CT acquisition parameter selection in the real world: impacts on radiation dose and variation amongst 155 institutions.

OBJECTIVE: Quantify the relationship between CT acquisition parameters and radiation dose, how often parameters are adjusted in real-world practice, and their degree of contribution to real-world dose distribution. Identify discrepancies between parameters that are impactful in theory and impactful in practice. METHODS: This study analyses 1.3 million consecutive adult routine abdomen exams performed between November 2015 and Jan 2021 included in the University of California, San Francisco International CT Dose Registry of 155 institutions. We calculated geometric standard deviation (gSD) for five parameters (kV, mAs, spiral pitch, number of phases, scan length) to assess variation in practice. A Gaussian mixed regression model was performed to predict the radiation dose-length product (DLP) using the parameters. Three conceptualizations of "impact" were computed for each parameter. To reflect the theoretical impact, we predict the increase in DLP per 10% (and 15%) increase in the parameter. To reflect the real-world practical impact, we predict the increase in DLP per gSD increase in the parameter. RESULTS: Among studied examinations, mAs, number of phases, and scan length were frequently manipulated (gSD 1.52-1.70); kV was rarely manipulated (gSD 1.07). Theoretically, kV is the most impactful parameter (29% increase in DLP per 10% increase in kV, versus 5-9% increase for other parameters). In real-world practice, kV is less impactful; for each gSD increase in kV, the DLP increases by 20%, versus 22-69% for other parameters. CONCLUSION: Despite the potential impact of kV on radiation dose, this parameter is rarely manipulated in common practice and this potential remains untapped. CLINICAL RELEVANCE STATEMENT: CT beam energy (kV) modulation has the potential to strongly reduce radiation over-dosage to the patient, theoretically more so than similar degrees of modulation in other CT acquisition parameters. Despite this, beam energy modulation rarely occurs in practice, leaving its potential untapped. KEY POINTS: • The relationship between CT acquisition parameter selection and radiation dose roughly coincided with established theoretical understanding. • CT acquisition parameters differ from each other in frequency and magnitude of manipulation, with beam energy (kV) being rarely manipulated. • Beam energy (kV) has the potential to substantially impact radiation dose, but because it is rarely manipulated, it is the least impactful CT acquisition parameter affecting radiation dose in practice.

Large variation in radiation dose for routine abdomen CT: reasons for excess and easy tips for reduction.

OBJECTIVE: To characterize the use and impact of radiation dose reduction techniques in actual practice for routine abdomen CT. METHODS: We retrospectively analyzed consecutive routine abdomen CT scans in adults from a large dose registry, contributed by 95 hospitals and imaging facilities. Grouping exams into deciles by, first, patient size, and second, size-adjusted dose length product (DLP), we summarized dose and technical parameters and estimated which parameters contributed most to between-protocols dose variation. Lastly, we modeled the total population dose if all protocols with mean size-adjusted DLP above 433 or 645 mGy-cm were reduced to these thresholds. RESULTS: A total of 748,846 CTs were performed using 1033 unique protocols. When sorted by patient size, patients with larger abdominal diameters had increased dose and effective mAs (milliampere seconds), even after adjusting for patient size. When sorted by size-adjusted dose, patients in the highest versus the lowest decile in size-adjusted DLP received 6.4 times the average dose (1680 vs 265 mGy-cm) even though diameter was no different (312 vs 309 mm). Effective mAs was 2.1-fold higher, unadjusted CTDIvol 2.9-fold, and phase 2.5-fold for patients in the highest versus lowest size-adjusted DLP decile. There was virtually no change in kV (kilovolt). Automatic exposure control was widely used to modulate mAs, whereas kV modulation was rare. Phase was the strongest driver of between-protocols variation. Broad adoption of optimized protocols could result in total population dose reductions of 18.6-40%. CONCLUSION: There are large variations in radiation doses for routine abdomen CT unrelated to patient size. Modification of kV and single-phase scanning could result in substantial dose reduction. CLINICAL RELEVANCE: Radiation dose-optimization techniques for routine abdomen CT are routinely under-utilized leading to higher doses than needed. Greater modification of technical parameters and number of phases could result in substantial reduction in radiation exposure to patients. KEY POINTS: • Based on an analysis of 748,846 routine abdomen CT scans in adults, radiation doses varied tremendously across patients of the same size and optimization techniques were routinely under-utilized. • The difference in observed dose was due to variation in technical parameters and phase count. Automatic exposure control was commonly used to modify effective mAs, whereas kV was rarely adjusted for patient size. Routine abdomen CT should be performed using a single phase, yet multi-phase was common. • kV modulation by patient size and restriction to a single phase for routine abdomen indications could result in substantial reduction in radiation doses using well-established dose optimization approaches.

Technical note: A comparison of physician doses in C-Arm and CT fluoroscopy procedures.

PURPOSE: We address the misconception that the typical physician dose is higher for CT fluoroscopy (CTF) procedures compared to C-Arm procedures. METHODS: We compare physician scatter doses using two methods: a literature review of reported doses and a model based on a modified form of the dose area product (DAP). We define this modified form of DAP, "cumulative absorbed DAP," as the product of the area of the x-ray beam striking the patient, the dose rate per unit area, and the exposure time. RESULTS: The patient entrance dose rate for C-Arm fluoroscopy (0.2 mGy/s) was found to be 15 times lower than for CT fluoroscopy (3 mGy/s). A typical beam entrance area for C-Arm fluoroscopy reported in the literature was found to be 10.6 × 10.6 cm (112 cm2), whereas for CTF was 0.75 × 32 cm (24 cm2). The absorbed DAP rate for C-Arm fluoroscopy (22 mGy*cm2/s) was found to be 3.3 times lower than for CTF (72 mGy*cm2/s). The mean fluoroscopy time for C-Arm procedures (710 s) was found to be 21 times higher than for CT fluoroscopy procedures (23 s). The cumulative absorbed DAP for C-Arm procedures was found to be 9.4 times higher when compared to CT procedures (1.59 mGy*m2 vs. 0.17 mGy*m2). CONCLUSIONS: The higher fluoroscopy time in C-Arm procedures leads to a much lower cumulative DAP (i.e., physician scatter dose) in CTF procedures. This result can inform interventional physicians deciding on whether to perform inter-procedural imaging inside the room as opposed to retreating from the room.

Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects.

Filtered back projection (FBP) has been the standard CT image reconstruction method for 4 decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in several clinical applications. However, with faster and more advanced CT scanners, FBP has become increasingly obsolete. Higher image noise and more artifacts are especially noticeable in lower-dose CT imaging using FBP. This performance gap was partly addressed by model-based iterative reconstruction (MBIR). Yet, its "plastic" image appearance and long reconstruction times have limited widespread application. Hybrid iterative reconstruction partially addressed these limitations by blending FBP with MBIR and is currently the state-of-the-art reconstruction technique. In the past 5 years, deep learning reconstruction (DLR) techniques have become increasingly popular. DLR uses artificial intelligence to reconstruct high-quality images from lower-dose CT faster than MBIR. However, the performance of DLR algorithms relies on the quality of data used for model training. Higher-quality training data will become available with photon-counting CT scanners. At the same time, spectral data would greatly benefit from the computational abilities of DLR. This review presents an overview of the principles, technical approaches, and clinical applications of DLR, including metal artifact reduction algorithms. In addition, emerging applications and prospects are discussed.

Percutaneous CT-Guided Abdominal and Pelvic Biopsies: Comparison of an Electromagnetic Navigation System and CT Fluoroscopy.

PURPOSE: To compare electromagnetic navigation (EMN) with computed tomography (CT) fluoroscopy for guiding percutaneous biopsies in the abdomen and pelvis. MATERIALS AND METHODS: A retrospective matched-cohort design was used to compare biopsies in the abdomen and pelvis performed with EMN (consecutive cases, n = 50; CT-Navigation; Imactis, Saint-Martin-d'Hères, France) with those performed with CT fluoroscopy (n = 100). Cases were matched 1:2 (EMN:CT fluoroscopy) for target organ and lesion size (±10 mm). RESULTS: The population was well-matched (age, 65 vs 65 years; target size, 2.0 vs 2.1 cm; skin-to-target distance, 11.4 vs 10.7 cm; P > .05, EMN vs CT fluoroscopy, respectively). Technical success (98% vs 100%), diagnostic yield (98% vs 95%), adverse events (2% vs 5%), and procedure time (33 minutes vs 31 minutes) were not statistically different (P > .05). Operator radiation dose was less with EMN than with CT fluoroscopy (0.04 vs 1.2 µGy; P < .001), but patient dose was greater (30.1 vs 9.6 mSv; P < .001) owing to more helical scans during EMN guidance (3.9 vs 2.1; P < .001). CT fluoroscopy was performed with a mean of 29.7 tap scans per case. In 3 (3%) cases, CT fluoroscopy was performed with gantry tilt, and the mean angle out of plane for EMN cases was 13.4°. CONCLUSIONS: Percutaneous biopsies guided by EMN and CT fluoroscopy were closely matched for technical success, diagnostic yield, procedure time, and adverse events in a matched cohort of patients. EMN cases were more likely to be performed outside of the gantry plane. Radiation dose to the operator was higher with CT fluoroscopy, and patient radiation dose was higher with EMN. Further study with a wider array of procedures and anatomic locations is warranted.

CT Number Accuracy and Association With Object Size: A Phantom Study Comparing Energy-Integrating Detector CT and Deep Silicon Photon-Counting Detector CT.

BACKGROUND. Variable beam hardening based on patient size causes variation in CT numbers for energy-integrating detector (EID) CT. Photon-counting detector (PCD) CT more accurately determines effective beam energy, potentially improving CT number reliability. OBJECTIVE. The purpose of the present study was to compare EID CT and deep silicon PCD CT in terms of both the effect of changes in object size on CT number and the overall accuracy of CT numbers. METHODS. A phantom with polyethylene rings of varying sizes (mimicking patient sizes) as well as inserts of different materials was scanned on an EID CT scanner in single-energy (SE) mode (120-kV images) and in rapid-kilovoltage-switching dual-energy (DE) mode (70-keV images) and on a prototype deep silicon PCD CT scanner (70-keV images). ROIs were placed to measure the CT numbers of the materials. Slopes of CT number as a function of object size were computed. Materials' ideal CT number at 70 keV was computed using the National Institute of Standards and Technology XCOM Photon Cross Sections Database. The root mean square error (RMSE) between measured and ideal numbers was calculated across object sizes. RESULTS. Slope (expressed as Hounsfield units per centimeter) was significantly closer to zero (i.e., less variation in CT number as a function of size) for PCD CT than for SE EID CT for air (1.2 vs 2.4 HU/cm), water (-0.3 vs -1.0 HU/cm), iodine (-1.1 vs -4.5 HU/cm), and bone (-2.5 vs -10.1 HU/cm) and for PCD CT than for DE EID CT for air (1.2 vs 2.8 HU/cm), water (-0.3 vs -1.0 HU/cm), polystyrene (-0.2 vs -0.9 HU/cm), iodine (-1.1 vs -1.9 HU/cm), and bone (-2.5 vs -6.2 HU/cm) (p < .05). For all tested materials, PCD CT had the smallest RMSE, indicating CT numbers closest to ideal numbers; specifically, RMSE (expressed as Hounsfield units) for SE EID CT, DE EID CT, and PCD CT was 32, 44, and 17 HU for air; 7, 8, and 3 HU for water; 9, 10, and 4 HU for polystyrene; 31, 37, and 13 HU for iodine; and 69, 81, and 20 HU for bone, respectively. CONCLUSION. For numerous materials, deep silicon PCD CT, in comparison with SE EID CT and DE EID CT, showed lower CT number variability as a function of size and CT numbers closer to ideal numbers. CLINICAL IMPACT. Greater reliability of CT numbers for PCD CT is important given the dependence of diagnostic pathways on CT numbers.

A Characterization of Deep Learning Reconstruction Applied to Dual-Energy Computed Tomography Monochromatic and Material Basis Images.

OBJECTIVE: Advancements in computed tomography (CT) reconstruction have enabled image quality improvements and dose reductions. Previous advancements have included iterative and model-based reconstruction. The latest image reconstruction advancement uses deep learning, which has been evaluated for polychromatic imaging only. This article characterizes a commercially available deep learning imaging reconstruction applied to dual-energy CT. METHODS: Monochromatic, iodine basis, and water basis images were reconstructed with filtered back projection (FBP), iterative (ASiR-V), and deep learning (DLIR) methods in a phantom experiment. Slice thickness, contrast-to-noise ratio, modulation transfer function, and noise power spectrum metrics were used to characterize ASiR-V and DLIR relative to FBP over a range of dose levels, phantom sizes, and iodine concentrations. RESULTS: Slice thicknesses for ASiR-V and DLIR demonstrated no statistically significant difference relative to FBP for all measurement conditions. Contrast-to-noise ratio performance for DLIR-high and ASiR-V 40% at 2 mg I/mL on 40-keV images were 162% and 30% higher than FBP, respectively. Task-based modulation transfer function measurements demonstrated no clinically significant change between FBP and ASiR-V and DLIR on monochromatic or iodine basis images. CONCLUSIONS: Deep learning image reconstruction enabled better image quality at lower monochromatic energies and on iodine basis images where image contrast is maximized relative to polychromatic or high-energy monochromatic images. Deep learning image reconstruction did not demonstrate thicker slices, decreased spatial resolution, or poor noise texture (ie, "plastic") relative to FBP.

Electrocardiographic Gating and Cerebral Perfusion Computed Tomography Option-Set Prevalence and Utilization Data From 62 Institutions in the United States.

OBJECTIVES: To provide the radiology community with data to address the question: "Compared with peer institutions, is my institution efficiently using its electrocardiographic (ECG) gating and cerebral perfusion-capable computed tomography (CT) scanners?" METHODS: In this retrospective study, we analyze 6 months of scanner utilization data from 62 institutions (299 locations, 507 scanners) to identify scanners capable of performing ECG gating and perfusion CT studies. We report the number of ECG gating/perfusion-capable scanners and locations as a function of the total number of locations and scanners in each institution. We additionally regress the number of ECG-gated and perfusion examinations on (1) the number of locations/scanners capable of performing these examinations and (2) the fraction of the institution's CT examination volume that requires ECG gating or perfusion. We provide look-up tables so an institution can compare its ECG-gated/perfusion examination volume to other institutions with similar ECG-gated/perfusion examination fractions and capable scanners. RESULTS: We detected an effect of both ECG-gating examination fraction and the number of ECG gating-capable scanners on ECG-gated examination volume ( χ21 = 77.5 [ P < 0.001] and χ21 = 64.2 [ P < 0.001], respectively). Similar results were obtained for perfusion examination fraction and perfusion-capable scanners as they relate to perfusion examination volume ( χ21 = 51.6 [ P < 0.001] and χ21 = 45.2 [ P < 0.001], respectively). The number of ECG gating/perfusion-capable scanners and locations within an institution were found to positively correlate with both the total number of locations and scanners within an institution ( P < 0.001 for all hypothesis tests). CONCLUSIONS: The study provides multi-institutional data on ECG gating and perfusion examination volumes that can be used to inform CT purchasing decisions.

How Do Cancer-Specific Computed Tomography Protocols Compare With the American College of Radiology Dose Index Registry? An Analysis of Computed Tomography Dose at 2 Cancer Centers.

BACKGROUND: Little guidance exists on how to stratify radiation dose according to diagnostic task. Changing dose for different cancer types is currently not informed by the American College of Radiology Dose Index Registry dose survey. METHODS: A total of 9602 patient examinations were pulled from 2 National Cancer Institute designated cancer centers. Computed tomography dose (CTDI vol ) was extracted, and patient water equivalent diameter was calculated. N-way analysis of variance was used to compare the dose levels between 2 protocols used at site 1, and three protocols used at site 2. RESULTS: Sites 1 and 2 both independently stratified their doses according to cancer indications in similar ways. For example, both sites used lower doses ( P < 0.001) for follow-up of testicular cancer, leukemia, and lymphoma. Median dose at median patient size from lowest to highest dose level for site 1 were 17.9 (17.7-18.0) mGy (mean [95% confidence interval]) and 26.8 (26.2-27.4) mGy. For site 2, they were 12.1 (10.6-13.7) mGy, 25.5 (25.2-25.7) mGy, and 34.2 (33.8-34.5) mGy. Both sites had higher doses ( P < 0.001) between their routine and high-image-quality protocols, with an increase of 48% between these doses for site 1 and 25% for site 2. High-image-quality protocols were largely applied for detection of low-contrast liver lesions or subtle pelvic pathology. CONCLUSIONS: We demonstrated that 2 cancer centers independently choose to stratify their cancer doses in similar ways. Sites 1 and 2 dose data were higher than the American College of Radiology Dose Index Registry dose survey data. We thus propose including a cancer-specific subset for the dose registry.

Factors Contributing to Computed Tomography Trauma Scan Times at a Tertiary Center: Improving Emergency Department Trauma Imaging Workflow Through Targeted Interventions.

PURPOSES: The aims of the study are to identify factors contributing to computed tomography (CT) trauma scan turnaround time variation and to evaluate the effects of an automated intervention on time metrics. METHODS: Throughput metrics were captured via picture archiving and communication system from January 1, 2018, to December 16, 2019, and included 17,709 CT trauma scans from our institution. Initial data showed that imaging technologist variation played a significant role in trauma imaging turnaround time. In December 2019, we implemented a 2-pronged intervention: (1) educational intervention to techs and (2) modified trauma CT abdomen/pelvis to autogenerate and autosend reformats to picture archiving and communication system. A total of 13,169 trauma CT scans were evaluated from the postintervention period taking place from January 2020 to March 2021. Throughput metrics such as last image to first report interval and emergency department length of stay were captured and compared with performing technologist, time of day, and weekday versus weekend scans. RESULTS: Substantial variability among trauma CT scans was observed. For CT trauma abdomen/pelvis, the interval from last image to initial report decreased from 26.4 to 24.0 minutes ( P = 0.001) while the interval between first and last image time decreased from 11.4 to 4.2 minutes ( P < 0.001). Emergency department length of stay also decreased from 3.9 to 3.7 hours ( P < 0.0001) in the postintervention period. Variation among imaging technologist was statistically significant and became less significant after intervention ( P = 0.09, P = 0.54). CONCLUSIONS: Factors such as imaging technologist variability, time of day, and day of the week of trauma scans played a significant role in CT trauma turnaround time variability. Automation interventions can help with efficiency in image turnaround time.

CT Fluoroscopy for Image-Guided Procedures: Physician Radiation Dose During Full-Rotation and Partial-Angle CT Scanning.

PURPOSE: To determine physician radiation exposure when using partial-angle computed tomography (CT) fluoroscopy (PACT) vs conventional full-rotation CT and whether there is an optimal tube/detector position at which physician dose is minimized. MATERIALS AND METHODS: Physician radiation dose (entrance air kerma) was measured for full-rotation CT (360°) and PACT (240°) at all tube/detector positions using a human-mimicking phantom placed in a 64-channel multidetector CT. Parameters included 120 kV, 20- and 40-mm collimation, and 100 mA. The mean, standard deviation, and increase/decrease in physician dose compared with a full-rotation scan were reported. RESULTS: Physician radiation exposure during CT fluoroscopy with PACT was highly dependent on the position of the tube/detector during scanning. The lowest PACT physician dose was when the physician was on the detector side (center view angle 116°; -35% decreased dose vs full-angle CT). The highest PACT physician dose was with the physician on the tube side (center view angle 298°; +34% increased dose vs full-angle CT), all doses P <.05 vs full-rotation CT. CONCLUSIONS: Partial-angle CT has the potential to both significantly increase or decrease physician radiation dose during CT fluoroscopy-guided procedures. The detector/tube position has a profound effect on physician dose. The lowest dose during PACT was achieved when the physician was located on the detector side (ie, distant from the tube). This data could be used to optimize CT fluoroscopy parameters to reduce physician radiation exposure for PACT-capable scanners.

Protocol Optimization Considerations for Implementing Deep Learning CT Reconstruction.

OBJECTIVE. Previous advances over filtered back projection (FBP) have incorporated model-based iterative reconstruction. The purpose of this study was to characterize the latest advance in image reconstruction, that is, deep learning. The focus was on applying characterization results of a deep learning approach to decisions about clinical CT protocols. MATERIALS AND METHODS. A proprietary deep learning image reconstruction (DLIR) method was characterized against an existing advanced adaptive statistical iterative reconstruction method (ASIR-V) and FBP from the same vendor. The metrics used were contrast-to-noise ratio, spatial resolution as a function of contrast level, noise texture (i.e., noise power spectra [NPS]), noise scaling as a function of slice thickness, and CT number consistency. The American College of Radiology accreditation phantom and a uniform water phantom were used at a range of doses and slice thicknesses for both axial and helical acquisition modes. RESULTS. ASIR-V and DLIR were associated with improved contrast-to-noise ratio over FBP for all doses and slice thicknesses. No dose or contrast dependencies of spatial resolution were observed for ASIR-V or DLIR. NPS results showed DLIR maintained an FBP-like noise texture whereas ASIR-V shifted the NPS to lower frequencies. Noise changed with dose and slice thickness in the same manner for ASIR-V and FBP. DLIR slice thickness noise scaling differed from FBP, exhibiting less noise penalty with decreasing slice thickness. No clinically significant changes were observed in CT numbers for any measurement condition. CONCLUSION. In a phantom model, DLIR does not suffer from the concerns over reduction in spatial resolution and introduction of poor noise texture associated with previous methods.

A Metric for Quantification of Iodine Contrast Enhancement (Q-ICE) in Computed Tomography.

BACKGROUND: Poor contrast enhancement is related to issues with examination execution, contrast prescription, computed tomography (CT) protocols, and patient conditions. Currently, our community has no metric to monitor true enhancement on routine single-phase examinations because this requires knowledge of both pre- and postcontrast CT number. PURPOSE: We propose an automatable solution to quantifying contrast enhancement without requiring a dedicated noncontrast series. METHODS: The difference in CT number between a target region in an enhanced and unenhanced image defines the metric "quantification of iodine contrast enhancement" (Q-ICE). Quantification of iodine contrast enhancement uses the noncontrast bolus tracking baseline image from routine abdominal examinations, which mitigates the need for a dedicated noncontrast series. We applied this method retrospectively to 312 patient livers from 2 sites between 2017 and 2020. Each site used a weight-based contrast injection protocol for weights 60 to 113 kg and a constant volume less than 60 kg and greater than 113 kg. Hypothesis testing was performed to compare Q-ICE between sites and detect Q-ICE dependence on weight and kilovoltage (kV). RESULTS: Mean Q-ICE differed between sites (P = 0.004) by 4.96 Hounsfield unit with 95% confidence interval (1.63-8.28), albeit this difference was roughly 2 times smaller than the SD in Q-ICE across patients at a single site. For patients between 60 and 113 kg, we did not observe evidence of Q-ICE varying with patient weight (P = 0.920 and 0.064 for 120 and 140 kV, respectively). The Q-ICE did vary with patient weight for patients less than 60 kg (P = 0.003) and greater than 113 kg (P = 0.04). We observed a roughly 10 Hounsfield unit reduction in Q-ICE liver for patients scanned with 140 versus 120 kV. We observed several underenhancing examinations with an arterial phase appearance motivating our CT protocol optimization team to consider increasing the delay for slowly enhancing patients. CONCLUSIONS: A quality metric for quantifying CT contrast enhancement was developed and suggested tangible opportunities for quality improvement and potential financial savings.

Ultra-Low Radiation Dose CT Fluoroscopy for Percutaneous Interventions: A Porcine Feasibility Study.

Purpose To determine the feasibility of ultra-low-dose (ULD) CT fluoroscopy for performing percutaneous CT-guided interventions in an in vivo porcine model and to compare radiation dose, spatial accuracy, and metal artifact for conventional CT versus CT fluoroscopy. Materials and Methods An in vivo swine model was used (n = 4, ∼50 kg) for 20 procedures guided by 246 incremental conventional CT scans (mean, 12.5 scans per procedure). The procedures were approved by the Institutional Animal Care and Use Committee and performed by two experienced radiologists from September 7, 2017, to August 8, 2018. ULD CT fluoroscopic acquisitions were simulated by using only two of 984 conventional CT projections to locate and reconstruct the needle, which was superimposed on a previously acquired and motion-compensated CT scan. The authors (medical physicists) compared the ULD CT fluoroscopy results to those of conventional CT for needle location, radiation dose, and metal artifacts using Deming regression and generalized mixed models. Results The average distance between the needle tip reconstructed using conventional CT and ULD CT fluoroscopy was 1.06 mm. Compared with CT fluoroscopy, the estimated dose for a percutaneous procedure, including planning acquisitions, was 0.99 mSv (21% reduction) for patients (effective dose) and 0.015 µGy (97% reduction) for physicians (scattered dose in air). Metal artifacts were statistically significantly reduced (P < .001, bootstrapping), and the average registration error of the motion compensation was within 1-3 mm. Conclusion Ultra-low-dose CT fluoroscopy has the potential to reduce radiation exposure for intraprocedural scans to patients and staff by a factor of approximately 500 times compared with conventional CT acquisition, while maintaining image quality without metal artifacts. © RSNA, 2019.

Radiation Dose for Multiregion CT Protocols: Challenges and Limitations.

OBJECTIVE. The purpose of this study was to devise a method for classification of individual chest and abdomen-pelvis CT doses for multiregion CT. MATERIALS AND METHODS. A retrospective analysis of volume CT dose index (CTDIvol) and dose-length product (DLP) associated with chest (150 adult patients), abdomen-pelvis (150 patients), and multiregion combined chest-abdomen-pelvis CT (210 patients; 60 single-run chest-abdomen-pelvis CT; 150 split-run with separate chest and abdomen-pelvis CT). All 510 CT examinations were performed with one of four MDCT scanners (64-, 64-, 128-, 256-MDCT). CTDIvol, DLP, and scan length were recorded. Scan lengths were obtained for these 510 CT examinations and for an additional 7745 examinations of patients at another institution. Data were analyzed by ANOVA and ROC analysis. RESULTS. The respective DLPs (chest, 258-381 mGy · cm; abdomen-pelvis, 360-433 mGy · cm; single-run chest-abdomen-pelvis, 595-636 mGy · cm) and scan lengths (chest, 31-33 cm; abdomen-pelvis, 45-46 cm; single-run chest-abdomen-pelvis, 63-65 cm) for chest, abdomen-pelvis, and multiregion combined chest-abdomen-pelvis CT were significantly different (p < 0.0001). For split-run, chest-abdomen-pelvis CT, scan lengths and dose indexes for individual body regions were not different from those of single-body-region CT (p > 0.05). ROC analysis of chest and abdomen examinations showed an ideal scan length threshold of 38 cm to differentiate abdomen-pelvis CT from chest CT with accuracy of 97.39% and an AUC of 0.9764. CONCLUSION. Despite interscanner variabilities in CT radiation doses, shorter scan length for chest than for abdomen-pelvis CT enables accurate binning of radiation doses for split-run combined chest-abdomen-pelvis CT.

The Current State of CT Dose Management Across Radiology: Well Intentioned but Not Universally Well Executed.

OBJECTIVE: Recent well-publicized sentinel events have resulted in an appropriately heightened awareness of CT dose. Concern also exists regarding the potential of CT dose increasing the risk of cancer. Several professional societies, governmental and accreditation agencies, and CT vendors have responded to these concerns with campaigns, mandatory standards, and software enhancements. The objective of this article is to review such CT dose management efforts. CONCLUSION: Although CT dose awareness campaigns, mandatory standards, and software enhancements are well intentioned, their implementation is often suboptimal.

Evaluation of AAPM Reports 204 and 220: Estimation of effective diameter, water-equivalent diameter, and ellipticity ratios for chest, abdomen, pelvis, and head CT scans.

PURPOSE: To confirm AAPM Reports 204/220 and provide data for the future expansion of these reports by: (a) presenting the first large-scale confirmation of the reports using clinical data, (b) providing the community with size surrogate data for the head region which was not provided in the original reports, and additionally providing the measurements of patient ellipticity ratio for different body regions. METHOD: A total of 884 routine scans were included in our analysis including data from the head, thorax, abdomen, and pelvis for adults and pediatrics. We calculated the ellipticity ratio and all of the size surrogates presented in AAPM Reports 204/220. We correlated the purely geometric-based metrics with the "gold standard" water-equivalent diameter (DW ). RESULTS: Our results and AAPM Reports 204/220 agree within our data's 95% confidence intervals. Outliers to the AAPM reports' methods were caused by excess gas in the GI tract, exceptionally low BMI, and cranial metaphyseal dysplasia. For the head, we show lower correlation (R2 = 0.812) between effective diameter and DW relative to other body regions. The ellipticity ratio of the shoulder region was the highest at 2.28 ± 0.22 and the head the smallest at 0.85 ± 0.08. The abdomen pelvis, chest, thorax, and abdomen regions all had ellipticity values near 1.5. CONCLUSION: We confirmed AAPM reports 204/220 using clinical data and identified patient conditions causing discrepancies. We presented new size surrogate data for the head region and for the first time presented ellipticity data for all regions. Future automatic exposure control characterization should include ellipticity information.

A General Framework for Monitoring Image Acquisition Workflow in the Radiology Environment: Timeliness for Acute Stroke CT Imaging.

Many facets of an image acquisition workflow leave a digital footprint, making workflow analysis amenable to an informatics-based solution. This paper describes a detailed framework for analyzing workflow and uses acute stroke response timeliness in CT as a practical demonstration. We review methods for accessing the digital footprints resulting from common technologist/device interactions. This overview lays a foundation for obtaining data for workflow analysis. We demonstrate the method by analyzing CT imaging efficiency in the setting of acute stroke. We successfully used digital footprints of CT technologists to analyze their workflow. We presented an overview of other digital footprints including but not limited to contrast administration, patient positioning, billing, reformat creation, and scheduling. A framework for analyzing image acquisition workflow was presented. This framework is transferable to any modality, as the key steps of image acquisition, image reconstruction, image post processing, and image transfer to PACS are common to any imaging modality in diagnostic radiology.

Variation in CT Number and Image Noise Uniformity According to Patient Positioning in MDCT.

OBJECTIVE: Many algorithms for clinical decision making rely on assessment of the CT number (expressed as Hounsfield units); however, to our knowledge, few, if any, studies have addressed how CT numbers change as a function of patient positioning within the scanner. MATERIALS AND METHODS: An anthropomorphic phantom underwent imaging with varying amounts of vertical orientation misalignment with respect to isocenter. CT number and noise were measured using ROIs in the upper thorax, mid thorax, and abdomen. The degree of noise nonuniformity and changes in the CT number were assessed by comparing values obtained in the anterior versus posterior ROIs. To add clinical relevance, data on vertical mispositioning were collected from 20,316 clinical abdominal CT scans. Box-and-whisker plot analysis was used to identify the range of patient positioning. RESULTS: Absolute CT number changes of more than 20 HU were observed for some ROIs at phantom positions of 10 cm from isocenter, with important differences noted between the thoracic and abdominal regions. Noise uniformity varied by more than twofold for all regions at 10 cm below isocenter. On clinical CT examinations, off-centering of more than 1, 2, 4, and 6 cm occurred for 41%, 19%, 1.9%, and 0.3% of patients, respectively. CONCLUSION: Radiologists should treat CT number measurements with caution when patients are grossly mispositioned in the scanner. The substantial changes in attenuation values shown in the present study are large enough to warrant further investigation.