategies.

Objective image characterization of a spectral CT scanner with dual-layer detector.

This work evaluated the performance of a detector-based spectral CT system by obtaining objective reference data, evaluating attenuation response of iodine and accuracy of iodine quantification, and comparing conventional CT and virtual monoenergetic images in three common phantoms. Scanning was performed using the hospital's clinical adult body protocol. Modulation transfer function (MTF) was calculated for a tungsten wire and visual line pair targets were evaluated. Image noise power spectrum (NPS) and pixel standard deviation were calculated. MTF for monoenergetic images agreed with conventional images within 0.05 lp cm-1. NPS curves indicated that noise texture of 70 keV monoenergetic images is similar to conventional images. Standard deviation measurements showed monoenergetic images have lower noise except at 40 keV. Mean CT number and CNR agreed with conventional images at 75 keV. Measured iodine concentration agreed with true concentration within 6% for inserts at the center of the phantom. Performance of monoenergetic images at detector based spectral CT is the same as, or better than, that of conventional images. Spectral acquisition and reconstruction with a detector based platform represents the physical behaviour of iodine as expected and accurately quantifies the material concentration.

The role of advanced reconstruction algorithms in cardiac CT.

Non-linear iterative reconstruction (IR) algorithms have been increasingly incorporated into clinical cardiac CT protocols at institutions around the world. Multiple IR algorithms are available commercially from various vendors. IR algorithms decrease image noise and are primarily used to enable lower radiation dose protocols. IR can also be used to improve image quality for imaging of obese patients, coronary atherosclerotic plaques, coronary stents, and myocardial perfusion. In this article, we will review the various applications of IR algorithms in cardiac imaging and evaluate how they have changed practice.

Update on Cardiovascular Applications of Multienergy CT.

Advances in scanner technology enabling shorter scan times, improvements in spatial and temporal resolution, and more dose-efficient data reconstruction coupled with rapidly growing evidence from clinical trials have established computed tomography (CT) as an important imaging modality in the evaluation of cardiovascular disorders. Multienergy (or spectral or dual-energy) CT is a relatively recent advance in which attenuation data from different energies are used to characterize materials beyond what is possible at conventional CT. Current technologies for multienergy CT are either source based (ie, dual source, rapid kilovoltage switching, dual spin, and split beam) or detector based (ie, dual layer and photon counting), and material-based decomposition occurs in either image or projection space. In addition to conventional diagnostic images, multienergy CT provides image sets such as iodine maps, virtual nonenhanced, effective atomic number, and virtual monoenergy (VM) images as well as data at the elemental level (CT fingerprinting), which can complement and in some areas overcome the limitations posed by conventional CT methods. In myocardial perfusion imaging, iodine maps improve the sensitivity of perfusion defects, and VM images improve the specificity by decreasing artifacts. Iodine maps are also useful in improving the performance of CT in delayed-enhancement imaging. In pulmonary perfusion imaging, iodine maps enhance the sensitivity of detection of both acute and chronic pulmonary emboli. Low-energy (as measured in kiloelectron volts) VM images allow enhancement of vascular contrast, which can either be used to lower contrast dose or salvage a suboptimal contrast-enhanced study. High-energy VM images can be used to decrease or eliminate artifacts such as beam-hardening and metallic artifacts. Virtual nonenhanced images have similar attenuation as true nonenhanced images and help in reducing radiation dose by eliminating the need for the latter in multiphasic vascular studies. Other potential applications of multienergy CT include calcium scoring from virtual nonenhanced images created from coronary CT angiograms and myocardial iron quantification. Online supplemental material is available for this article. ©RSNA, 2017.

Calibration and error analysis of metal-oxide-semiconductor field-effect transistor dosimeters for computed tomography radiation dosimetry.

PURPOSE: Metal-oxide-semiconductor field-effect transistors (MOSFETs) serve as a helpful tool for organ radiation dosimetry and their use has grown in computed tomography (CT). While different approaches have been used for MOSFET calibration, those using the commonly available 100 mm pencil ionization chamber have not incorporated measurements performed throughout its length, and moreover, no previous work has rigorously evaluated the multiple sources of error involved in MOSFET calibration. In this paper, we propose a new MOSFET calibration approach to translate MOSFET voltage measurements into absorbed dose from CT, based on serial measurements performed throughout the length of a 100-mm ionization chamber, and perform an analysis of the errors of MOSFET voltage measurements and four sources of error in calibration. METHODS: MOSFET calibration was performed at two sites, to determine single calibration factors for tube potentials of 80, 100, and 120 kVp, using a 100-mm-long pencil ion chamber and a cylindrical computed tomography dose index (CTDI) phantom of 32 cm diameter. The dose profile along the 100-mm ion chamber axis was sampled in 5 mm intervals by nine MOSFETs in the nine holes of the CTDI phantom. Variance of the absorbed dose was modeled as a sum of the MOSFET voltage measurement variance and the calibration factor variance, the latter being comprised of three main subcomponents: ionization chamber reading variance, MOSFET-to-MOSFET variation and a contribution related to the fact that the average calibration factor of a few MOSFETs was used as an estimate for the average value of all MOSFETs. MOSFET voltage measurement error was estimated based on sets of repeated measurements. The calibration factor overall voltage measurement error was calculated from the above analysis. RESULTS: Calibration factors determined were close to those reported in the literature and by the manufacturer (~3 mV/mGy), ranging from 2.87 to 3.13 mV/mGy. The error σV of a MOSFET voltage measurement was shown to be proportional to the square root of the voltage V: σV=cV where c = 0.11 mV. A main contributor to the error in the calibration factor was the ionization chamber reading error with 5% error. The usage of a single calibration factor for all MOSFETs introduced an additional error of about 5-7%, depending on the number of MOSFETs that were used to determine the single calibration factor. The expected overall error in a high-dose region (~30 mGy) was estimated to be about 8%, compared to 6% when an individual MOSFET calibration was performed. For a low-dose region (~3 mGy), these values were 13% and 12%. CONCLUSIONS: A MOSFET calibration method was developed using a 100-mm pencil ion chamber and a CTDI phantom, accompanied by an absorbed dose error analysis reflecting multiple sources of measurement error. When using a single calibration factor, per tube potential, for different MOSFETs, only a small error was introduced into absorbed dose determinations, thus supporting the use of a single calibration factor for experiments involving many MOSFETs, such as those required to accurately estimate radiation effective dose.

Spectral detector CT for cardiovascular applications.

Spectral detector computed tomography (SDCT) is a novel technology that uses two layers of detectors to simultaneously collect low and high energy data. Spectral data is used to generate conventional polyenergetic images as well as dedicated spectral images including virtual monoenergetic and material composition (iodine-only, virtual unenhanced, effective atomic number) images. This paper provides an overview of SDCT technology and a description of some spectral image types. The potential utility of SDCT for cardiovascular imaging and the impact of this new technology on radiation and contrast dose are discussed through presentation of initial patient studies performed on a SDCT scanner. The value of SDCT for salvaging suboptimal studies including those with poor contrast-enhancement or beam hardening artifacts through retrospective reconstruction of spectral data is discussed. Additionally, examples of specific benefits for the evaluation of aortic disease, imaging before transcatheter aortic valve implantation, evaluation of pulmonary veins pre- and post-pulmonary radiofrequency ablation, evaluation of coronary artery lumen, assessment of myocardial perfusion, detection of pulmonary embolism, and characterization of incidental findings are presented.

Artifacts at Cardiac CT: Physics and Solutions.

Computed tomography is vulnerable to a wide variety of artifacts, including patient- and technique-specific artifacts, some of which are unique to imaging of the heart. Motion is the most common source of artifacts and can be caused by patient, cardiac, or respiratory motion. Cardiac motion artifacts can be reduced by decreasing the heart rate and variability and the duration of data acquisition; adjusting the placement of the data window within a cardiac cycle; performing single-heartbeat scanning; and using multisegment reconstruction, motion-correction algorithms, and electrocardiographic editing. Respiratory motion artifacts can be minimized with proper breath holding and shortened scan duration. Partial volume averaging is caused by the averaging of attenuation values from all tissue contained within a voxel and can be reduced by improving the spatial resolution, using a higher x-ray energy, or displaying images with a wider window width. Beam-hardening artifacts are caused by the polyenergetic nature of the x-ray beam and can be reduced by using x-ray filtration, applying higher-energy x-rays, altering patient position, modifying contrast material protocols, and applying certain reconstruction algorithms. Metal artifacts are complex and have multiple causes, including x-ray scatter, underpenetration, motion, and attenuation values that exceed the typical dynamic range of Hounsfield units. Quantum mottle or noise is caused by insufficient penetration of tissue and can be improved by increasing the tube current or peak tube potential, reconstructing thicker sections, increasing the rotation time, using appropriate patient positioning, and applying iterative reconstruction algorithms. ©RSNA, 2016.

Assessing Level of Agreement for Atherosclerotic Cardiovascular Disease Risk Categorization Between Coronary Artery Calcium Score and the American College of Cardiology/American Heart Association Cardiovascular Prevention Guidelines and the Potential Impact on Treatment Recommendations.

The 2013 American College of Cardiology/American Heart Association cardiovascular prevention guidelines use a new pooled cohort equation (PCE) to predict 10-year risk of atherosclerotic cardiovascular disease (ASCVD) events which form the basis of treatment recommendations. Coronary artery calcium score (CACS) has been proposed as a means to assess atherosclerotic risk. We sought to study the level of agreement in predicted ASCVD risk by CACS and PCE-calculated models and the potential impact on therapy of additional CACS testing. We studied 687 treatment naive, consecutive patients (mean age 53.5 years, 72% men) who had a CACS study at our institution. Clinical and imaging data were recorded. ASCVD risk was calculated using the published PCE-based algorithm. CACS-based risk was categorized by previously published recommendations. Risk stratification comparisons were made and level of agreement calculated. In the cohort, mean ASCVD PCE-calculated risk was 5.3 ± 5.2% and mean CACS was 80 ± 302 Agatston units (AU). Of the intermediate PCE-calculated risk (5% to <7.5%) cohort, 85% had CACS <100 AU. Of the cohort categorized as reasonable to treat per the ASCVD prevention guidelines, 40% had a CACS of 0 AU and an additional 44% had CACS >0 but <100 AU. The level of agreement between the new PCE model of ASCVD risk and demonstrable coronary artery calcium is low. CACS testing may be most beneficial in those with an intermediate risk of ASCVD (PCE-calculated risk of 5% to <7.5%) where, in approximately half of patients, CACS testing significantly refined risk assessment primarily into a very low-risk category.

Practical considerations for optimizing cardiac computed tomography protocols for comprehensive acquisition prior to transcatheter aortic valve replacement.

Transcatheter aortic valve replacement (TAVR) is performed frequently in patients with severe, symptomatic aortic stenosis who are at high risk or inoperable for open surgical aortic valve replacement. Computed tomography angiography (CTA) has become the gold standard imaging modality for pre-TAVR cardiac anatomic and vascular access assessment. Traditionally, cardiac CTA has been most frequently used for assessment of coronary artery stenosis, and scanning protocols have generally been tailored for this purpose. Pre-TAVR CTA has different goals than coronary CTA and the high prevalence of chronic kidney disease in the TAVR patient population creates a particular need to optimize protocols for a reduction in iodinated contrast volume. This document reviews details which allow the physician to tailor CTA examinations to maximize image quality and minimize harm, while factoring in multiple patient and scanner variables which must be considered in customizing a pre-TAVR protocol.