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Objectives: This study assessed whether patient-specific contrast enhancement optimizer simulation software (p-COP) can reduce the contrast material (CM) dose compared with the conventional body weight (BW)-tailored scan protocol during transcatheter aortic valve implantation-computed tomography angiography (TAVI-CTA) in patients with aortic stenosis., Methods: We used the CM injection protocol selected by the p-COP in group A (n = 30). p-COP uses an algorithm that concerns data on an individual patient's cardiac output. Group B (n = 30) was assigned to the conventional BW-tailored CM injection protocol group. We compared the CM dose, CM amount, injection rate, and computed tomography (CT) values in the abdominal aorta between the 2 groups and classified them as acceptable (>280 Hounsfield units (HU)) or unacceptable (<279 HU) based on the optimal CT value and visualization scores for TAVI-CTA. We used the Mann-Whitney U test to compare patient characteristics and assess the interpatient variability of subjects in both groups., Results: Group A received 56.2 mL CM and 2.6 mL/s of injection, whereas group B received 76.9 mL CM and 3.4 mL/s of injection ( P < 0.01). The CT value for the abdominal aorta at the celiac level was 287.0 HU in group A and 301.7HU in group B ( P = 0.46). The acceptable (>280 HU) and unacceptable (<280 HU) CT value rates were 22 and 8 patients in group A and 24 and 6 patients in group B, respectively ( P = 0.76). We observed no significant differences in the visualization scores between groups A and B (visualization score = 3, P = 0.71)., Conclusion: The utilization of p-COP may decrease the CM dosage and injection rate by approximately 30% in individuals with aortic stenosis compared with the body-weight-tailored scan protocol during TAVI-CTA., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

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., Competing Interests: Toshinori Hirai has received research support from Canon Medical Systems and is a guarantor of this study. Takeshi Nakaura has received research support from Nemoto Kyorindo Co, Ltd. Nemoto Kyorindo Co, Ltd had no control over the interpretation, writing, or publication of this work. There are no conflicts of interest to report for the other authors., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: This study aimed to predict contrast effects in cardiac computed tomography (CT) from CT localizer radiographs using a deep learning (DL) model and to compare the prediction performance of the DL model with that of conventional models based on patients' physical size., Methods: This retrospective study included 473 (256 men and 217 women) cardiac CT scans between May 2014 and August 2017. We developed and evaluated DL models that predict milligrams of iodine per enhancement of the aorta from CT localizer radiographs. To assess the model performance, we calculated and compared Pearson correlation coefficient ( r ) between the actual iodine dose that was necessary to obtain a contrast effect of 1 HU (iodine dose per contrast effect [IDCE]) and IDCE predicted by DL, body weight, lean body weight, and body surface area of patients., Results: The model was tested on 52 cases for the male group (mean [SD] age, 63.7 ± 11.4) and 44 cases for the female group (mean [SD] age, 69.8 ± 11.6). Correlation coefficients between the actual and predicted IDCE were 0.607 for the male group and 0.412 for the female group, which were higher than the correlation coefficients between the actual IDCE and body weight (0.539 for male, 0.290 for female), lean body weight (0.563 for male, 0.352 for female), and body surface area (0.587 for male, 0.349 for female)., Conclusions: The performance for predicting contrast effects by analyzing CT localizer radiographs with the DL model was at least comparable with conventional methods using the patient's body size, notwithstanding that no additional measurements other than CT localizer radiographs were required., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: This study aimed to investigate whether machine learning (ML) is useful for predicting the contrast material (CM) dose required to obtain a clinically optimal contrast enhancement in hepatic dynamic computed tomography (CT)., Methods: We trained and evaluated ensemble ML regressors to predict the CM doses needed for optimal enhancement in hepatic dynamic CT using 236 patients for a training data set and 94 patients for a test data set. After the ML training, we randomly divided using the ML-based (n = 100) and the body weight (BW)-based protocols (n = 100) by the prospective trial. The BW protocol was performed using routine protocol (600 mg/kg of iodine) by the prospective trial. The CT numbers of the abdominal aorta and hepatic parenchyma, CM dose, and injection rate were compared between each protocol using the paired t test. Equivalence tests were performed with equivalent margins of 100 and 20 Hounsfield units for the aorta and liver, respectively., Results: The CM dose and injection rate for the ML and BW protocols were 112.3 mL and 3.7 mL/s, and 118.0 mL and 3.9 mL/s ( P < 0.05). There were no significant differences in the CT numbers of the abdominal aorta and hepatic parenchyma between the 2 protocols ( P = 0.20 and 0.45). The 95% confidence interval for the difference in the CT number of the abdominal aorta and hepatic parenchyma between 2 protocols was within the range of predetermined equivalence margins., Conclusions: Machine learning is useful for predicting the CM dose and injection rate required to obtain the optimal clinical contrast enhancement for hepatic dynamic CT without reducing the CT number of the abdominal aorta and hepatic parenchyma., Competing Interests: The authors declare no conflict of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: The aim of this study was to compare prediction ability between ensemble machine learning (ML) methods and simulation software for aortic contrast enhancement on dynamic hepatic computed tomography., Methods: We divided 339 human hepatic dynamic computed tomography scans into 2 groups. One group consisted of 279 scans used to create cross-validation data sets, the other group of 60 scans were used as test data sets. To evaluate the effect of the patient characteristics on enhancement, we calculated changes in the contrast medium dose per enhancement of the abdominal aorta in the hepatic arterial phase. The parameters for ML were the patient sex, age, height, body weight, body mass index, and cardiac output. We trained 9 ML regressors by applying 5-fold cross-validation, integrated the predictions of all ML regressors for ensemble learning and the simulations, and used the training and test data to compare their Pearson correlation coefficients., Results: Comparison of different ML methods showed that the Pearson correlation coefficient for the real and predicted contrast medium dose per enhancement of the abdominal aorta was highest with ensemble ML (r = 0.786). It was higher than that obtained with the simulation software (r = 0.350). With ensemble ML, the Bland-Altman limit of agreement [mean difference, 5.26 Hounsfield units (HU); 95% limit of agreement, -112.88 to 123.40 HU] was narrower than that obtained with the simulation software (mean difference, 11.70 HU; 95% limit of agreement, -164.71 to 188.11 HU)., Conclusion: The performance for predicting contrast enhancement of the abdominal aorta in the hepatic arterial phase was higher with ensemble ML than with the simulation software., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Objective: The aim of this study was to compare the diagnostic performance of 100- and 120-kVp coronary computed tomography (CT) angiography (CCTA) scans for the identification of coronary plaque components., Methods: We included 116 patients with coronary plaques who underwent CCTA and integrated backscatter intravascular ultrasound studies. On 100-kVp scans, we observed 24 fibrous and 24 fatty/fibrofatty plaques; on 120-kVp scans, we noted 27 fibrous and 41 fatty/fibrofatty plaques. We compared the fibrous and the fatty/fibrofatty plaques, the CT number of the coronary lumen, and the radiation dose on scans obtained at 100 and 120 kVp. We also compared the area under the receiver operating characteristic (ROC) curve of the coronary plaques on 100- and 120-kVp scans with their ROC curves on integrated backscatter intravascular ultrasound images., Results: The mean CT numbers of fatty and fatty/fibrofatty plaques were 5.71 ± 36.5 and 76.6 ± 33.7 Hounsfield units (HU), respectively, on 100-kVp scans; on 120-kVp scans, they were 13.9 ± 29.4 and 54.5 ± 22.3 HU, respectively. The CT number of the coronary lumen was 323.1 ± 81.2 HU, and the radiation dose was 563.7 ± 81.2 mGy-cm on 100-kVp scans; these values were 279.3 ± 61.8 HU and 819.1 ± 115.1 mGy-cm on 120-kVp scans. The results of ROC curve analysis identified 30.5 HU as the optimal diagnostic cutoff value for 100-kVp scans (area under the curve = 0.93, 95% confidence interval = 0.87-0.99, sensitivity = 95.8%, specificity = 78.9%); for 120-kVp plaque images, the optimal cutoff was 37.4 HU (area under the curve = 0.87, 95% confidence interval = 0.79-0.96, sensitivity = 82.1%, specificity = 85.7%)., Conclusions: For the discrimination of coronary plaque components, the diagnostic performance of 100- and 120-kVp CCTA scans is comparable.

Objectives: The objective of this study is to evaluate the usefulness of iterative model reconstruction designed for brain computed tomography (CT) (IMR-Neuro) for the diagnosis of acute ischemic stroke., Methods: This retrospective study included 20 patients with acute middle cerebral artery infarction who have undergone brain CT and 20 nonstroke patients (control). We reconstructed axial images with filtered back projection (FBP) and IMR-Neuro (slice thickness, 1 and 5 mm). We compared the CT number of the infarcted area, the image noise, contrast, and the contrast to noise ratio of the infarcted and the noninfarcted areas between the different reconstruction methods. We compared the performance of 10 radiologists in the detection of parenchymal hypoattenuation between 2 techniques using the receiver operating characteristic (ROC) techniques with the jackknife method., Results: The image noise was significantly lower with IMR-Neuro [5 mm: 2.5 Hounsfield units (HU) ± 0.5, 1 mm: 3.9 HU ± 0.5] than with FBP (5 mm: 4.9 HU ± 0.5, 1 mm: 10.1 HU ± 1.4) (P < 0.01). The contrast to noise ratio was significantly greater with IMR-Neuro (5 mm: 2.6 ± 2.1, 1 mm: 1.6 ± 1.3) than with FBP (5 mm: 1.2 ± 1.0; 1 mm: 0.6 ± 0.5) (P < 0.01). The value of the average area under the receiver operating curve was significantly higher with IMR-Neuro than FBP (5 mm: 0.79 vs 0.74, P = 0.04; 1 mm: 0.76 vs 0.69, P = 0.04)., Conclusions: Compared with FBP, IMR-Neuro improves the image quality and the performance for the detection of parenchymal hypoattenuation with acute ischemic stroke.

Objective: The aim of this study was to evaluate the effect of cardiac output (CO) on aortic peak enhancement using protocols with different contrast material (CM) injection durations., Methods: We used a flow phantom that simulated the human circulatory system. Contrast material was injected at a rate of 4.0 mL/s for a period of 2.5, 5, 10, 15, or 20 seconds for a CO of 2.8, 4.2, and 5.6 L/min. Single-level serial computed tomography scans of the simulated aorta were acquired after the start of CM delivery, and aortic peak enhancement was recorded under the different injection protocols., Results: Under a long injection duration protocol (20 seconds), a decrease in CO increased aortic peak enhancement proportionally (CO of 2.8 L/min, 420 Hounsfield units [HU]; CO of 4.2 L/min, 365 HU; CO of 5.6 L/min, 291 HU). However, this effect was decreased under shorter injection duration protocols (5, 10, and 15 seconds); under the shortest (2.5-second) injection duration protocol, a decrease in CO resulted in a decrease in aortic peak enhancement (CO of 2.8 L/min, 36 HU; CO of 4.2 L/min, 51 HU; CO of 5.6 L/min, 55 HU)., Conclusions: The magnitude of the effect of CO on aortic peak enhancement depends on the CM injection duration.

Objective: We evaluated the effect of the age, sex, total body weight (TBW), height (HT) and cardiac output (CO) of patients on aortic and hepatic contrast enhancement during hepatic-arterial phase (HAP) and portal venous phase (PVP) computed tomography (CT) scanning., Methods: This prospective study received institutional review board approval; prior informed consent to participate was obtained from all 168 patients. All were examined using our routine protocol; the contrast material was 600 mg/kg iodine. Cardiac output was measured with a portable electrical velocimeter within 5 minutes of starting the CT scan. We calculated contrast enhancement (per gram of iodine: [INCREMENT]HU/gI) of the abdominal aorta during the HAP and of the liver parenchyma during the PVP. We performed univariate and multivariate linear regression analysis between all patient characteristics and the [INCREMENT]HU/gI of aortic- and liver parenchymal enhancement., Results: Univariate linear regression analysis demonstrated statistically significant correlations between the [INCREMENT]HU/gI and the age, sex, TBW, HT, and CO (all P < 0.001). However, multivariate linear regression analysis showed that only the TBW and CO were of independent predictive value (P < 0.001). Also, only the CO was independently and negatively related to aortic enhancement during HAP and to liver parenchymal enhancement when the contrast material injection protocol was adjusted for the TBW (P < 0.001)., Conclusion: By multivariate linear regression analysis only the TBW and CO were significantly correlated with aortic and liver parenchymal enhancement; the age, sex, and HT were not. The CO was the only independent factor affecting aortic and liver parenchymal enhancement at hepatic CT when the protocol was adjusted for the TBW.

Rationale and Objectives: We delivered the saline chaser via a spiral flow-generating tube or a conventional connecting tube and compared arterial enhancement at computed tomography angiography (CTA) of the lower extremities., Materials and Methods: We randomly assigned 100 patients whose ankle bronchial pressure index or clinical symptoms were suspect of peripheral arterial disease to a spiral flow-generating tube (protocol A) or a conventional connecting-tube protocol (protocol B) and performed CTA of the lower extremities. The test bolus was delivered under protocol A or B, and the CT numbers recorded for each protocol were compared. Two radiological technologists visually evaluated the descending genicular artery., Results: In the test injection, the median CT number for the popliteal artery was significantly higher with protocol A than B (204.5 HU vs. 170.5 HU, P = 0.03). For CTA of the lower extremities, the median CT number for the popliteal artery at the level of the patella was 436.1 HU (range, 259-608 HU) under protocol A; with protocol B, it was 382.9 HU (range, 244-564 HU) (P = 0.02). The visual score assigned in the descending genicular artery was statistically significantly higher under protocol A than B (P = 0.03)., Conclusions: Use of the spiral flow-generating tube increased the effect of the saline chaser and significantly improved arterial enhancement from the abdominal aorta to the arteries of the foot at CTA of the lower extremities.

Objective: The aim of this study was to evaluate the effect of a low tube voltage technique and hybrid iterative reconstruction (HIR) on image quality at 3-dimensional computed tomographic angiography (3D-CTA) of the liver., Methods: Before hepatic surgery, we randomly assigned 60 patients (17 women, 43 men; mean ± SD age, 68.9 ± 10.1 years) who had undergone 3D-CTA to 1 of 2 protocols; 30 patients underwent scanning under the conventional 120-kilovolt (peak) protocol with filtered back projection (P1); and 30 patients, under an 80-kilovolt (peak) protocol with HIR (P2). The estimated effective radiation dose, computed tomographic attenuation, image noise, contrast-to-noise ratio, and figure of merit were calculated, and the visual image quality of 3D-CTA was scored on a 4-point scale., Results: The mean effective radiation dose was significantly lower under P2 than P1 (4.8 ± 1.2 vs 7.2 ± 1.5 mSv, P < 0.01). P1 and P2 did not significantly differ with respect to the image noise (10.5 ± 2.3 vs 9.9 ± 1.6 Hounsfield units; P = 0.46). Computed tomographic attenuation, contrast-to-noise ratio, figure of merit, and the visual scores for image quality were higher under P2 than P1 (P < 0.01)., Conclusions: The use of low tube voltage and HIR can yield significantly improved image quality at 3D-CTA of the liver.

Objective: The objective of this study was to evaluate the effect of a low-tube-voltage technique and hybrid iterative reconstruction (HIR) on image quality at dynamic computed tomography (CT) of the pancreas., Methods: The study included 18 consecutive patients (10 women, 8 men; mean age, 68.5 ± 9.5 years) with locally advanced pancreatic cancer who received chemotherapy and had stable disease during the 100- and 120-kV CT studies. The 120-kV images were reconstructed using filtered back projection, and the 100-kV images were postprocessed using filtered back projection and HIR. Scans obtained during 3 pancreatic phases were subjected to quantitative and qualitative analysis., Results: The mean effective dose was significantly lower under the 100- than the 120-kV protocols (29.2 ± 3.6 vs 52.1 ± 5.1 mSv; P < 0.01). The mean contrast-to-noise ratio of the pancreatic cancer and the visual scores were significantly higher under 100 kV with HIR than those under the other 2 protocols (P < 0.01)., Conclusions: Use of low tube voltage and HIR can provide significantly improved image quality at pancreatic dynamic CT.

We compare the performance of low tube voltage with the hybrid iterative reconstruction (iDose) with standard and low tube voltage with the filtered backprojection (FBP) using phantoms at computed tomographic coronary angiography. In computed tomographic coronary angiography, application of the combined low tube voltage with iDose resulted in significant image quality improvements compared to the low tube voltage with FBP. Image quality was the same or better despite a reduction in the radiation dose by 76% compared with standard tube voltage with FBP.

We describe the effect of the off-center position, the bowtie filter type, and the direction of scan projection radiograph on the radiation dose and image noise of 64-detector computed tomographic scans using the automatic tube current modulation technique. On anteroposterior scan projection radiographs, the tube current ratio increased, with an increase in the off-center position of the phantom irrespective of the filter size. On lateral scan projection radiographs, it remained constant with each bowtie filter type regardless of the off-center phantom position.

Objective: To investigate the effect of the radiation dose (tube current second product) and the attenuation value of nodules with ground-glass opacity (GGO) on their detectability at multidetector computed tomography (MDCT)., Methods: We scanned a chest CT phantom that included simulated GGO nodules with an MDCT scanner. The attenuation value of the simulated lung parenchyma was -900 Hounsfield units (HU); it was -800 and -650 HU for the simulated GGO nodules. We used a tube current second product of 180 mA as the standard and 21, 45, 60, and 90 mAs as the low-dose and performed receiver operating characteristic analysis to compare the performance of 5 radiologists in detecting GGO nodules at each milliampere. To assess the detectability of GGO nodules on human lung images, the observers were presented with 38 GGO nodules from 15 patients. The 5 radiologists independently reviewed chest CT images at 21 and 45 mAs., Results: In the phantom study, the Az value for GGO nodules with a CT number of -800 HU was significantly lower at 21 than 180 effective mA (0.86 vs. 0.96; P < 0.01). There was no statistically significant difference in the Az value of GGO nodules with a CT number of -650 HU, irrespective of milliamperes used (P = 0.165). In the clinical study, 39.5% and 25.8% of GGO were missed at 21 and 45 mAs, respectively., Conclusions: At MDCT, GGO nodules with a CT number of -650 HU or less were difficult to detect at the lower milliampere settings (21 and 45 mAs).