Comparison of Radiation Dose and Image Quality of Pediatric High-Resolution Chest CT Between Photon-Counting Detector CT and Energy-Integrated Detector CT: A Matched Study.

BACKGROUND. Photon-counting detector (PCD) CT has been shown to reduce radiation dose and improve image quality in adult chest CT examinations; its potential impact in pediatric CT is not well documented. OBJECTIVE. The purpose of our study was to compare radiation dose, objective image quality, and subjective image quality of PCD CT and energy-integrating detector (EID) CT in children undergoing high-resolution CT (HRCT) of the chest. METHODS. This retrospective study included 27 children (median age, 3.9 years; 10 girls, 17 boys) who underwent PCD CT between March 1, 2022, and August 31, 2022, and 27 children (median age, 4.0 years; 13 girls, 14 boys) who underwent EID CT between August 1, 2021, and January 31, 2022; all examinations comprised clinically indicated chest HRCT. The patients in the two groups were matched by age and water-equivalent diameter. Radiation dose parameters were recorded. One observer placed ROIs to measure objective parameters (lung attenuation, image noise, and SNR). Two radiologists independently assessed subjective measures (overall image quality and motion artifacts) using 5-point Likert scales (1 = highest quality). Groups were compared. RESULTS. PCD CT, in comparison with EID CT, showed lower median CTDIvol (0.41 vs 0.71 mGy, p < .001), DLP (10.2 vs 13.7 mGy × cm, p = .008), size-specific dose estimate (0.82 vs 1.34 mGy, p < .001), and tube current-exposure time product (48.0 vs 202.0 mAs, p < .001). PCD CT and EID CT showed no significant difference in right upper lobe (RUL) lung attenuation (mean, -793 vs -750 HU; p = .09), right lower lobe (RLL) lung attenuation (mean, -745 vs -716 HU; p = .23), RUL image noise (mean, 55 vs 51 HU; p = .27), RLL image noise (mean, 59 vs 57 HU; p = .48), RUL SNR (mean, -14.9 vs -15.8; p = .89), or RLL SNR (mean, -13.1 vs -13.6; p = .79). PCD CT and EID CT showed no significant difference in median overall image quality for reader 1 (1.0 vs 1.0, p = .28) or reader 2 (1.0 vs 1.0, p = .17) or median motion artifacts for reader 1 (1.0 vs 1.0, p = .07) or reader 2 (1.0 vs 1.0, p = .22). CONCLUSION. PCD CT showed significantly reduced dose levels without a significant difference in objective or subjective image quality compared with EID CT. CLINICAL IMPACT. These data expand understanding of the capabilities of PCD CT and support its routine use in children.

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

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

Effects of Dual-Energy Technique on Radiation Exposure and Image Quality in Pediatric Body CT.

OBJECTIVE: The purpose of this study was to assess the effects of dual-energy CT (DECT) on radiation exposure and image quality in pediatric body CT. MATERIALS AND METHODS: This retrospective study included 79 children (median age, 10.1 years; range, 12 days-18 years) who underwent thoracic or abdominal-pelvic CT or CT angiography with dual-energy technique between October 2014 and March 2015. The delivered volume CT dose index (CTDIvol) from DECT was recorded and compared with the estimated CTDIvol had the patient undergone scanning with a standard single-energy CT (SECT) protocol. Size-specific dose estimates were calculated for both DECT and SECT. Image quality was subjectively scored (scale, 1-4). For 16 of 79 patients who underwent both DECT and SECT, image contrast and noise were measured and contrast-to-noise ratio calculated. Parametric and nonparametric testing of independent and paired samples was performed. RESULTS: For all 79 studies, actual median CTDIvol and size-specific dose estimate were 3.7 and 5.9 mGy for DECT versus prescanning estimates of 4.4 and 7.7 mGy for SECT, resulting in 12.5% and 11.2% radiation exposure reduction (p < 0.01). Diagnostic image quality was achieved in all patients. In the 16-patient subset, the median CTDIvol values of DECT and SECT were 3.1 and 3.4 mGy (p < 0.05). Median noise was greater with DECT than with SECT (p < 0.01), but the mean contrast-to-noise ratios for the liver and portal vein were similar (liver, p = 0.32; portal vein, p = 0.21). CONCLUSION: In pediatric body CT, the use of DECT results in radiation exposures comparable to or less than those of SECT while maintaining contrast and contrast-to-noise ratio.

CT Dose Optimization in Pediatric Radiology: A Multiyear Effort to Preserve the Benefits of Imaging While Reducing the Risks.

The marked increase in radiation exposure from medical imaging, especially in children, has caused considerable alarm and spurred efforts to preserve the benefits but reduce the risks of imaging. Applying the principles of the Image Gently campaign, data-driven process and quality improvement techniques such as process mapping and flowcharting, cause-and-effect diagrams, Pareto analysis, statistical process control (control charts), failure mode and effects analysis, "lean" or Six Sigma methodology, and closed feedback loops led to a multiyear program that has reduced overall computed tomographic (CT) examination volume by more than fourfold and concurrently decreased radiation exposure per CT study without compromising diagnostic utility. This systematic approach involving education, streamlining access to magnetic resonance imaging and ultrasonography, auditing with comparison with benchmarks, applying modern CT technology, and revising CT protocols has led to a more than twofold reduction in CT radiation exposure between 2005 and 2012 for patients at the authors' institution while maintaining diagnostic utility.

Urinary stone differentiation in patients with large body size using dual-energy dual-source computed tomography.

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.

Evaluation of strategies to reduce radiation dose in perfusion CT imaging using a reproducible biologic phantom.

OBJECTIVE: The purpose of this study is to evaluate radiation dose reduction strategies in perfusion CT by using a biologic phantom. MATERIALS AND METHODS: A formalin-preserved porcine liver was submerged in a 32-cm-wide acrylic phantom filled with water. The portal vein was connected to a continuous flow pump. The phantom was scanned with a perfusion CT protocol using 80 kVp and 400 mAs, every 1 second, for 50 seconds. This was repeated using 100 and 20 mAs. It was also repeated again using 400 mAs to assess reproducibility. A sparser scan frequency was simulated retrospectively. Blood flow was determined for each dataset using the maximum slope and deconvolution methods. RESULTS: Measurements of the mean blood flow values in identical regions of interest had a percent difference of 7% for repeated perfusion CT protocols using the same settings regardless of perfusion model used. The 100 mAs scans agreed with 400 mAs scans, with percent differences of 21% and 31% for the maximum slope and deconvolution methods, respectively. At a simulated frequency of one scan every 4 seconds, blood flow values differed up to 17% and 60% from the reference scan for the maximum slope and deconvolution methods, respectively. At 20 mAs and one scan every 1 second, or 400 mAs and a simulated frequency of one scan every 8 seconds, both computation methods failed to provide accurate blood flow estimates. CONCLUSION: The biologic phantom showed reproducible measurements that can help in optimizing perfusion CT protocols by determining both the acquisition parameters that affect radiation dose and the accuracy of estimates from different perfusion models.

Coronary Artery Calcium Evaluation Using New Generation Photon-counting Computed Tomography Yields Lower Radiation Dose Compared With Standard Computed Tomography.

ABSTRACT: Prospective head-to-head comparison of coronary calcium scores between standard computed tomography (CT) and photon-counting CT show no significant differences, while photon-counting CT administers substantially lower radiation dose.

A strategy to decrease partial scan reconstruction artifacts in myocardial perfusion CT: phantom and in vivo evaluation.

PURPOSE: Partial scan reconstruction (PSR) artifacts are present in myocardial perfusion imaging using dynamic multidetector computed tomography (MDCT). PSR artifacts appear as temporal CT number variations due to inconsistencies in the angular data range used to reconstruct images and compromise the quantitative value of myocardial perfusion when using MDCT. The purpose of this work is to present and evaluate a technique termed targeted spatial frequency filtration (TSFF) to reduce CT number variations due to PSR when applied to myocardial perfusion imaging using MDCT. METHODS: The TSFF algorithm requires acquiring enough X-ray projections to reconstruct both partial (π + fan angle α) and full (2π) scans. Then, using spatial linear filters, the TSFF-corrected image data are created by superimposing the low spatial frequency content of the full scan reconstruction (containing no PSR artifacts, but having low spatial resolution and worse temporal resolution) with the high spatial frequency content of the partial scan reconstruction (containing high spatial frequencies and better temporal resolution). The TSFF method was evaluated both in a static anthropomorphic thoracic phantom and using an in vivo porcine model and compared with a previously validated reference standard technique that avoids PSR artifacts by pacing the animal heart in synchrony with the gantry rotation. CT number variations were quantified by measuring the range and standard deviation of CT numbers in selected regions of interest (ROIs) over time. Myocardial perfusion parameters such as blood volume (BV), mean transit time (MTT), and blood flow (BF) were quantified and compared in the in vivo study. RESULTS: Phantom experiments demonstrated that TSFF reduced PSR artifacts by as much as tenfold, depending on the location of the ROI. For the in vivo experiments, the TSFF-corrected data showed two- to threefold decrease in CT number variations. Also, after TSFF, the perfusion parameters had an average difference of 13.1% (range 4.5%-25.6%) relative to the reference method, in contrast to an average difference of 31.8% (range 0.3%-54.0%) between the non-TSFF processed data with the reference method. CONCLUSIONS: TSFF demonstrated consistent reduction in CT number variations due to PSR using controlled phantom and in vivo experiments. TSFF-corrected data provided quantitative measures of perfusion (BV, MTT, and BF) with better agreement to a reference method compared to noncorrected data. Practical implementation of TSFF is expected to incur in an additional radiation exposure of 14%, when tube current is modulated to 20% of its maximum, to complete the needed full scan reconstruction.

Prognostic Value of Computed Tomography-Derived Fractional Flow Reserve Comparison With Myocardial Perfusion Imaging.

OBJECTIVES: The aim of this study was to compare the incremental prognostic value of coronary computed tomography (CT) angiography (CCTA)-derived machine learning fractional flow reserve CT (ML-FFRct) versus that of ischemia detected on single-photon emission-computed tomography (SPECT) myocardial perfusion imaging (MPI) on incident cardiovascular outcomes. BACKGROUND: SPECT MPI and ML-FFRct are noninvasive tools that can assess the hemodynamic significance of coronary atherosclerotic disease. METHODS: We studied a retrospective cohort of consecutive patients who underwent clinically indicated CCTA and SPECT MPI. ML-FFRct was computed using a ML prototype. The primary outcome was all-cause mortality and nonfatal myocardial infarction (D/MI), and the secondary outcome was D/MI and unplanned revascularization, percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG) occurring more than 90 days postimaging. Multiple nested multivariate cox regression was used to model a scenario wherein an initial anatomical assessment was followed by a functional assessment. RESULTS: A total of 471 patients (mean age: 64 ± 13 year; 53% males) were included. Comorbidities were prevalent (78% hypertension, 66% diabetes, 81% dyslipidemia). ML-FFRct was <0.8 in at least 1 proximal/midsegment was present in 41.6% of patients, and ischemia on MPI was present in 13.8%. After a median follow-up of 18 months, 7% of patients (n = 33) experienced D/MI. On multivariate Cox proportional analysis, the presence of ischemia on MPI but not ML-FFRct significantly predicted D/MI (HR: 2.3; 95% CI: 1.0-5.0; P = 0.047; or HR: 0.7; 95% CI: 0.3-1.4; P = 0.306 respectively) when added to CCTA obstructive stenosis. Furthermore, the model with SPECT ischemia had higher global chi-square result and significantly improved reclassification. Results were similar using the secondary outcome and on several sensitivity analyses. CONCLUSIONS: In a high-risk patient cohort, SPECT MPI but not ML-FFRct adds independent and incremental prognostic information to CCTA-based anatomical assessment and clinical risk factors in predicting incident outcomes.

Sex differences in machine learning computed tomography-derived fractional flow reserve.

Coronary computed tomography angiography (CCTA) derived machine learning fractional flow reserve (ML-FFRCT) can assess the hemodynamic significance of coronary artery stenoses. We aimed to assess sex differences in the association of ML-FFRCT and incident cardiovascular outcomes. We studied a retrospective cohort of consecutive patients who underwent clinically indicated CCTA and single photon emission computed tomography (SPECT). Obstructive stenosis was defined as ≥ 70% stenosis severity in non-left main vessels or ≥ 50% in the left main coronary. ML-FFRCT was computed using a machine learning algorithm with significant stenosis defined as ML-FFRCT < 0.8. The primary outcome was a composite of death or non-fatal myocardial infarction (D/MI). Our study population consisted of 471 patients with mean (SD) age 65 (13) years, 53% men, and multiple comorbidities (78% hypertension, 66% diabetes, 81% dyslipidemia). Compared to men, women were less likely to have obstructive stenosis by CCTA (9% vs. 18%; p = 0.006), less multivessel CAD (4% vs. 6%; p = 0.25), lower prevalence of ML-FFRCT < 0.8 (39% vs. 44%; p = 0.23) and higher median (IQR) ML-FFRCT (0.76 (0.53-0.86) vs. 0.71 (0.47-0.84); p = 0.047). In multivariable adjusted models, there was no significant association between ML-FFRCT < 0.8 and D/MI [Hazard Ratio 0.82, 95% confidence interval (0.30, 2.20); p = 0.25 for interaction with sex.]. In a high-risk cohort of symptomatic patients who underwent CCTA and SPECT testing, ML-FFRCT was higher in women than men. There was no significant association between ML-FFRCT and incident mortality or MI and no evidence that the prognostic value of ML-FFRCT differs by sex.

Interoperator reliability of an on-site machine learning-based prototype to estimate CT angiography-derived fractional flow reserve.

BACKGROUND: Advances in CT and machine learning have enabled on-site non-invasive assessment of fractional flow reserve (FFRCT). PURPOSE: To assess the interoperator and intraoperator variability of coronary CT angiography-derived FFRCT using a machine learning-based postprocessing prototype. MATERIALS AND METHODS: We included 60 symptomatic patients who underwent coronary CT angiography. FFRCT was calculated by two independent operators after training using a machine learning-based on-site prototype. FFRCT was measured 1 cm distal to the coronary plaque or in the middle of the segments if no coronary lesions were present. Intraclass correlation coefficient (ICC) and Bland-Altman analysis were used to evaluate interoperator variability effect in FFRCT estimates. Sensitivity analysis was done by cardiac risk factors, degree of stenosis and image quality. RESULTS: A total of 535 coronary segments in 60 patients were assessed. The overall ICC was 0.986 per patient (95% CI 0.977 to 0.992) and 0.972 per segment (95% CI 0.967 to 0.977). The absolute mean difference in FFRCT estimates was 0.012 per patient (95% CI for limits of agreement: -0.035 to 0.039) and 0.02 per segment (95% CI for limits of agreement: -0.077 to 0.080). Tight limits of agreement were seen on Bland-Altman analysis. Distal segments had greater variability compared with proximal/mid segments (absolute mean difference 0.011 vs 0.025, p<0.001). Results were similar on sensitivity analysis. CONCLUSION: A high degree of interoperator and intraoperator reproducibility can be achieved by on-site machine learning-based FFRCT assessment. Future research is required to evaluate the physiological relevance and prognostic value of FFRCT.

Dual-energy dual-source CT with additional spectral filtration can improve the differentiation of non-uric acid renal stones: an ex vivo phantom study.

OBJECTIVE: The purpose of this study was to determine the ex vivo ability of dual-energy dual-source CT (DSCT) with additional tin filtration to differentiate among five groups of human renal stone types. MATERIALS AND METHODS: Forty-three renal stones of 10 types were categorized into five primary groups on the basis of effective atomic numbers, which were calculated as the weighted average of the atomic numbers of constituent atoms. Stones were embedded in porcine kidneys and placed in a 35-cm water phantom. Dual-energy DSCT scans were performed at 80 and 140 kV with and without tin filtration of the 140-kV beam. The CT number ratio, defined as the ratio of the CT number of a given material in the low-energy image to the CT number of the same material in the high-energy image, was calculated on a volumetric voxel-by-voxel basis for each stone. Statistical analysis was performed, and receiver operating characteristic (ROC) curves were plotted to compare the difference in CT number ratio with and without tin filtration, and to measure the discrimination among stone groups. RESULTS: The CT number ratio of non-uric acid stones increased on average by 0.17 (range, 0.03-0.36) with tin filtration. The CT number ratios for non-uric acid stone groups were not significantly different (p > 0.05) between any of the two adjacent groups without tin filtration. Use of the additional tin filtration on the high-energy x-ray tube significantly improved the separation of non-uric acid stone types by CT number ratio (p < 0.05). The area under the ROC curve increased from 0.78 to 0.84 without fin filtration and to 0.89-0.95 with tin filtration. CONCLUSION: Our results showed better separation among different stone types when additional tin filtration was used on dual-energy DSCT. The increased spectral separation allowed a five-group stone classification scheme. Some overlapping between particular stone types still exists, including brushite and calcium oxalate.

Deep learning based spectral extrapolation for dual-source, dual-energy x-ray computed tomography.

PURPOSE: Data completion is commonly employed in dual-source, dual-energy computed tomography (CT) when physical or hardware constraints limit the field of view (FoV) covered by one of two imaging chains. Practically, dual-energy data completion is accomplished by estimating missing projection data based on the imaging chain with the full FoV and then by appropriately truncating the analytical reconstruction of the data with the smaller FoV. While this approach works well in many clinical applications, there are applications which would benefit from spectral contrast estimates over the larger FoV (spectral extrapolation)-e.g. model-based iterative reconstruction, contrast-enhanced abdominal imaging of large patients, interior tomography, and combined temporal and spectral imaging. METHODS: To document the fidelity of spectral extrapolation and to prototype a deep learning algorithm to perform it, we assembled a data set of 50 dual-source, dual-energy abdominal x-ray CT scans (acquired at Duke University Medical Center with 5 Siemens Flash scanners; chain A: 50 cm FoV, 100 kV; chain B: 33 cm FoV, 140 kV + Sn; helical pitch: 0.8). Data sets were reconstructed using ReconCT (v14.1, Siemens Healthineers): 768 × 768 pixels per slice, 50 cm FoV, 0.75 mm slice thickness, "Dual-Energy - WFBP" reconstruction mode with dual-source data completion. A hybrid architecture consisting of a learned piecewise linear transfer function (PLTF) and a convolutional neural network (CNN) was trained using 40 scans (five scans reserved for validation, five for testing). The PLTF learned to map chain A spectral contrast to chain B spectral contrast voxel-wise, performing an image domain analog of dual-source data completion with approximate spectral reweighting. The CNN with its U-net structure then learned to improve the accuracy of chain B contrast estimates by copying chain A structural information, by encoding prior chain A, chain B contrast relationships, and by generalizing feature-contrast associations. Training was supervised, using data from within the 33-cm chain B FoV to optimize and assess network performance. RESULTS: Extrapolation performance on the testing data confirmed our network's robustness and ability to generalize to unseen data from different patients, yielding maximum extrapolation errors of 26 HU following the PLTF and 7.5 HU following the CNN (averaged per target organ). Degradation of network performance when applied to a geometrically simple phantom confirmed our method's reliance on feature-contrast relationships in correctly inferring spectral contrast. Integrating our image domain spectral extrapolation network into a standard dual-source, dual-energy processing pipeline for Siemens Flash scanner data yielded spectral CT data with adequate fidelity for the generation of both 50 keV monochromatic images and material decomposition images over a 30-cm FoV for chain B when only 20 cm of chain B data were available for spectral extrapolation. CONCLUSIONS: Even with a moderate amount of training data, deep learning methods are capable of robustly inferring spectral contrast from feature-contrast relationships in spectral CT data, leading to spectral extrapolation performance well beyond what may be expected at face value. Future work reconciling spectral extrapolation results with original projection data is expected to further improve results in outlying and pathological cases.

Porcine ex vivo liver phantom for dynamic contrast-enhanced computed tomography: development and initial results.

OBJECTIVES: : To demonstrate the feasibility of developing a fixed, dual-input, biologic liver phantom for dynamic contrast-enhanced computed tomography (CT) imaging and to report initial results of use of the phantom for quantitative CT perfusion imaging. MATERIALS AND METHODS: : Porcine livers were obtained from completed surgical studies and perfused with saline and fixative. The phantom was placed in a body-shaped, CT-compatible acrylic container and connected to a perfusion circuit fitted with a contrast injection port. Flow-controlled contrast-enhanced imaging experiments were performed using 128-slice and 64-slice dual-source multidetector CT scanners. CT angiography protocols were used to obtain portal venous and hepatic arterial vascular enhancement, reproduced over a period of 4 to 6 months. CT perfusion protocols were used at different input flow rates to correlate input flow with calculated tissue perfusion, to test reproducibility, and to determine the feasibility of simultaneous dual-input liver perfusion. Histologic analysis of the liver phantom was also performed. RESULTS: : CT angiogram 3-dimensional reconstructions demonstrated homogenous tertiary and quaternary branching of the portal venous system to the periphery of all lobes of the liver as well as enhancement of the hepatic arterial system to all lobes of the liver and gallbladder throughout the study period. For perfusion CT, the correlation between the calculated mean tissue perfusion in a volume of interest and input pump flow rate was excellent (R = 0.996) and color blood flow maps demonstrated variations in regional perfusion in a narrow range. Repeat perfusion CT experiments demonstrated reproducible time-attenuation curves, and dual-input perfusion CT experiments demonstrated that simultaneous dual input liver perfusion is feasible. Histologic analysis demonstrated that the hepatic microvasculature and architecture appeared intact and well preserved at the completion of 4 to 6 months of laboratory experiments and contrast-enhanced imaging. CONCLUSIONS: : We have demonstrated successful development of a porcine liver phantom using a flow-controlled extracorporeal perfusion circuit. This phantom exhibited reproducible dynamic contrast-enhanced CT of the hepatic arterial and portal venous system over a 4- to 6-month period.

Radiation dose reduction in computed tomography: techniques and future perspective.

Despite universal consensus that computed tomography (CT) overwhelmingly benefits patients when used for appropriate indications, concerns have been raised regarding the potential risk of cancer induction from CT due to the exponentially increased use of CT in medicine. Keeping radiation dose as low as reasonably achievable, consistent with the diagnostic task, remains the most important strategy for decreasing this potential risk. This article summarizes the general technical strategies that are commonly used for radiation dose management in CT. Dose-management strategies for pediatric CT, cardiac CT, dual-energy CT, CT perfusion and interventional CT are specifically discussed, and future perspectives on CT dose reduction are presented.