A Novel Approach to Detecting Contrast Extravasation in Computed Tomography: Evaluating the Injection Pressure-to-Injection Rate Ratio.

OBJECTIVE: The purpose of this study was to evaluate the usefulness of the injection pressure-to-injection rate (IPIR) ratio for the early detection of contrast extravasation at the venipuncture site during contrast-enhanced computed tomography. METHODS: We retrospectively enrolled 57,528 patients who underwent contrast-enhanced computed tomography examinations in a single hospital. The power injector recorded the contrast injection pressure at 0.25-second intervals. We constructed logistic regression models using the IPIR ratio as the independent variable and extravasation occurrence as the dependent variable (IPIR ratio models) at 1, 2, 3, 4, 5, and 6 seconds after the start of contrast administration. Univariate logistic regression models in which injection pressure is used as an independent variable (injection pressure models) were also constructed as a reference baseline. The performance of the models was evaluated with the area under the receiver operating characteristic curves. RESULTS: Of the 57,528 cases, 46,022 were assigned to the training group and 11,506 were assigned to the test group, which included 112 extravasation cases (0.24%) in the training group and 28 (0.24%) in the test group. The area under the receiver operating characteristic curves for the IPIR ratio models and injection pressure models were 0.555 versus 0.563 at t = 1 (P = 0.270), 0.712 versus 0.678 at t = 2 (P = 0.305), 0.758 versus 0.693 at t = 3 (P = 0.032), 0.776 versus 0.688 at t = 4 (P = 0.005), 0.810 versus 0.699 at t = 5 (P = 0.002), and 0.811 versus 0.706 at t = 6 (P = 0.002). CONCLUSIONS: The IPIR ratio models perform better in detecting contrast extravasation at 3 to 6 seconds after the start of contrast administration than injection pressure models.

Diagnostic performance of unenhanced electrocardiogram-gated cardiac CT for detecting myocardial edema.

To assess the diagnostic performance of unenhanced electrocardiogram (ECG)-gated cardiac computed tomography (CT) for detecting myocardial edema, using MRI T2 mapping as the reference standard. This retrospective study protocol was approved by our institutional review board, which waived the requirement for written informed consent. Between December 2017 to February 2019, consecutive patients who had undergone T2 mapping for myocardial tissue characterization were identified. We excluded patients who did not undergo unenhanced ECG-gated cardiac CT within 3 months from MRI T2 mapping or who had poor CT image quality. All patients underwent unenhanced ECG-gated cardiac CT with an axial scan using a third-generation, 320 × 0.5 mm detector-row CT unit. Two radiologists together drew regions of interest (ROIs) in the interventricular septum on the unenhanced ECG-gated cardiac CT images. Using T2 mapping as the reference standard, the diagnostic performance of unenhanced cardiac CT for detecting myocardial edema was evaluated by using the area under the receiver operating characteristic curve with sensitivity and specificity. Youden index was used to find an optimal sensitivity-specificity cutoff point. A cardiovascular radiologist independently performed the measurements, and interobserver reliability was assessed using intraclass correlation coefficients for CT value measurements. A P value of <.05 was considered statistically significant. We included 257 patients who had undergone MRI T2 mapping. Of the 257 patients, 35 patients underwent unenhanced ECG-gated cardiac CT. One patient was excluded from the study because of poor CT image quality. Finally, 34 patients (23 men; age 64.7 ±â€…14.6 years) comprised our study group. Using T2 mapping, we identified myocardial edema in 19 patients. Mean CT and T2 values for 34 patients were 46.3 ±â€…2.7 Hounsfield unit and 49.0 ±â€…4.9 ms, respectively. Mean CT values moderately correlated with mean T2 values (Rho = -0.41; P < .05). Mean CT values provided a sensitivity of 63.2% and a specificity of 93.3% for detecting myocardial edema, with a cutoff value of ≤45.0 Hounsfield unit (area under the receiver operating characteristic curve = 0.77; P < .01). Inter-observer reproducibility in measuring mean CT values was excellent (intraclass correlation coefficient = 0.93; [95% confidence interval: 0.86, 0.96]). Myocardial edema could be detected by CT value of myocardium in unenhanced ECG-gated cardiac CT.

Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography.

OBJECTIVES: To evaluate the effect of super-resolution deep-learning-based reconstruction (SR-DLR) on the image quality of coronary CT angiography (CCTA). METHODS: Forty-one patients who underwent CCTA using a 320-row scanner were retrospectively included. Images were reconstructed with hybrid (HIR), model-based iterative reconstruction (MBIR), normal-resolution deep-learning-based reconstruction (NR-DLR), and SR-DLR algorithms. For each image series, image noise, and contrast-to-noise ratio (CNR) at the left main trunk, right coronary artery, left anterior descending artery, and left circumflex artery were quantified. Blooming artifacts from calcified plaques were measured. Image sharpness, noise magnitude, noise texture, edge smoothness, overall quality, and delineation of the coronary wall, calcified and noncalcified plaques, cardiac muscle, and valves were subjectively ranked on a 4-point scale (1, worst; 4, best). The quantitative parameters and subjective scores were compared among the four reconstructions. Task-based image quality was assessed with a physical evaluation phantom. The detectability index for the objects simulating the coronary lumen, calcified plaques, and noncalcified plaques was calculated from the noise power spectrum (NPS) and task-based transfer function (TTF). RESULTS: SR-DLR yielded significantly lower image noise and blooming artifacts with higher CNR than HIR, MBIR, and NR-DLR (all p < 0.001). The best subjective scores for all the evaluation criteria were attained with SR-DLR, with significant differences from all other reconstructions (p < 0.001). In the phantom study, SR-DLR provided the highest NPS average frequency, TTF50%, and detectability for all task objects. CONCLUSION: SR-DLR considerably improved the subjective and objective image qualities and object detectability of CCTA relative to HIR, MBIR, and NR-DLR algorithms. CLINICAL RELEVANCE STATEMENT: The novel SR-DLR algorithm has the potential to facilitate accurate assessment of coronary artery disease on CCTA by providing excellent image quality in terms of spatial resolution, noise characteristics, and object detectability. KEY POINTS: • SR-DLR designed for CCTA improved image sharpness, noise property, and delineation of cardiac structures with reduced blooming artifacts from calcified plaques relative to HIR, MBIR, and NR-DLR. • In the task-based image-quality assessments, SR-DLR yielded better spatial resolution, noise property, and detectability for objects simulating the coronary lumen, coronary calcifications, and noncalcified plaques than other reconstruction techniques. • The image reconstruction times of SR-DLR were shorter than those of MBIR, potentially serving as a novel standard-of-care reconstruction technique for CCTA performed on a 320-row CT scanner.

Noise power spectrum properties of deep learning-based reconstruction and iterative reconstruction algorithms: Phantom and clinical study.

PURPOSE: To compare the noise power spectrum (NPS) properties and perform a qualitative analysis of hybrid iterative reconstruction (IR), model-based IR (MBIR), and deep learning-based reconstruction (DLR) at a similar noise level in clinical study and compare these outcomes with those in phantom study. METHODS: A Catphan phantom with an external body ring was used in the phantom study. In the clinical study, computed tomography (CT) examination data of 34 patients were reviewed. NPS was calculated from DLR, hybrid IR, and MBIR images. The noise magnitude ratio (NMR) and the central frequency ratio (CFR) were calculated from DLR, hybrid IR, and MBIR images relative to filtered back-projection images using NPS. Clinical images were independently reviewed by two radiologists. RESULTS: In the phantom study, DLR with a mild level had a similar noise level as hybrid IR and MBIR with strong levels. In the clinical study, DLR with a mild level had a similar noise level as hybrid IR with standard and MBIR with strong levels. The NMR and CFR were 0.40 and 0.76 for DLR, 0.42 and 0.55 for hybrid IR, and 0.48 and 0.62 for MBIR. The visual inspection of the clinical DLR image was superior to that of the hybrid IR and MBIR images. CONCLUSION: Deep learning-based reconstruction improves overall image quality with substantial noise reduction while maintaining image noise texture compared with the CT reconstruction techniques.

Coronary Stent Evaluation by CTA: Image Quality Comparison Between Super-Resolution Deep Learning Reconstruction and Other Reconstruction Algorithms.

BACKGROUND. A super-resolution deep learning reconstruction (SR-DLR) algorithm may provide better image sharpness than earlier reconstruction algorithms and thereby improve coronary stent assessment on coronary CTA. OBJECTIVE. The purpose of our study was to compare SR-DLR and other reconstruction algorithms in terms of image quality measures related to coronary stent evaluation in patients undergoing coronary CTA. METHODS. This retrospective study included patients with at least one coronary artery stent who underwent coronary CTA between January 2020 and December 2020. Examinations were performed using a 320-row normal-resolution scanner and were reconstructed with hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), normal-resolution deep learning reconstruction (NR-DLR), and SR-DLR algorithms. Quantitative image quality measures were determined. Two radiologists independently reviewed images to rank the four reconstructions (4-point scale: 1 = worst reconstruction, 4 = best reconstruction) for qualitative measures and to score diagnostic confidence (5-point scale: score ≥ 3 indicating an assessable stent). The assessability rate was calculated for stents with a diameter of 3.0 mm or less. RESULTS. The sample included 24 patients (18 men, six women; mean age, 72.5 ± 9.8 [SD] years), with 51 stents. SR-DLR, in comparison with the other reconstructions, yielded lower stent-related blooming artifacts (median, 40.3 vs 53.4-58.2), stent-induced attenuation increase ratio (0.17 vs 0.27-0.31), and quantitative image noise (18.1 vs 20.9-30.4 HU) and higher in-stent lumen diameter (2.4 vs 1.7-1.9 mm), stent strut sharpness (327 vs 147-210 ΔHU/mm), and CNR (30.0 vs 16.0-25.6) (all p < .001). For both observers, all ranked measures (image sharpness; image noise; noise texture; delineation of stent strut, in-stent lumen, coronary artery wall, and calcified plaque surrounding the stent) and diagnostic confidence showed a higher score for SR-DLR (median, 4.0 for all features) than for the other reconstructions (range, 1.0-3.0) (all p < .001). The assessability rate for stents with a diameter of 3.0 mm or less (n = 37) was higher for SR-DLR (86.5% for observer 1 and 89.2% for observer 2) than for HIR (35.1% and 43.2%), MBIR (59.5% and 62.2%), and NR-DLR (62.2% and 64.9%) (all p < .05). CONCLUSION. SR-DLR yielded improved delineation of the stent strut and in-stent lumen, with better image sharpness and less image noise and blooming artifacts, in comparison with HIR, MBIR, and NR-DLR. CLINICAL IMPACT. SR-DLR may facilitate coronary stent assessment on a 320-row normal-resolution scanner, particularly for small-diameter stents.

Deep learning-based reconstruction can improve the image quality of low radiation dose head CT.

OBJECTIVES: To evaluate the image quality of deep learning-based reconstruction (DLR), model-based (MBIR), and hybrid iterative reconstruction (HIR) algorithms for lower-dose (LD) unenhanced head CT and compare it with those of standard-dose (STD) HIR images. METHODS: This retrospective study included 114 patients who underwent unenhanced head CT using the STD (n = 57) or LD (n = 57) protocol on a 320-row CT. STD images were reconstructed with HIR; LD images were reconstructed with HIR (LD-HIR), MBIR (LD-MBIR), and DLR (LD-DLR). The image noise, gray and white matter (GM-WM) contrast, and contrast-to-noise ratio (CNR) at the basal ganglia and posterior fossa levels were quantified. The noise magnitude, noise texture, GM-WM contrast, image sharpness, streak artifact, and subjective acceptability were independently scored by three radiologists (1 = worst, 5 = best). The lesion conspicuity of LD-HIR, LD-MBIR, and LD-DLR was ranked through side-by-side assessments (1 = worst, 3 = best). Reconstruction times of three algorithms were measured. RESULTS: The effective dose of LD was 25% lower than that of STD. Lower image noise, higher GM-WM contrast, and higher CNR were observed in LD-DLR and LD-MBIR than those in STD (all, p ≤ 0.035). Compared with STD, the noise texture, image sharpness, and subjective acceptability were inferior for LD-MBIR and superior for LD-DLR (all, p < 0.001). The lesion conspicuity of LD-DLR (2.9 ± 0.2) was higher than that of HIR (1.2 ± 0.3) and MBIR (1.8 ± 0.4) (all, p < 0.001). Reconstruction times of HIR, MBIR, and DLR were 11 ± 1, 319 ± 17, and 24 ± 1 s, respectively. CONCLUSION: DLR can enhance the image quality of head CT while preserving low radiation dose level and short reconstruction time. KEY POINTS: • For unenhanced head CT, DLR reduced the image noise and improved the GM-WM contrast and lesion delineation without sacrificing the natural noise texture and image sharpness relative to HIR. • The subjective and objective image quality of DLR was better than that of HIR even at 25% reduced dose without considerably increasing the image reconstruction times (24 s vs. 11 s). • Despite the strong noise reduction and improved GM-WM contrast performance, MBIR degraded the noise texture, sharpness, and subjective acceptance with prolonged reconstruction times relative to HIR, potentially hampering its feasibility.

Lung-Optimized Deep-Learning-Based Reconstruction for Ultralow-Dose CT.

RATIONALE AND OBJECTIVES: To evaluate the image properties of lung-specialized deep-learning-based reconstruction (DLR) and its applicability in ultralow-dose CT (ULDCT) relative to hybrid- (HIR) and model-based iterative-reconstructions (MBIR). MATERIALS AND METHODS: An anthropomorphic chest phantom was scanned on a 320-row scanner at 50-mA (low-dose-CT 1 [LDCT-1]), 25-mA (LDCT-2), and 10-mA (ULDCT). LDCT were reconstructed with HIR; ULDCT images were reconstructed with HIR (ULDCT-HIR), MBIR (ULDCT-MBIR), and DLR (ULDCT-DLR). Image noise and contrast-to-noise ratio (CNR) were quantified. With the LDCT images as reference standards, ULDCT image qualities were subjectively scored on a 5-point scale (1 = substantially inferior to LDCT-2, 3 = comparable to LDCT-2, 5 = comparable to LDCT-1). For task-based image quality analyses, a physical evaluation phantom was scanned at seven doses to achieve the noise levels equivalent to chest phantom; noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated. Clinical ULDCT (10-mA) images obtained in 14 nonobese patients were reconstructed with HIR, MBIR, and DLR; the subjective acceptability was ranked. RESULTS: Image noise was lower and CNR was higher in ULDCT-DLR and ULDCT-MBIR than in LDCT-1, LDCT-2, and ULDCT-HIR (p < 0.01). The overall quality of ULDCT-DLR was higher than of ULDCT-HIR and ULDCT-MBIR (p < 0.01), and almost comparable with that of LDCT-2 (mean score: 3.4 ± 0.5). DLR yielded the highest NPS peak frequency and TTF50% for high-contrast object. In clinical ULDCT images, the subjective acceptability of DLR was higher than of HIR and MBIR (p < 0.01). CONCLUSION: DLR optimized for lung CT improves image quality and provides possible greater dose optimization opportunity than HIR and MBIR.

Myocardial extracellular volume quantification by cardiac CT in pulmonary hypertension: Comparison with cardiac MRI.

PURPOSE: Myocardial extracellular volume (ECV) measured by cardiac magnetic resonance imaging (MRI) has been suggested as a marker of disease severity in pulmonary hypertension (PH). However, consistency between ECVs quantified by computed tomography (CT) and MRI has not been sufficiently investigated in (PH). We investigated the utility of CT-ECV in PH, using MRI-ECV as a reference standard. METHOD: We evaluated 20 patients with known or suspected PH who underwent dual-energy CT, cardiac MRI, and right heart catheterization. We used Pearson correlation analysis to investigate correlations between CT-ECV and MRI-ECV. We also assessed correlations between ECV and mean pulmonary artery pressure (mPAP). RESULTS: CT-ECV showed a very strong correlation with MRI-ECV at the anterior (r = 0.83) and posterior right ventricular insertion points (RVIPs) (r = 0.84). CT-ECV and MRI-ECV were strongly correlated in the septum and left ventricular free wall (r = 0.79-0.73) but weakly correlated in the right ventricular free wall (r = 0.26). CT-ECV showed a strong correlation with mPAP in the anterior RVIP (r = 0.64) and a moderate correlation in the posterior RVIP and septum (r = 0.50-0.42). Compared with CT-ECV, MRI-ECV had a higher correlation with mPAP; however, the difference was not significant (anterior RVIP, r = 0.72 [MRI-ECV] vs. 0.64 [CT-ECV], p = 0.663; posterior RVIP, r = 0.67 vs. 0.50, p = 0.446). CONCLUSION: Dual-energy CT can quantify myocardial ECV and yield results comparable to those obtained using cardiac MRI. CT-ECV in the anterior RVIP could be a noninvasive surrogate marker of disease severity in PH.

Radiation dose optimization potential of deep learning-based reconstruction for multiphase hepatic CT: A clinical and phantom study.

PURPOSE: This clinical and phantom study aimed to evaluate the impact of deep learning-based reconstruction (DLR) on image quality and its radiation dose optimization capability for multiphase hepatic CT relative to hybrid iterative reconstruction (HIR). METHODS: Task-based image quality was assessed with a physical evaluation phantom; the high- and low-contrast detectability of HIR and DLR images were computed from the noise power spectrum and task-based transfer function at five different size-specific dose estimate (SSDE) values in the range 5.3 to 18.0-mGy. For the clinical study, images of 73 patients who had undergone multiphase hepatic CT under both standard-dose (STD) and lower-dose (LD) examination protocols within a time interval of about four-months on average, were retrospectively examined. STD images were reconstructed with HIR, while LD with HIR (LD-HIR) and DLR (LD-DLR). SSDE, quantitative image noise, and contrast-to-noise ratio (CNR) were compared between protocols. The noise magnitude, noise texture, streak artifact, image sharpness, interface smoothness, and overall image quality were subjectively rated by two independent radiologists. RESULTS: In phantom study, the high- and low-contrast detectability of DLR images obtained at 5.3-mGy and 7.3-mGy, respectively, were slightly higher than those obtained with HIR at the STD protocol dose (18.0-mGy). In clinical study, LD-DLR yielded lower image noise, higher CNR, and higher subjective scores for all evaluation criteria than STD (all, p ≤ 0.05), despite having 52.8% lower SSDE (8.0 ± 2.5 vs. 16.8 ± 3.4-mGy). CONCLUSIONS: DLR improved the subjective and objective image quality of multiphase hepatic CT compared with HIR techniques, even at approximately half the radiation dose.

Practical Preventive Strategies for Extravasation of Contrast Media During CT: What the Radiology Team Should Do.

RATIONALE AND OBJECTIVES: This study aimed to assess the effectiveness of practical preventive strategies (i.e., venous vulnerability assessment and prevention scan protocol rules) taken by our radiology team (radiology nurses, radiology technicians, radiologists) on reducing extravasation of contrast media (ECM) during CT. MATERIALS AND METHODS: A total of 73,931 patients who underwent contrast-enhanced CT scans between January 2013 and December 2019 were retrospectively included. Venous vulnerability assessment by the radiology team began in 2015, and prevention scan protocol rules for the prevention of ECM were added in 2017. We defined each period as follows: 2013-2014, no prevention (Period A); 2015-2016, early prevention (Period B, venous vulnerability assessment only); and 2017-2019: late prevention (Period C, venous vulnerability assessment with prevention scan protocol rules). The incident reports, radiology reports, and medical records of patients in whom ECM occurred were reviewed. We compared the frequency of ECM during each period. RESULTS: ECM occurred in 0.39% (292/73,931) of the patients. The frequencies of ECM for Periods A, B, and C were 0.62% (121/19,505), 0.43% (89/20,847), and 0.24% (82/33,579), respectively. There were significant differences in the frequencies of ECM among the three periods (Chi-squared test, p < 0.01). CONCLUSION: Implementation of venous vulnerability assessment and prevention scan protocol rules by a radiology team can be a practical and simple solution to reduce the risk of ECM during CT.

Radiation Dose Reduction for 80-kVp Pediatric CT Using Deep Learning-Based Reconstruction: A Clinical and Phantom Study.

BACKGROUND. Deep learning-based reconstruction (DLR) may facilitate CT radiation dose reduction, but a paucity of literature has compared lower-dose DLR images with standard-dose iterative reconstruction (IR) images or explored application of DLR to low-tube-voltage scanning in children. OBJECTIVE. The purpose of this study was to assess whether DLR can be used to reduce radiation dose while maintaining diagnostic image quality in comparison with hybrid IR (HIR) and model-based IR (MBIR) for low-tube-voltage pediatric CT. METHODS. This retrospective study included children 6 years old or younger who underwent contrast-enhanced 80-kVp CT with a standard-dose or lower-dose protocol. Standard images were reconstructed with HIR, and lower-dose images were reconstructed with HIR, MBIR, and DLR. Size-specific dose estimate (SSDE) was calculated for both protocols. Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were quantified. Two radiologists independently evaluated noise magnitude, noise texture, streak artifact, edge sharpness, and overall quality. Interreader agreement was assessed, and mean values were calculated. To evaluate task-based object detection performance, a phantom was imaged with 80-kVp CT at six doses (SSDE, 0.6-5.3 mGy). Detectability index (d') was calculated from the noise power spectrum and task-based transfer function. Reconstruction methods were compared. RESULTS. Sixty-five children (mean age, 25.0 ± 25.2 months) who underwent CT with standard- (n = 31) or lower-dose (n = 34) protocol were included. SSDE was 54% lower for the lower-dose than for the standard-dose group (1.9 ± 0.4 vs 4.1 ± 0.8 mGy). Lower-dose DLR and MBIR yielded lower image noise and higher SNR and CNR than standard-dose HIR (p < .05). Interobserver agreement on subjective features ranged from a kappa coefficient of 0.68 to 0.78. The readers subjectively scored noise texture, edge sharpness, and overall quality lower for lower-dose MBIR than for standard-dose HIR (p < .001), though higher for lower-dose DLR than for standard-dose HIR (p < .001). In the phantom, DLR provided higher d' than HIR and MBIR at each dose. Object detectability was greater for 2.0-mGy DLR than for 4.0-mGy HIR for low-contrast (3.67 vs 3.57) and high-contrast (1.20 vs 1.04) objects. CONCLUSION. Compared with IR algorithms, DLR results in substantial dose reduction with preserved or even improved image quality for low-tube-voltage pediatric CT. CLINICAL IMPACT. Use of DLR at 80 kVp allows greater dose reduction for pediatric CT than do current IR techniques.

Comparison of the effects of varying tube voltage and iodinated concentration on increasing the iodinated radiation dose in computed tomography.

PURPOSE: To compare the effects of tube voltage and iodinated concentration on increasing the iodinated radiation dose in computed tomography (CT). MATERIAL AND METHODS: Water and iodinated materials were inserted in an anthropomorphic thorax phantom. Helical CT scans were performed with tube voltages of 80 and 120 kV. Monte Carlo simulation of radiation doses in CT images was used to study the radiation dose profiles. The radiation doses at different iodine to water ratios in images were calculated from cumulative radiation doses for iodine and water at each iodine concentration, tube voltage, and peripheral/central location. A radiation dose ratio from 80 to 120 kV was calculated from cumulative radiation doses at the same iodine concentration. RESULTS: The iodinated radiation doses with small and large phantoms were 1.56-2.04 and 1.61-1.82 times higher at 80 kV and 1.55-2.23 and 1.22-1.79 times higher at 120 kV than those in water. In the central portion, the iodinated radiation dose ratio decreased by 1.14-0.75 and 1.20-0.93 times at 80 kV and 1.29-1.23 and 1.17-1.23 times at 120 kV with increasing iodinated concentrations. In the peripheral portion, the iodinated radiation dose was slightly higher for 80 kV than for 120 kV. In the central portion, the ratio decreased with increasing iodinated concentration. CONCLUSION: The increase in iodinated radiation dose caused by photoelectric absorption was greater with increased iodine load than with lower tube voltage.

Effects of tube voltage and iodine contrast medium on radiation dose of whole-body CT.

BACKGROUND: The low-tube-voltage scan generally needs a higher tube current than the conventional 120 kVp to maintain the image noise. In addition, the low-tube-voltage scan increases the photoelectric effect, which increases the radiation absorption in organs. PURPOSE: To compare the organ radiation dose caused by iodine contrast medium between low tube voltage with low contrast medium and that of conventional 120-kVp protocol with standard contrast medium. MATERIAL AND METHODS: After the propensity-matching analysis, 66 patients were enrolled including 33 patients with 120 kVp and 600 mgI/kg and 33 patients with 80 kVp and 300 mgI/kg (50% iodine reduction). The pre- and post-contrast phases were assessed in all patients. The Monte Carlo simulation tool was used to simulate the radiation dose. The computed tomography (CT) numbers for 10 organs and the organ doses were measured. The organ doses were normalized by the volume CT dose index, and the 120-kVp protocol was compared with the 80-kVp protocol. RESULTS: On contrast-enhanced CT, there were no significant differences in the mean CT numbers of the organs between 80-kVp and 120-kVp protocols except for the pancreas, kidneys, and small intestine. The normalized organ doses at 80 kVp were significantly lower than those of 120 kVp in all organs (e.g. liver, 1.6 vs. 1.9; pancreas, 1.5 vs. 1.8; spleen, 1.7 vs. 2.0) on contrast-enhanced CT. CONCLUSION: The low tube voltage with low-contrast-medium protocol significantly reduces organ doses at the same volume CT dose index setting compared with conventional 120-kVp protocol with standard contrast medium on contrast-enhanced CT.

Effect of image quality on myocardial extracellular volume quantification using cardiac computed tomography: a phantom study.

BACKGROUND: The image quality directly affects the accuracy of computed tomography (CT) extracellular volume (ECV) quantification. PURPOSE: To investigate the effects of image quality and acquisition protocol on the accuracy of ECV quantification. MATERIAL AND METHODS: One-volume scans were performed on a 320-row multidetector CT volume scanner using a multi-energy CT phantom. To simulate the blood pool and myocardium, solid rods representing blood and soft tissue were used in precontrast CT. Moreover, the solid rods including different iodine concentrations were used in postcontrast CT. The tube voltage was set at 120 kVp, and the tube current was changed from 750 mA (100% dose) to 190 mA (25% dose). All images underwent full- and half-scan reconstructions based on model-based iterative reconstruction. The ECV was calculated from the CT numbers between pre- and postcontrast. RESULTS: The mean ECV with full- and half-scan reconstructions at the central portion was 0.275 at 100% scan dose to 0.271 at 25% scan dose and 0.276 at 100% scan dose to 0.269 at 25% scan dose. Compared with that in the 100% scan dose, the variation in each ECV increased with decreasing radiation dose. The ECV at the center of the image along the z-axis had lower variation than that at outer portion of the images. On the reconstruction algorithm, there was no statistical difference in ECVs with full- and half-scan reconstructions. CONCLUSION: For stable ECV quantifications, excessive radiation dose reduction may be inappropriate, and it is better to consider the variations in ECV values depending on the slice location.

Deep Learning-based Reconstruction for Lower-Dose Pediatric CT: Technical Principles, Image Characteristics, and Clinical Implementations.

Optimizing the CT acquisition parameters to obtain diagnostic image quality at the lowest possible radiation dose is crucial in the radiosensitive pediatric population. The image quality of low-dose CT can be severely degraded by increased image noise with filtered back projection (FBP) reconstruction. Iterative reconstruction (IR) techniques partially resolve the trade-off relationship between noise and radiation dose but still suffer from degraded noise texture and low-contrast detectability at considerably low-dose settings. Furthermore, sophisticated model-based IR usually requires a long reconstruction time, which restricts its clinical usability. With recent advances in artificial intelligence technology, deep learning-based reconstruction (DLR) has been introduced to overcome the limitations of the FBP and IR approaches and is currently available clinically. DLR incorporates convolutional neural networks-which comprise multiple layers of mathematical equations-into the image reconstruction process to reduce image noise, improve spatial resolution, and preserve preferable noise texture in the CT images. For DLR development, numerous network parameters are iteratively optimized through an extensive learning process to discriminate true attenuation from noise by using low-dose training and high-dose teaching image data. After rigorous validations of network generalizability, the DLR engine can be used to generate high-quality images from low-dose projection data in a short reconstruction time in a clinical environment. Application of the DLR technique allows substantial dose reduction in pediatric CT performed for various clinical indications while preserving the diagnostic image quality. The authors present an overview of the basic concept, technical principles, and image characteristics of DLR and its clinical feasibility for low-dose pediatric CT. ©RSNA, 2021.

Conditional generative adversarial networks to generate pseudo low monoenergetic CT image from a single-tube voltage CT scanner.

PURPOSE: To generate pseudo low monoenergetic CT images of the abdomen from 120-kVp CT images with cGAN. MATERIALS AND METHODS: We retrospectively included 48 patients who underwent contrast-enhanced abdominal CT using dual-energy CT. We reconstructed paired data sets of 120 kVp CT images and virtual low monoenergetic (55-keV) CT images. cGAN was prepared to generate pseudo 55-keV CT images from 120-kVp CT images. The pseudo 55 keV CT images in epoch 10, 50, 100, and 500 were compared to the 55 keV images generated using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). RESULTS: The PSNRs were 28.0, 28.5, 28.6, and 28.8 at epochs 10, 50, 100, and 500, respectively. The SSIM was approximately constant from epochs 50 to 500. CONCLUSION: Pseudo low monoenergetic abdominal CT images were generated from 120-kVp CT images using cGAN, and the images had good quality similar to that of monochromatic images obtained with DECT software.

Assessment of cardiac implantable electric device lead perforation using a metal artifact reduction algorithm in cardiac computed tomography.

PURPOSE: CT is considered the non-invasive gold standard for evaluating cardiac implantable electronic devices (CIEDs) lead perforation, but metal artifacts caused by the lead tip affect the image quality and make a definitive diagnosis challenging. We compared the performances of the metal artifact reduction (MAR) algorithm and the conventional algorithm for identification of the right ventricular (RV) lead tip position in cardiac CT studies of patients with CIEDs. METHOD: Forty-seven consecutive patients (26 men; age 70.3 ±â€¯15.4 years) with CIEDs underwent cardiac CT. Using the conventional and MAR algorithm, two image reconstructions were performed for each scan. We calculated the artifact index (AI) to assess the quantitative capability of the MAR algorithm for artifact reduction and visually assessed the RV lead tip position on both images as follows: non-perforation, perforation, and equivocal. RESULTS: The mean AIs were significantly lower with the MAR algorithm than with the conventional algorithm (96.7 ±â€¯40.1 HU vs. 284.6 ±â€¯134.1 HU, P < 0.001). Thirteen (27.7 %) patients were diagnosed as equivocal using the conventional algorithm but were diagnosed with perforation (2 patients) and non-perforation (11 patients) using the MAR algorithm (equivocal rate: 27.7 % vs. 0%, P < 0.001). Using the MAR algorithm, all cases were diagnosed with perforation (6 patients, 12.8 %) or non-perforation (41 patients, 87.2 %). CONCLUSIONS: The MAR algorithm effectively reduced metal artifacts and allowed us to diagnose the presence or absence of perforation in all cases, whereas definitive diagnosis was difficult with the use of conventional algorithm in 27.7 % of cases.

Myocardial Extracellular Volume Quantification Using Cardiac Computed Tomography: A Comparison of the Dual-energy Iodine Method and the Standard Subtraction Method.

RATIONALE AND OBJECTIVES: To clarify the accuracy of two measurement methods for myocardial extracellular volume (ECV) quantification (ie, the standard subtraction method [ECVsub] and the dual-energy iodine method [ECViodine]) with the use of cardiac CT in comparison to cardiac magnetic resonance imaging (CMR) as a reference standard. MATERIALS AND METHODS: Equilibrium phase cardiac images of 21 patients were acquired with a dual-layer spectral detector CT and CMR, and the images were retrospectively analyzed. CT-ECV was calculated using ECVsub and ECViodine. The correlation between the ECV values measured by each method was assessed. Bland-Altman analysis was used to identify systematic errors and to determine the limits of agreement between the CT-ECV and CMR-ECV values. Root mean squared errors and residual values for the ECVsub and ECViodine were also assessed. RESULTS: The correlations between ECVsub and ECViodine for both septal and global measurement were r = 0.95 (p < 0.01) and 0.91 (p < 0.01), respectively, while those between the mean ECVsub and CMR-ECV were r = 0.90 (septal, p < 0.01) and 0.84 (global, p < 0.01), and those between ECViodine and CMR-ECV were r = 0.94 (septal, p < 0.01) and 0.95 (global, p < 0.01). Bland-Altman plots showed lower 95% limits of agreement between ECViodine and CMR-ECV compared with that between ECVsub and CMR-ECV in both septal and global measurement. The root mean squared error of ECVsub was higher than that of ECViodine. The mean residual value of ECVsub was significantly higher than that of ECViodine. CONCLUSION: ECViodine yielded more accurate myocardial ECV quantification than ECVsub, and provided a comparable ECV value to that obtained by CMR.

Decreasing the radiation dose for contrast-enhanced abdominal spectral CT with a half contrast dose: a matched-pair comparison with a 120 kVp protocol.

OBJECTIVES: To compare the estimated radiation dose of 50% reduced iodine contrast medium (halfCM) for virtual monochromatic images (VMIs) with that of standard CM (stdCM) with a 120 kVp imaging protocol for contrast-enhanced CT (CECT). METHODS: We enrolled 30 adults with renal dysfunction who underwent abdominal CT with halfCM for spectral CT. As controls, 30 matched patients without renal dysfunction using stdCM were also enrolled. CT images were reconstructed with the VMIs at 55 keV with halfCM and 120 kVp images with stdCM and halfCM. The Monte-Carlo simulation tool was used to simulate the radiation dose. The organ doses were normalized to CTDIvol for the liver, pancreas, spleen, and kidneys and measured between halfCM and stdCM protocols. RESULTS: For the arterial phase, the mean organ doses normalized to CTDIvol for stdCM and halfCM were 1.22 and 1.29 for the liver, 1.50 and 1.35 for the spleen, 1.75 and 1.51 for the pancreas, and 1.89 and 1.53 for the kidneys. As compared with non-enhanced CT, the average increase in the organ dose was significantly lower for halfCM (13.8% ± 14.3 and 26.7% ± 16.7) than for stdCM (31.0% ± 14.3 and 38.5% ± 14.8) during the hepatic arterial and portal venous phases (p < 0.01). CONCLUSION: As compared with stdCM with the 120 kVp imaging protocol, a 50% reduction in CM with VMIs with the 55 keV protocol allowed for a substantial reduction of the average organ dose of iodine CM while maintaining the iodine CT number for CECT. ADVANCES IN KNOWLEDGE: This study provides that the halfCM protocol for abdominal CT with a dual-layer-dual-energy CT can significantly reduce the increase in the average organ dose for non-enhanced CT as compared with the standard CM protocol.

Noninvasive flow evaluations of coronary artery bypass grafting using dynamic cardiac CT.

We aimed to investigate the correlation of graft flow measurements between transit-time flow measurement (TTFM) during coronary artery bypass grafting (CABG) surgery and dynamic cardiac CT after the surgery.Fourteen patients underwent CABG with TTFM and postoperative dynamic cardiac CT; 11 internal thoracic artery (ITA) grafts and 15 saphenous venous grafts (SVGs) were included for analysis. Pearsons correlation analysis was performed for the comparisons of the TTFM and cardiac dynamic CT flow parameters.TTFM was not significantly correlated with the CT flow of the ITA grafts (r = -0.23, P = .49), but it had a very strong correlation with the CT flow of the SVGs (r = 0.83, P < .01).In patients who underwent CABG surgery, dynamic cardiac CT enabled quantitative evaluation of SVG flow, with good correlation with TTFM.