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

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.

The Feasibility of Using a Deep Learning-Based Model to Determine Cardiac Computed Tomographic Contrast Dose

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.

Influence of splenomegaly on aortic and liver parenchymal CT numbers during contrast-enhance CT in patients with cirrhosis

INTRODUCTION

To compare CT (computed tomography) values for enhancement of the abdominal aorta and liver parenchyma during dynamic contrast enhancement (CE) CT in cirrhotic patients with and without splenomegaly (SM).

METHODS

We considered 258 patients (83 males and 46 females for the splenomegaly group, and 83 males and 46 females for the control group) for this retrospective study. We measured CT values in the abdominal aorta and hepatic parenchyma during the hepatic arterial (HAP) and portal venous (PVP) phases. The aortic CE at HAP and the hepatic parenchymal CE at PVP were compared between the two groups. For success rate of scans, we also calculated the optimal CE rates (>280 HU in the abdominal aorta and >50 HU in the hepatic parenchyma) for each group.

RESULTS

In the SM group, the CE for abdominal aorta was decreased during the aortic phase for a dynamic CE-CT (p < 0.05). When evaluating the success rates, they were found to be 65.1 % and 58.9 % in the SM group and 81.4 % and 72.3 % in the non-SM group (p < 0.05).

CONCLUSION

The success rate of scans and CE for the abdominal aorta during the aortic phase exhibited a significant decrease during dynamic CE-CT scans on patients with SM. Patients with SM may have reduced diagnostic ability with typical contrast injection protocols.

IMPLICATIONS FOR PRACTICE

It may be necessary to change the injection rates and contrast medium volume during CE-CT depending on the presence or absence of SM.

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.

Proposal of a novel protocol using estimated cardiac index fractional dose to improve aortic contrast enhancement for early-phase dynamic CT

Maximum aortic computed tomography value (CTV) is difficult to control because of variations in cardiac function and patient physique. Therefore, to improve early-phase aortic enhancement on dynamic computed tomography (CT), we developed an estimated cardiac index fractional dose (eciFD). The eciFD protocol is a novel and original protocol for administering fractional dose (FD), representing the amount of iodine per unit body weight per injection duration, based on cardiac index (cardiac output divided by body surface area) as estimated by age in early-phase dynamic CT. At the time of administration, by selecting FD based on the patient's age and selecting a parameter that can achieve this FD, an aortic CTV ≥300 HU (ACTV≥300) can be obtained. This study aimed to investigate aortic enhancement on CT angiography using the eciFD protocol.This retrospective study investigated 291 consecutive patients who underwent dynamic CT from neck to abdomen after recommendation of the eciFD protocol at our institution. We compared early-phase aortic CTV distributions by scan delay between an eciFD group (eciFD applied, n = 135) and a non-eciFD group (eciFD not applied, n = 80). The effect of eciFD on early-phase ACTV≥300 was evaluated using logistic regression analysis adjusted for several potentially meaningful clinical confounders related to aortic CTV, namely male sex, heart rate ≤80 beats/min, estimated glomerular filtration rate ≤40 mL/min, use of eciFD, bolus tracking (BT), history of myocardial infarction, and order from the emergency center.The eciFD protocol was a significant factor for early-phase ACTV≥300 after adjusting for several confounders (odds ratio 3.03; 95% confidence intervals 1.59-5.77; P = .001). No interaction was seen between BT and eciFD protocol (p for interaction = 0.76). In terms of CTV distribution, with both a fixed scan delay time and BT, the eciFD group showed a high aortic CTV. The combination of eciFD protocol with BT provided a particularly high percentage of patients with ACTV≥300 (86.4%).The eciFD protocol was useful for improving aortic contrast enhancement. These findings need to be validated in a randomized controlled study.

Venous volume and compliance in the calf and forearm does not change after acute endurance exercise performed at continuous or interval workloads

Short-term endurance exercise training for 6-8 weeks leads to increases in venous volume and compliance in the limbs. However, it is not known whether these venous vascular properties are improved by acute endurance exercise. We examined the effects of acute endurance exercise involving continuous or interval workloads on venous volume and compliance in the exercising (calf) and non-exercising (forearm) limbs. Sixteen healthy young volunteers performed cycling exercise involving a continuous workload of 60% heart rate (HR) reserve or an interval workload of 40% HRreserve and 80% HRreserve, alternating every 2 min, for a total of 32 min each. Before and 60 min after acute cycling exercise, venous volume in the calf and forearm was measured by venous occlusion plethysmography during a cuff-deflation protocol with a venous collecting cuff wrapped to the thigh and upper arm and strain gauges attached to the calf and forearm. The cuff pressure was maintained at 60 mmHg for 8 min and was then deflated to 0 mmHg at a rate of 1 mmHg/s. Venous compliance was calculated as the numerical derivative of the cuff pressure-limb venous volume curve. In both the calf and forearm, the cuff pressure-venous volume curve and the cuff pressure-venous compliance relationship did not differ between before and 60 min after exercise involving continuous or interval workloads. These results suggest that acute exercise does not improve venous volume and compliance in both the exercising and non-exercising limbs.

[Can Echocardiography Adjust Corrected Contrast Injection Condition in Coronary CT Angiography?]

PURPOSE

The purposes were to search which factor of cardiac function in echocardiography correlates with the CT value, to correct contrast injection conditions with cardiac function in addition to suppress error in the contrast effect between patients, and to achieve the target CT value (350 HU) in coronary computed tomography angiography (CCTA).

METHODS

In 112 patients (conventional group), the contrast material was administered at a fractional dose (FD) of 21 mgI/kg/s. We measured the aortic CT value in the coronary origin part. In 112 patients (correction group), the contrast material was administered at corrected injection conditions with the most correlated functional factor and CT value.

RESULTS

The CT value of the conventional group was an average of 400.8±51.5 HU. The most correlated factor with the CT value was stroke volume [SV (r=-0.555)]. The CT value of the conventional group was an average of 360±46 HU. The case of the aim CT level was improved from 46% to 74%. In the correction group, the average value of FD was 18.5 mgI/kg/s. This enabled the reduction of the contrast material in 95% of patients.

CONCLUSION

The best correlation was obtained between the CT value of coronary arteries and SV. The contrast medium injection conditions were corrected for cardiac function in addition to body weight. As a result, we were able to control the CCTA target CT value of 300 to 400 HU at our hospital.

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.

Contrast medium protocols in routine chest CT: a survey study

BACKGROUND

Administration of contrast medium (CM) is an important image quality factor in computed tomography (CT) of the chest. There is no clear evidence or guidelines on CM strategies for chest CT, thus a consensus approach is needed.

PURPOSE

To survey the potential impact on differences in chest CT protocols, with emphasis on strategies for the administration of CM.

MATERIAL AND METHODS

A total of 170 respondents were included in this survey, which used two different approaches: (i) an online survey was sent to the members of the European Society of Thoracic Imaging (ESTI); and (ii) an email requesting a copy of their CT protocol was sent to all hospitals in Norway, and university hospitals in Sweden and Denmark. The survey focused on factors affecting CM protocols and enhancement in chest CT.

RESULTS

The overall response rate was 24% (n = 170): 76% of the respondents used a CM concentration of ≥350 mgI/mL; 52% of the respondents used a fixed CM volume strategy. Fixed strategies for injection rate and delay were also the most common approach, practiced by 73% and 57% of the respondents, respectively. The fixed delay was in the range of 20-90 s. Of the respondents, 56% used flexible tube potential strategies (kV).

CONCLUSION

The chest CT protocols and CM administration strategies employed by the respondents vary widely, affecting the image quality. The results of this study underline the need for further research and consensus guidelines related to chest CT.

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.

Workload of diagnostic radiologists in the foreseeable future based on recent scientific advances: growth expectations and role of artificial intelligence

Objective

To determine the anticipated contribution of recently published medical imaging literature, including artificial intelligence (AI), on the workload of diagnostic radiologists.

Methods

This study included a random sample of 440 medical imaging studies published in 2019. The direct contribution of each study to patient care and its effect on the workload of diagnostic radiologists (i.e., number of examinations performed per time unit) was assessed. Separate analyses were done for an academic tertiary care center and a non-academic general teaching hospital.

Results

In the academic tertiary care center setting, 65.0% (286/440) of studies could directly contribute to patient care, of which 48.3% (138/286) would increase workload, 46.2% (132/286) would not change workload, 4.5% (13/286) would decrease workload, and 1.0% (3/286) had an unclear effect on workload. In the non-academic general teaching hospital setting, 63.0% (277/240) of studies could directly contribute to patient care, of which 48.7% (135/277) would increase workload, 46.2% (128/277) would not change workload, 4.3% (12/277) would decrease workload, and 0.7% (2/277) had an unclear effect on workload. Studies with AI as primary research area were significantly associated with an increased workload (p < 0.001), with an odds ratio (OR) of 10.64 (95% confidence interval (CI) 3.25–34.80) in the academic tertiary care center setting and an OR of 10.45 (95% CI 3.19–34.21) in the non-academic general teaching hospital setting.

Conclusions

Recently published medical imaging studies often add value to radiological patient care. However, they likely increase the overall workload of diagnostic radiologists, and this particularly applies to AI studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-021-01031-4.

[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.

Impact of iodine concentration and iodine delivery rate on contrast enhancement in coronary CT angiography: a randomized multicenter trial (CT-CON)

OBJECTIVE

To compare the effect of contrast medium iodine concentration on contrast enhancement, heart rate, and injection pressure when injected at a constant iodine delivery rate in coronary CT angiography (CTA).

METHODS

One thousand twenty-four patients scheduled for coronary CTA were prospectively randomized to receive one of four contrast media: iopromide 300 mg I/ml, iohexol 350 mg I/ml, iopromide 370 mg I/ml, or iomeprol 400 mg I/ml. Contrast media were delivered at an equivalent iodine delivery rate of 2.0 g I/s. Intracoronary attenuation was measured and compared (per vessel and per segment). Heart rate before and after contrast media injection was documented. Injection pressure was recorded (n = 403) during contrast medium injection and compared between groups.

RESULTS

Intracoronary attenuation values were similar for the different contrast groups. The mean attenuation over all segments ranged between 384 HU for 350 mg I/ml and 395 HU for 400 mg I/ml (p = 0.079). Dose-length product (p = 0.8424), signal-to-noise ratio (all p > 0.05), time to peak (p = 0.324), and changes in heart rate (p = 0.974) were comparable between groups. The peak pressures differed: 197.4 psi for 300 mg I/ml (viscosity 4.6 mPa s), 229.8 psi for 350 mg I/ml (10.4 mPa s), 216.1 psi for 370 mg I/ml (9.5 mPa s), and 243.7 psi for 400 mg I/ml (12.6 mPa s) (p < 0.0001).

CONCLUSION

Intravascular attenuation and changes in heart rate are independent of iodine concentration when contrast media are injected at the same iodine delivery rate. Differences in injection pressures are associated with the viscosity of the contrast media.

KEY POINTS

• The contrast enhancement in coronary CT angiography is independent of the iodine concentration when contrast media are injected at body temperature (37 °C) with the same iodine delivery rate. • Iodine concentration does not influence the change in heart rate when contrast media are injected at identical iodine delivery rates. • For a fixed iodine delivery rate and contrast temperature, the viscosity of the contrast medium affects the injection pressure.