Effect of contrast material injection duration on arterial enhancement at CT in patients with various cardiac indices: Analysis using computer simulation

Features and risk factors of early intraluminal thrombus formation within the frozen elephant trunk stent graft

OBJECTIVE

The frozen elephant trunk is a standard treatment method for aortic arch pathologies extending into the descending aorta. We previously described the phenomenon of early postoperative intraluminal thrombosis within the frozen elephant trunk. We investigated the features and predictors of intraluminal thrombosis.

METHODS

A total of 281 patients (66% male, mean age 60 ± 12 years) underwent frozen elephant trunk implantation between May 2010 and November 2019. In 268 patients (95%), early postoperative computed tomography angiography was available to assess intraluminal thrombosis.

RESULTS

The incidence of intraluminal thrombosis after frozen elephant trunk implantation was 8.2%. Intraluminal thrombosis was diagnosed early after the procedure (4.6 ± 2.9 days) and could be successfully treated with anticoagulation in 55% of patients. A total of 27% developed embolic complications. Mortality (27% vs 11%, P = .044) and morbidity were significantly higher in patients with intraluminal thrombosis. Our data showed a significant association of intraluminal thrombosis with prothrombotic medical conditions and anatomic slow flow features. The incidence of heparin-induced thrombopenia was higher in patients with intraluminal thrombosis (18% vs 3.3%, P = .011). Stent-graft diameter index, anticipated endoleak Ib, and degenerative aneurysm were significant independent predictors of intraluminal thrombosis. Therapeutic anticoagulation was a protective factor. Glomerular filtration rate, extracorporeal circulation time, postoperative rethoracotomy, and intraluminal thrombosis (odds ratio, 3.19, P = .047) were independent predictors of perioperative mortality.

CONCLUSIONS

Intraluminal thrombosis is an underrecognized complication after frozen elephant trunk implantation. In patients with risk factors of intraluminal thrombosis indication for frozen elephant trunk should be carefully evaluated and postoperative anticoagulation considered. Early thoracic endovascular aortic repair extension should be considered in patients with intraluminal thrombosis to prevent embolic complications. Stent-graft designs should be improved to prevent intraluminal thrombosis after frozen elephant trunk implantation.

Clinical audit of the image quality and customised contrast volume using P3T contrast injection software versus standard injection protocol in CT coronary angiography

INTRODUCTION

The implications of shorter scan time and lower tube voltage in the dual-source CT coronary angiography (CTCA) scan protocol necessitate the adaptation of contrast media (CM) injection parameters. This audit evaluates the coronary arteries' vascular attenuation and image quality by comparing the personalised patient protocol technology (P3T) contrast injection software with standard injection protocol. The secondary aim is to determine the relationship between CM volume and the patient's weight.

METHODOLOGY

A Siemens Somatom Definition Force CT Unit was used to scan 30 sets of patients between August 2020 and October 2020. Patients were selected retrospectively and separated into Standard Injection and P3T injection protocols. An experienced radiologist blinded to the groups reviewed the coronary vessels' contrast enhancement and image quality.

RESULTS

Overall, the mean HU of all the main coronary artery vessels obtained from P3T injection software reached above 350 HU and was diagnostically sufficient. The mean attenuation at the proximal region of RCA in the 80-99 kg weight category was significantly higher in the P3T injection software than the standard injection protocol (p < 0.001). The CM volume proposed by P3T injection software for 40-59 kg was approximately 57 ± 5 mls, while 75 ml was used for the standard injection protocol.

CONCLUSION

P3T injection software in CTCA resulted in an adequate diagnostic attenuation of coronary arteries (>350HU) in all weight groups, most effectively in the higher weight group, while maintaining diagnostic image quality. Further, the P3T software reduces CM volumes in lower-weight patients.

IMPLICATIONS

P3T software enables reducing CM volume in lower-weight patients while improving vascular enhancement in CTCA scans in higher-weight patients.

Using Patient-Specific Contrast Enhancement Optimizer Simulation Software During the Transcatheter Aortic Valve Implantation-Computed Tomography Angiography in Patients With Aortic Stenosis

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.

Estimating the differences between inter-operator contrast enhancement in cerebral CT angiography

BACKGROUND

Computed tomography (CT) angiography (CTA) is a non-invasive imaging method used to detect arteries and examine various brain diseases. When CTA is performed for follow-up or postoperative evaluation, reproducibility of vessel delineation is required. A reproducible and stable contrast enhancement can be achieved by manipulating the factors affecting it. Previous studies have investigated several factors that alter the contrast enhancement of arteries. However, no reports establishing the effect of different operators on contrast enhancement exist.

PURPOSE

To assess the differences between inter-operator arterial contrast enhancement in cerebral CTA using Bayesian statistical modeling.

METHODS

Image data were obtained using a multistage sampling method from the cerebral CTA scans of patients who underwent the process between January 2015 and December 2018. Several Bayesian statistical models were developed, and the objective variable was the mean CT number of the bilateral internal carotid arteries after contrast enhancement. The explanatory variables were sex, age, fractional dose (FD), and the operator's information. The posterior distributions of the parameters were computed via Bayesian inference using the Markov chain Monte Carlo (MCMC) method, with the Hamiltonian Monte Carlo method employed as the algorithm. The posterior predictive distributions were computed using the posterior distributions of the parameters. Finally, the differences between inter-operator arterial contrast enhancement on the CT number in cerebral CTA were estimated.

RESULTS

The posterior distributions showed that all parameters representing the difference between operators included zero at the 95% credible intervals (CIs). The maximum mean difference between inter-operator CT number in the posterior predictive distribution was only 12.59 Hounsfield units (HUs).

CONCLUSIONS

The Bayesian statistical modeling results suggest that contrast enhancement of cerebral CTA examination between operator-to-operator differences in postcontrast CT number was small compared to those within-operator differences resulting from factors not considered in the model.

Can Machine Learning Identify the Intravenous Contrast Dose and Injection Rate Needed for Optimal Enhancement on Dynamic Liver Computed Tomography?

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.

Contrast timing optimization of a two-volume dynamic CT pulmonary perfusion technique

The purpose of this study is to develop and validate an optimal timing protocol for a low-radiation-dose CT pulmonary perfusion technique using only two volume scans. A total of 24 swine (48.5 ± 14.3 kg) underwent contrast-enhanced dynamic CT. Multiple contrast injections were made under different pulmonary perfusion conditions, resulting in a total of 141 complete pulmonary arterial input functions (AIFs). Using all the AIF curves, an optimal contrast timing protocol was developed for a first-pass, two-volume dynamic CT perfusion technique (one at the base and the other at the peak of AIF curve). A subset of swine was used to validate the prospective two-volume pulmonary perfusion technique. The prospective two-volume perfusion measurements were quantitatively compared to the previously validated retrospective perfusion measurements with t-test, linear regression, and Bland–Altman analysis. As a result, the pulmonary artery time-to-peak (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${T}_{PA}$$\end{document}TPA) was related to one-half of the contrast injection duration (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{{T}_{Inj}}{2}$$\end{document}TInj2) by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${T}_{PA}=1.01\frac{{T}_{Inj}}{2}+1.01$$\end{document}TPA=1.01TInj2+1.01 (r = 0.95). The prospective two-volume perfusion measurements (P_PRO) were related to the retrospective measurements (P_RETRO) by P_PRO = 0.87P_RETRO + 0.56 (r = 0.88). The CT dose index and size-specific dose estimate of the two-volume CT technique were estimated to be 28.4 and 47.0 mGy, respectively. The optimal timing protocol can enable an accurate, low-radiation-dose two-volume dynamic CT perfusion technique.

Prediction of Aortic Contrast Enhancement on Dynamic Hepatic Computed Tomography-Performance Comparison of Machine Learning Methods and Simulation Software

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.

Using Machine Learning to Identify Intravenous Contrast Phases on Computed Tomography

PURPOSE

The purpose of the present work is to demonstrate the application of machine learning (ML) techniques to automatically identify the presence and physiologic phase of intravenous (IV) contrast in Computed Tomography (CT) scans of the Chest, Abdomen and Pelvis.

MATERIALS AND METHODS

Training, testing and validation data were acquired from a dataset of 82,690 chest and abdomen CT examinations performed at 17 different institutions. Free text in DICOM metadata was utilized as weak labels for semi-supervised classification training. Contrast phase identification was approached as a classification task, using a 12-layer CNN and ResNet18 with four contrast-phase output. The model was reformulated to fit a regression task aimed to predict actual seconds from time of IV contrast administration to series image acquisition. Finally, transfer learning was used to optimize the model to predict contrast presence on CT Chest.

RESULTS

By training based on labels inferred from noisy, free text DICOM information, contrast phase was predicted with 93.3% test accuracy (95% CI: 89.3%, 96.6%) . Regression analysis resulted in delineation of early vs late arterial phases and a nephrogenic phase in between the portal venous and delayed excretory phase. Transfer learning applied to Chest CT achieved an AUROC of 0.776 (95% CI: 0.721, 0.832) directly using the model trained for abdomen CT and 0.999 (95% CI: 0.998, 1.000) by fine-tuning.

CONCLUSIONS

The presence and phase of contrast on CT examinations of the Abdomen-pelvis accurately and automatically be ascertained by a machine learning algorithm. Transfer learning applied to CT Chest achieves high precision with as little as 100 labeled samples.

Usefulness of the patient-specific contrast enhancement optimizer simulation software during the whole-body computed tomography angiography

To evaluate whether the patient-specific contrast enhancement optimizer simulation software (p-COP) is useful for predicting contrast enhancement during whole-body computed tomography angiography (WBCTA). We randomly divided the patients into two groups using a random number table. We used the contrast material (CM) injection protocol selected by p-COP in group A (n = 52). The p-COP used an algorithm including data on the individual patient's cardiac output. Group B (n = 50) was assigned to the conventional CM injection protocol based on body weight. We compared the CT number in the abdominal aorta at the celiac artery level between the two groups and classified them as acceptable (> 280 HU) and unacceptable (< 279 HU) based on the optimal CT number for the WBCTA scans. To evaluate the difference in both injection protocols, we compared the visual inspection of the images of the artery of Adamkiewicz in both protocols. The CM dosage and injection rate in group A were significantly lower than those in group B (480.8 vs. 501.1 mg I/kg and 3.1 vs. 3.3 ml/s, p < 0.05). The CT number of the abdominal aorta at the celiac level was 382.4 ± 62.3 HU in group A and 363.8 ± 71.3 HU in group B (p = 0.23). CM dosage and injection rate were positively correlated to cardiac output for group A (r = 0.80, p < 0.05) and group B (r = 0.16, p < 0.05). The number of patients with an acceptable CT number was higher in group A [46/6 (86.7%)] than in group B [43/7 (71.4%)], but not significant (p = 0.71). The visualization rate for the Adamkiewicz artery was not significantly different between groups A and B (p = 0.89). The p-COP was useful for predicting contrast enhancement during WBCTA with a lower CM dosage and a lower contrast injection rate than that based on the body weight protocol. In patients with lower cardiac output a reduction in contrast injection rate and CM dosage did not lead to a reduced imaging quality, thus particularly in this group CM dosage can be reduced by p-COP.

[Comparison of Contrast Enhancement between Bolus-tracking and Test-bolus Methods on Coronary CT Angiography]

PURPOSE

To compare the contrast enhancement between bolus-tracking (BT) and test-bolus (TB) methods in coronary computed tomography angiography (CCTA).

METHOD

We enrolled 300 patients who underwent CCTA by BT (245 mg I/kg main bolus) or TB (77.4 mg I/kg test bolus with 245 mg I/kg main bolus) methods. In group BT (n=150), scanning was started automatically 5-second after contrast enhancement exceeded a predefined threshold of 150 Hounsfield units (HU). In group TB (n=150), TB peak attenuation plus 2-second was used as a delay. We recorded the CT number in the ascending aorta and determined whether the CT number was equivalent in two groups. For the equivalence test, we adopted 70 HU as the equivalence margin. The standard deviation (SD) in the CT number and the rate of patients with an acceptable CT number were compared. We also compared total iodine dose and total dose length product (DLP).

RESULT

The CT number of the ascending aorta was 437.6±68.9 HU in group BT and 438.9±69.7 HU in group TB; the 95% confidence interval for the difference between the groups was from -11.6 to 20.2 HU and within the range of the equivalence margins. The SD of the CT number and the rate of patients with acceptable CT number did not differ significantly between the two groups (p=0.857 and p=0.614, respectively). Total iodine dose in group TB was significantly higher than in group BT (p<0.001), and total DLP was not statistically significant (p=0.197).

CONCLUSION

The contrast enhancement between BT and TB methods in CCTA was equivalent, and the distribution was not significantly different between the two groups.

Individual Optimization of Contrast Media Injection Protocol at Hepatic Dynamic Computed Tomography Using Patient-Specific Contrast Enhancement Optimizer

OBJECTIVE

We developed a patient-specific contrast enhancement optimizer (p-COP) that can exploratorily calculate the contrast injection protocol required to obtain optimal enhancement at target organs using a computer simulator. Appropriate contrast media dose calculated by the p-COP may minimize interpatient enhancement variability. Our study sought to investigate the clinical utility of p-COP in hepatic dynamic computed tomography (CT).

METHODS

One hundred thirty patients (74 men, 56 women; median age, 65 years) undergoing hepatic dynamic CT were randomly assigned to 1 of 2 contrast media injection protocols using a random number table. Group A (n = 65) was injected with a p-COP-determined iodine dose (developed by Higaki and Awai, Hiroshima University, Japan). In group B (n = 65), a standard protocol was used. The variability of measured CT number (SD) between the 2 groups of aortic and hepatic enhancement was compared using the F test. In the equivalence test, the equivalence margins for aortic and hepatic enhancement were set at 50 and 10 Hounsfield units (HU), respectively. The rate of patients with an acceptable aortic enhancement (250-350 HU) for the diagnosis of hypervascular liver tumors was compared using the χ test.

RESULTS

The mean ± SD values of aortic and hepatic enhancement were 311.0 ± 39.9 versus 318.7 ± 56.5 and 59.0 ± 11.5 versus 58.6 ± 11.8 HU in groups A and B, respectively. Although the SD for aortic enhancement was significantly lower in group A (P = 0.006), the SD for hepatic enhancement was not significantly different (P = 0.871). The 95% confidence interval for the difference in aortic and hepatic enhancement between the 2 groups was within the range of the equivalence margins. The number of patients with acceptable aortic enhancement was significantly greater in group A than in group B (P < 0.01).

CONCLUSIONS

The p-COP software reduced interpatient variability in aortic enhancement and obtained acceptable aortic enhancement at a significantly higher rate compared with the standard injection protocol for hepatic dynamic CT.

Minimizing individual variations in arterial enhancement on coronary CT angiographs using "contrast enhancement optimizer": a prospective randomized single-center study

OBJECTIVES

To investigate the clinical utility of our newly developed contrast enhancement optimizer (CEO) software for coronary CT angiography (CCTA).

METHODS

We randomly assigned 295 patients (168 males, 127 females, median age 71 years) undergoing CCTA to one of two contrast media injection protocols. Group A (n = 150) was injected with a CEO-selected iodine dose based on patient factors. In group B (n = 145), we used our standard protocol (245 mg I/kg). We recorded the CT number in the ascending aorta and determined whether the CT number was equivalent in groups A and B. For the equivalence test, we adopted 75 Hounsfield units (HU) as the equivalence margin. The standard deviation in the CT number and the rate of patients with an acceptable CT number were compared using the F test and the chi-square test, respectively.

RESULTS

The iodine dose in group A was significantly smaller than that in group B (235.7 vs. 253.6 mg I/kg, p < 0.001). The CT number of the ascending aorta was 428.6 ± 55.5 HU in group A and 436.1 ± 68.7 HU in group B; the 95% confidence interval for the difference between the groups was -4.3 HU to 16.9 HU and within the range of the predetermined equivalence margins. In group A, the variance was significantly smaller than that in group B (p = 0.009). The number of patients with an acceptable CT number was significantly higher in group A than in group B (84.7% vs. 71.7%, p = 0.007).

CONCLUSIONS

The use of our CEO for CCTA studies yielded optimal aortic contrast enhancement in significantly more patients than the standard protocol based on the body weight.

KEY POINTS

• With our contrast enhancement optimizer (CEO) software, optimal and stable aortic enhancement can be obtained on coronary CT angiography scans irrespective of patient factors. • Management of contrast media becomes more appropriate by the CEO software. • The CEO software can control contrast enhancement at different tube voltage levels.