Contrast Enhancement During Hepatic Computed Tomography

Deep-learning reconstruction with low-contrast media and low-kilovoltage peak for CT of the liver

AIM

To compare images using reduced CM, low-kVp scanning and DLR reconstruction with conventional images (no CM reduction, normal tube voltage, reconstructed with HBIR. To compare images using reduced contrast media (CM), low kilovoltage peak (kVp) scanning and deep-learning reconstruction (DLR) with conventional image quality (no CM reduction, normal tube voltage, reconstructed with hybrid-type iterative reconstruction method [HBIR protocol]).

MATERIALS AND METHODS

A retrospective analysis was performed on 70 patients with liver disease and three-phase dynamic imaging using computed tomography (CT) from April 2020 to March 2022 at Oita University Hospital. Of these cases, 39 were reconstructed using the DLR protocol at a tube voltage of 80 kVp and CM of 300 mg iodine/kg while 31 were imaged at a tube voltage of 120 kVp with CM of 600 mg iodine/kg and were reconstructed by the usual HBIR protocol. Images from the DLR and HBIR protocols were analysed and compared based on the contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), figure-of-merit (FOM), and visual assessment. The CT dose index (CTDI)vol and size-specific dose estimates (SSDE) were compared with respect to radiation dose.

RESULTS

The DLR protocol was superior, with significant differences in CNR, SNR, and FOM except hepatic parenchyma in the arterial phase. For visual assessment, the DLR protocol had better values for vascular visualisation for the portal vein, image noise, and contrast enhancement of the hepatic parenchyma. Regarding comparison of the radiation dose, the DLR protocol was superior for all values of CTDIvol and SSDE, with significant differences (p<0.01; max. 52%).

CONCLUSION

Protocols using DLR with reduced CM and low kVp have better image quality and lower radiation dose compared to protocols using conventional HBIR.

[Investigation of the Correlation between Patient Characteristics and Contrast Enhancement during Hepatic Dynamic CT Scan: Comparison by the Sex]

The purpose of this study was to investigate the correlation between patient characteristics and contrast enhancement during the hepatic arterial phase (HAP) and portal venous phase (PVP) CT scanning. All were examined using a hepatic dynamic CT protocol; the scanning parameters were tube voltage 120 kVp, tube current 50 to 600 mA (noise index 8.0 HU), 0.5-s rotation, 5-mm detector row width, 0.813 or 0.825 beam pitch, and the contrast material 600 mg/kg iodine. We calculated contrast enhancement (per gram of iodine: ΔHU/gI) of the abdominal aorta during the HAP and that of the hepatic parenchyma during the PVP. There was a significant difference in the contrast enhancement of the abdominal aorta during the HAP (8.6±2.7 ΔHU/gI) and (9.5±1.7 ΔHU/gI) and that of the hepatic parenchyma during the PVP (1.4±0.5 ΔHU/gI) and (2.9±0.5 ΔHU/gI) between male and female patients (p<0.05). A significant positive correlation was seen between the ΔHU/gI of aortic enhancement and age in male and female patients (r=-0.382 and 0.213) (p<0.05). A significant inverse correlation was observed between the ΔHU/gI of aortic enhancement and the height (HT; r=-0.466 and -0.251), total body weight (TBW; r=-0.609 and -0.535), body mass index (BMI; r=-0.505 and -0.465), lean body weight (LBW; r=-0.642 and -0.576), and body surface area (BSA; r=-0.644 and -0.557) (p<0.05 for all) in male and female patients. A significant positive correlation was seen between the ΔHU/gI of hepatic parenchymal enhancement and the patient age in male and female patients (r=0.258 and 0.150) (p<0.05). A significant inverse correlation was observed between the ΔHU/gI of hepatic parenchymal enhancement and the HT (r=-0.487 and -0.321), TBW (r=-0.580 and -0.525), BMI (r=-0.473 and -0.413), LBW (r=-0.615 and -0.576) (p<0.05 for all), and BSA (r=-0.617 and -0.558) in male and female patients. The BSA was significantly correlated with the ΔHU/gI of aortic and hepatic parenchymal enhancement of the hepatic dynamic CT in male patients. However, LBW was significantly correlated with the ΔHU/gI of aortic and hepatic parenchymal enhancement of the hepatic dynamic CT in female patients. Since the patient factors that affect the contrast enhancement of the abdominal aorta and hepatic parenchyma may differ from facility to facility, we should therefore consider reassessing at each facility.

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.

Strategies for calculating contrast media dose for chest CT

BACKGROUND

Total body weight (TBW) is a frequently used contrast media (CM) strategy for dose calculation in enhanced CT, yet it is suboptimal as it lacks consideration of patient characteristics, such as body fat percentage (BFP) and muscle mass. Alternative CM dosage strategies are suggested by the literature. Our objectives were to analyze the CM dose impact when adjusting to body composition using methods of obtaining lean body mass (LBM) and body surface area (BSA) along with its correlation with demographic factors in contrast enhanced chest CT examinations.

METHODS

Eighty-nine adult patients referred for CM thoracic CT were retrospectively included, categorized as either normal, muscular, or overweight. Patient body composition data was used to calculate the CM dose according to LBM or BSA. LBM was calculated with the James method, Boer method, and bioelectric impedance (BIA). BSA was calculated using the Mostellar formula. We then correlated the corresponding CM doses with demographic factors.

RESULTS

BIA demonstrated the highest and lowest calculated CM dose in muscular and overweight groups respectively, compared to other strategies. For the normal group, the lowest calculated CM dose was achieved using TBW. The calculated CM dose was more closely correlated with BFP using the BIA method.

CONCLUSIONS

The BIA method is more adaptive to variations in patient body habitus especially in muscular and overweight patients and is most closely correlated to patient demographics. This study could support utilizing the BIA method for calculating LBM for a body-tailored CM dose protocol for enhanced chest CT examinations.

RELEVANCE STATEMENT

The BIA-based method is adaptive to variations in body habitus especially in muscular and overweight patients and is closely correlated to patient demographics for contrast-enhanced chest CT.

KEY POINTS

• Calculations based on BIA showed the largest variation in CM dose. • Lean body weight using BIA demonstrated the strongest correlation to patient demographics. • Lean body weight BIA protocol may be considered for CM dosing in chest CT.

Effect of patient characteristics on vessel enhancement on arterio-venous fistula CT angiography in a retrospective cohort study

To evaluate the effects of various patient characteristics on vessel enhancement on arterio-venous fistula (AVF) computed tomography (CT) angiography (AVF-CT angiography). A total of 127 patients with suspected or confirmed shunt stenosis and internal AVF complications were considered for inclusion in a retrospective cohort study. The tube voltage was 120 kVp, and the tube current was changed from 300 to 770 mA to maintain the image quality (noise index: 14) using automatic tube current modulation. To evaluate the effects of age, sex, body size, and scan delay on the CT number of the brachial artery or vein, we used correlation coefficients and multivariate regression analyses. There was a significant positive correlation between the CT number of the brachial artery or vein and age (R = 0.21 or 0.23, P < .01). The correlations were inverse with the height (r = -0.45 or -0.42), total body weight (r = -0.52 or -0.50), body mass index (r = -0.21 or -0.23), body surface area (body surface area [BSA]; r = -0.56 or -0.54), and lean body weight (r = -0.55 or -0.53) in linear regression analysis (P < .01 for all). There was a significant correlation between the CT number of the brachial artery or vein and scan delay (R = 0.19 or 01.9, P < .01). Only the BSA had significant effects on the CT number in multivariate regression analysis (P < .01). The BSA was significantly correlated with the CT number of the brachial artery or vein on AVF-CT angiography.

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.

Hepatic enhancement at computed tomography: is there a dependence on body weight past institutional contrast dosing limits?

BACKGROUND

Although described in product monographs, the maximum contrast media (CM) dose at computed tomography (CT) varies among institutions.

PURPOSE

To investigate whether an upper limit of 40 g of iodine in women and 50 g in men is sufficient or if there is a body weight (BW) dependence of mean hepatic enhancement (MHE) beyond those thresholds.

MATERIAL AND METHODS

At our institution, CM injection duration is fixed to 30 s and dosed 600 mg iodine/kg up to 40 g in women and 50 g in men. Pre- and post-contrast hepatic attenuation values (HU) were retrospectively obtained in 200 women and 200 men with glomerular filtration rate >45 mL/min undergoing 18-flurodeoxyglucose PET-CT (18F-FDG PET-CT) of which half weighed below and half above those dose thresholds using iodixanol 320 mg iodine/mL or iomeprol 400 mg iodine/mL. The correlation between BW and MHE was assessed by simple linear regression.

RESULTS

Weight range was 41-120 kg in women and 47-137 kg in men. There was no significant relationship between MHE and BW in women receiving <40 g (r = -0.05, P = 0.63) or in men receiving <50 g (r = 0.18, P = 0.07). Above those thresholds there was an inverse relationship (r = -0.64, P<0.001 in women and r = -0.30, P<0.002 in men). There was no apparent upper limit where the dependence of hepatic MHE on BW decreased. Hepatosteatosis limited MHE.

CONCLUSION

Adjusting CM to BW diminishes the dependence of MHE on BW. There was no apparent upper limit for the relationship between BW and MHE in heavier patients at CM-enhanced CT.

Effect of Patient Factors on Portal Vein and Hepatic Contrast Enhancement at Computed Tomography Scan With Protocol Combining Fixed Injection Duration and Patients’ Body Weight Tailored Dose of Contrast Material

Introduction

Fixed injection duration with patients’ body weight tailored dose of contrast material was recommended as the practical scan protocol in multiphasic contrast-enhanced abdominal computed tomography (CT). This study evaluated the effect of the demographic variables on portal vein and hepatic contrast enhancement in hepatic arterial phase (HAP), aiming to reduce the patient-to-patient variability and optimize the HAP images.

Methods

This retrospective analysis included 87 patients who underwent abdominal enhancement multiphase CT from April to June 2022. All the patients were examined using protocol combining fixed injection duration and patients’ body weight tailored dose of contrast material. Univariate and multivariate linear regression analyses were performed between all patient characteristics and the contrast-enhanced CT number of portal vein and hepatic parenchyma during HAP.

Results

Univariate linear regression analysis demonstrated statistically significant correlations between the CT number of hepatic parenchyma, and the body mass index (BMI), body surface area (BSA), and total body weight (TBW) (all P < 0.001) during HAP. However, multivariate linear regression analysis showed that the BMI or BMI and age were of independent predictive values (P < 0.001). Also, only the age was independently and negatively related to the CT number of portal vein enhancement during HAP (r = 0.240, P < 0.05) according to univariate linear regression analysis.

Conclusions

Univariate linear regression analysis revealed a significant inverse correlation between portal vein CT value and age. By multivariate linear regression analysis, only the BMI and age were significantly correlated with liver parenchymal enhancement, while gender, TBW, BSA, and HT were not.

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.

Evaluation of celiac disease with uniphasic and multiphasic dynamic MDCT imaging

PURPOSE

An analysis of dynamic contrast MRI has been shown to provide valuable information about disease activity in Crohn's disease and Celiac disease (CD). However, there are no reports of dynamic multi-detector computer tomography use in patients with CD. The aim of this study is to determine and compare the perfusion dynamics of the patients treated with control subjects and the perfusion dynamics in patients with untreated CD, using dynamic contrast in MDCT and compare studying contrast dynamics in Marsh types as well.

METHODS

In this retrospective study, uniphasic and multiphasic MDCT, untreated, treated, incompatible CD patients and healthy control group duodenum wall thickness and HU values were compared in terms of patient groups and modified Marsh types.

RESULT

In dynamic CT, the highest contrast curve was observed in the untreated group and Marsh type 1. While the contrast curve of the untreated and non-compliant patients increased rapidly and showed wash out, the type 4 contrast curve was observed, whereas the treated and control group slowly increased type 5 contrast curve. In the contrast-enhanced CT in the venous phase, in the ROC analysis between Marsh 1-2 and Marsh 3a-c, the sensitivity was 97% and the specificity was 87% when the cut off was taken as 4.45 mm for wall thickness (p: 0.005).

CONCLUSION

Contrast-enhanced single-phase and dynamic MDCT imaging in CD patients may be useful in evaluating the inflammatory and pathological process in the small intestine.

Prospective multicenter study on personalized and optimized MDCT contrast protocols: results on liver enhancement

OBJECTIVE

To determine a personalized and optimized contrast injection protocol for a uniform and optimal diagnostic level of liver parenchymal enhancement, in a large patient population enrolled in a multicenter study.

METHODS

Six hundred ninety-two patients who underwent a standardized multi-phase liver CT examination were prospectively assigned to one contrast media (CM) protocol group: G1 (100 mL fixed volume, 37 gI); G2 (600 mgI/kg of total body weight (TBW)); G3 (750 mgI/kg of fat-free mass (FFM)), and G4 (600 mgI/kg of FFM). Change in liver parenchyma CT number between unenhanced and contrast-enhanced images was measured by two radiologists, on 3-mm pre-contrast and portal phase axial reconstructions. The enhancement histograms were compared across CM protocols, specifically according to a target diagnostic value of 50 HU. The total amount of iodine dose was also compared among protocols by median and interquartile range (IQR). The Kruskal-Wallis and Mann-Whitney U tests were used to assess significant differences (p < 0.005), as appropriate.

RESULTS

A significant difference (p < 0.001) was found across the groups with liver enhancement decreasing from median over-enhanced values of 77.0 (G1), 71.3 (G2), and 65.1 (G3) to a target enhancement of 53.2 HU for G4. Enhancement IQR was progressively reduced from 26.5 HU (G1), 26.0 HU (G2), and 17.8 HU (G3) to 14.5 HU (G4). G4 showed a median iodine dose of 26.0 gI, significantly lower (p < 0.001) than G3 (33.9 gI), G2 (38.8 gI), and G1 (37 gI).

CONCLUSIONS

The 600 mgI/kg FFM-based protocol enabled a diagnostically optimized liver enhancement and improved patient-to-patient enhancement uniformity, while significantly reducing iodine load.

KEY POINTS

• Consistent and clinically adequate liver enhancement is observed with personalized and optimized contrast injection protocol. • Fat-free mass is an appropriate body size parameter for correlation with liver parenchymal enhancement. • Diagnostic oncology follow-up liver CT examinations may be obtained using 600 mgI/kg of FFM.

Optimization of contrast medium volume for abdominal CT in oncologic patients: prospective comparison between fixed and lean body weight-adapted dosing protocols

Background

Patient body size represents the main determinant of parenchymal enhancement and by adjusting the contrast media (CM) dose to patient weight may be a more appropriate approach to avoid a patient over dosage of CM. To compare the performance of fixed-dose and lean body weight (LBW)-adapted contrast media dosing protocols, in terms of image quality and parenchymal enhancement.

Results

One-hundred cancer patients undergoing multiphasic abdominal CT were prospectively enrolled in this multicentric study and randomly divided in two groups: patients in fixed-dose group (n = 50) received 120 mL of CM while in LBW group (n = 50) the amount of CM was computed according to the patient’s LBW. LBW protocol group received a significantly lower amount of CM (103.47 ± 17.65 mL vs. 120.00 ± 0.00 mL, p < 0.001). Arterial kidney signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) and pancreatic CNR were significantly higher in LBW group (all p ≤ 0.004). LBW group provided significantly higher arterial liver, kidney, and pancreatic contrast enhancement index (CEI) and portal venous phase kidney CEI (all p ≤ 0.002). Significantly lower portal vein SNR and CNR were observed in LBW-Group (all p ≤ 0.020).

Conclusions

LBW-adapted CM administration for abdominal CT reduces the volume of injected CM and improves both image quality and parenchymal enhancement.

Optimization of computed tomography contrast studies with a new, simple dosing regimen incorporating body size: examination of contrast effects in the thoracoabdominal aorta

The dosage of contrast agents for computed tomography contrast studies is calculated based on the parameter of actual body weight (ABW) to ensure reproducibility. The use of lean body weight (LBW) and adjustment for physique (lean or obese) improves accuracy. However, this method is complex, because LBW is not a general body parameter and requires a special device to measure. To solve this problem, contrast body weight (CBW), has been proposed as a new and simple parameter that considers physique. CBW is calculated by determining the blood volume ratio based on body height, ABW, and sex and can potentially correct for body size. It can be calculated by entering a formula in a Microsoft Excel sheet. Since CBW can be easily obtained using this general tool, we decided to compare the two body parameters of ABW and CBW. We compared ABW and CBW and demonstrated a higher correlation between CBW-based dosing and the amount of iodine used per body weight than with ABW-based dosing. CBW-based dosing allows correction for body size. This indicates that contrast enhancement over a spectrum of lean or obese examinees can be linearly evaluated. To date, this method has shown good results.

Lean body weight versus total body weight to calculate the iodinated contrast media volume in abdominal CT: a randomised controlled trial

Objectives

Iodinated contrast media (ICM) could be more appropriately dosed on patient lean body weight (LBW) than on total body weight (TBW).

Methods

After Ethics Committee approval, trial registration NCT03384979, patients aged ≥ 18 years scheduled for multiphasic abdominal CT were randomised for ICM dose to LBW group (0.63 gI/kg of LBW) or TBW group (0.44 gI/kg of TBW). Abdominal 64-row CT was performed using 120 kVp, 100–200 mAs, rotation time 0.5 s, pitch 1, Iopamidol (370 mgI/mL), and flow rate 3 mL/s. Levene, Mann–Whitney U, and χ² tests were used. The primary endpoint was liver contrast enhancement (LCE).

Results

Of 335 enrolled patients, 17 were screening failures; 44 dropped out after randomisation; 274 patients were analysed (133 LBW group, 141 TBW group). The median age of LBW group (66 years) was slightly lower than that of TBW group (70 years). Although the median ICM-injected volume was comparable between groups, its variability was larger in the former (interquartile range 27 mL versus 21 mL, p = 0.01). The same was for unenhanced liver density (IQR 10 versus 7 HU) (p = 0.02). Median LCE was 40 (35–46) HU in the LBW group and 40 (35–44) HU in the TBW group, without significant difference for median (p = 0.41) and variability (p = 0.23). Suboptimal LCE (< 40 HU) was found in 64/133 (48%) patients in the LBW group and 69/141 (49%) in the TBW group, but no examination needed repeating.

Conclusions

The calculation of the ICM volume to be administered for abdominal CT based on the LBW does not imply a more consistent LCE.

Integration of quantitative proteomics and metabolomics reveals tissue hypoxia mechanisms in an ischemic-hypoxic rat model

Tissues hypoxia caused by hemorrhage is a common complication in many clinical diseases. However, its pathological mechanism remains largely unknown. To partly address this issue, an ischemic-hypoxic rat model was established and the plasma proteomic and metabolic profiles were quantified and analyzed using TMT-based quantitative proteomics and metabolomics. The analysis revealed a total of 177 differentially expressed proteins and 32 metabolites that were uniquely altered in the hypoxic rat plasma, compared to the control. Bioinformatics analysis showed that these altered proteins and metabolites were involved in a wide range of biological processes. Twelve of the 177 differentially expressed proteins were involved in PI3K-Akt signaling, a pathway that has been reported to be strongly associated with tissue hypoxia. Other signaling pathways such as complement and coagulation cascades, GnRH signaling, relaxin signaling, protein processing in endoplasmic reticulum, as well as AGE-RAGE signaling were markedly altered in the ischemic-hypoxic response, implying their potential roles in tissue hypoxia. A joint analysis of proteome and metabolome showed that the significantly altered metabolites such as guanine, tryptamine, dopamine, hexadecenoic, l-methionine, and fumarate may have participated in the pathogenesis of tissue hypoxia. Further, we found that changes in the levels of metabolites matched the changes in protein abundance within the same pathway. Overall, this study presents an overview of the molecular networks in ischemic-hypoxic pathology and offers biochemical basis for further study on the mechanism of tissue hypoxia. SIGNIFICANCE: We employed an integrated metabonomic-proteomic method to systematically analyze the profiles of metabolites and proteins in an ischemic-hypoxic rat model. Bioinformatics and enrichment analysis showed that the differentially expressed proteins were mainly involved in complement and coagulation cascades, PI3K-Akt signaling, GnRH signaling, relaxin signaling, protein processing in endoplasmic reticulum, and AGE-RAGE signaling. Moreover, a panel of 12 candidate proteins involved in PI3K-Akt signaling (i.e., Vtn, Hsp90b1, Ywhae, Tnc, Ywhaz, Thbs4, Lamc1, Col1a1, Il2rg, Egfr, Newgene 621,351, and Tfrc) may serve as the potential biomarkers to predict tissue hypoxia.

Dosing Iodinated Contrast Media According to Lean Versus Total Body Weight at Abdominal CT: A Stratified Randomized Controlled Trial

RATIONALE AND OBJECTIVES

To compare the magnitude and interpatient variability in normalized mean hepatic enhancement (MHE) indices when dosing contrast media (CM) according to total body weight (TBW) and lean body weight (LBW).

MATERIALS AND METHODS

This ethics-approved stratified randomized controlled study allocated 280 outpatients for abdominal Computed Tomography (CT) between February-November 2018 to TBW- or LBW-dosing using computer-generated tables. CTs were acquired in portal venous phase after fixed 35-second injection of Iohexol 350. Patients with missing precontrast image, incorrect dose, or chronic kidney, liver or heart disease were excluded. The number of included patients and CM doses were: TBW arm, 51 women and 60 men, 1.22 mL/kg; LBW arm, 59 women, 1.66 mL/kg LBW, and 59 men, 1.52 mL/kg LBW. Liver attenuations were obtained from regions of interest. Values and standard deviations in MHE indices normalized to iodine dose (MHE/I) and iodine dose per kg TBW (aMHE = MHE/[I/TBW]) were compared (unpaired t tests and F-tests).

RESULTS

Cohorts were similar in age, sex, TBW, and LBW. TBW groups received more CM than LBW groups: men, 106.5 ± 20 versus 98.4 ± 11 mL, p = 0.007; women, 93.7 ± 20 versus 77.5 ± 11 mL, p < 0.0001. TBW and LBW groups showed no significant difference in MHE/I (women, 1.75 ± 0.5 versus 1.86 ± 0.6 HU/g, p = 0.31; men, 1.53 ± 0.4 versus 1.52 ± 0.4 HU/g, p = 0.90) or aMHE (women, 0.03 ± 0.01 versus 0.03 ± 0.01 HU/g/kg, p = 0.25; men, 0.02 ± 0.01 versus 0.02 ± 0.01 HU/g/kg, p = 0.52). Variances in MHE/I and aMHE were not significantly different for all groups (p > 0.05).

CONCLUSION

TBW- and LBW-based CM dosing yield a similar magnitude and interpatient variability in normalized MHE indices at routine abdominal CT.

Determination of contrast medium dose for hepatic CT enhancement with improved body size dependency using a non-linear analysis based on pharmacokinetic principles

AIM

To propose a pharmacokinetic non-linear analysis method to determine contrast medium (CM) dose for computed tomography (CT) hepatic enhancement to improve body size dependency and validate the proposed CM dose determination method through a clinical study.

MATERIALS AND METHODS

Enhancement data of 105 patients who underwent hepatic dynamic CT with a fixed CM dose were analysed. From the analysis results, CM doses as a function of each of four body size indices (body weight [BW], lean body weight [LBW], blood volume [BV], and body surface area [BSA]) for achieving improved body size dependency were determined (proposed method), and the body size dependencies were simulated using the enhancement data from 105 patients. The proposed method was validated with a two-arm clinical study on BW. Body size dependency was evaluated using p-value of correlation coefficient between Body size indices and enhancements (p<0.05: significant dependency) and mean absolute error (MAE).

RESULTS

The simulation showed that significant body size dependencies not considered by the conventional method can be improved by the proposed method. MAEs of BW, LBW, and BV were also significantly reduced (p<0.05). The clinical study with BW demonstrated a similar improvement to that in the simulation result. MAE was also significantly reduced (p<0.001).

CONCLUSION

The proposed method demonstrated more improved BW, LBW, and BV dependence compared to the conventional method. Through the two-arm clinical study, the proposed method using BW only, without height information, is a suitable index for improving body size dependency.

Abdominal CT: a radiologist-driven adjustment of the dose of iodinated contrast agent approaches a calculation per lean body weight

Background

The contrast agent (CA) dose for abdominal computed tomography (CT) is typically based on patient total body weight (TBW), ignoring adipose tissue distribution. We report on our experience of dosing according to the lean body weight (LBW).

Methods

After Ethics Committee approval, we retrospectively screened 219 consecutive patients, 18 being excluded for not matching the inclusion criteria. Thus, 201 were analysed (106 males), all undergoing a contrast-enhanced abdominal CT with iopamidol (370 mgI/mL) or iomeprol (400 mgI/mL). LBW was estimated using validated formulas. Liver contrast-enhancement (CE_L) was measured. Data were reported as mean ± standard deviation. Pearson correlation coefficient, ANOVA, and the Levene test were used.

Results

Mean age was 66 ± 13 years, TBW 72 ± 15 kg, LBW 53 ± 11 kg, and LBW/TBW ratio 74 ± 8%; body mass index was 26 ± 5 kg/m², with 9 underweight patients (4%), 82 normal weight (41%), 76 overweight (38%), and 34 obese (17%). The administered CA dose was 0.46 ± 0.06 gI/kg of TBW, corresponding to 0.63 ± 0.09 gI/kg of LBW. A negative correlation was found between TBW and CA dose (r = -0.683, p < 0.001). CE_L (Hounsfield units) was 51 ± 18 in underweight patients, 44 ± 8 in normal weight, 42 ± 9 in overweight, and 40 ± 6 in obese, with a significant difference for both mean (p = 0.004) and variance (p < 0.001). A low but significant positive correlation was found between CE_L and CA dose in gI per TBW (r = 0.371, p < 0.001) or per LBW (r = 0.333, p < 0.001).

Conclusions

The injected CA dose was highly variable, with obese patients receiving a lower dose than underweight patients, as a radiologist-driven ‘compensation effect’. Diagnostic abdomen CT examinations may be obtained using 0.63 gI/kg of LBW.

Lean Body Weight-Tailored Iodinated Contrast Injection in Obese Patient: Boer versus James Formula

Purpose

To prospectively compare the performance of James and Boer formula in contrast media (CM) administration, in terms of image quality and parenchymal enhancement in obese patients undergoing CT of the abdomen.

Materials and Methods

Fifty-five patients with a body mass index (BMI) greater than 35 kg/m² were prospectively included in the study. All patients underwent 64-row CT examination and were randomly divided in two groups: 26 patients in Group A and 29 patients in Group B. The amount of injected CM was computed according to the patient's lean body weight (LBW), estimated using either Boer formula (Group A) or James formula (Group B). Patient's characteristics, CM volume, contrast-to-noise ratio (CNR) of liver, aorta and portal vein, and liver contrast enhancement index (CEI) were compared between the two groups. For subjective image analysis readers were asked to rate the enhancement of liver, kidneys, and pancreas based on a 5-point Likert scale.

Results

Liver CNR, aortic CNR, and portal vein CNR showed no significant difference between Group A and Group B (all P ≥ 0.177). Group A provided significantly higher CEI compared to Group B (P = 0.007). Group A and Group B returned comparable overall subjective enhancement values (3.54 and vs 3.20, all P ≥ 0.199).

Conclusions

Boer formula should be the method of choice for LBW estimation in obese patients, leading to an accurate CM amount calculation and an optimal liver contrast enhancement in CT.

Relationships between patient characteristics and contrast agent dose for successful computed tomography venography with a body-weight-tailored contrast protocol

The aim of this study was to evaluate the effect of patient characteristics on the contrast agent dosage that is required to reach effective enhancement of the inferior vena cava (IVC) on computed tomography venographs (CTV).This retrospective study included 50 patients who underwent CTV at 80 kVp. The contrast injection protocol (iodine 600 mg/kg) was tailored to their body weight. We calculated the required contrast agent volume (CAVmean-IVC) to reach the mean enhancement of IVC. We performed univariate and multivariate linear regression analyses between the sex, age, body weight (BW), lean body weight (LBW), body surface area (BSA), height (HT), estimated glomerular filtration rate (eGFR), and CAVmean-IVC.The univariate linear regression analysis show that HT, BW, LBW, and BSA were significantly correlated with CAVmean-IVC (P < .01 for all). The CAVmean-IVC was significantly higher for males than females (P < .01). Multivariate regression analysis showed that BW, LBW, and BSA had a statistically significant effect on CAVmean-IVC. There was no significant correlation of age, HT, or eGFR with CAVmean-IVC.BW, LBW, and BSA each had an independent significant effect on CAVmean-IVC. The conventional BW-tailored contrast injection protocol might be insufficient for CTV.

Weight-adapted iodinated contrast media administration in abdomino-pelvic CT: Can image quality be maintained?

INTRODUCTION

In many centres, a fixed method of contrast-media administration is used for CT regardless of patient body habitus. The aim of this trial was to assess contrast enhancement of the aorta, portal vein, liver and spleen during abdomino-pelvic CT imaging using a weight-adapted contrast media protocol compared to the current fixed dose method.

METHODS

Thirty-nine oncology patients, who had previously undergone CT abdomino-pelvic imaging at the institution using a fixed contrast media dose, were prospectively imaged using a weight-adapted contrast media dose (1.4 ml/kg). The two sets of images were assessed for contrast enhancement levels (HU) at locations in the liver, aorta, portal vein and spleen during portal-venous enhancement phase. The t-test was used to compare the difference in results using a non-inferiority margin of 10 HU.

RESULTS

When the contrast dose was tailored to patient weight, contrast enhancement levels were shown to be non-inferior to the fixed dose method (liver p < 0.001; portal vein p = 0.003; aorta p = 0.001; spleen p = 0.001). As a group, patients received a total contrast dose reduction of 165 ml using the weight-adapted method compared to the fixed dose method, with a mean cost per patient of £6.81 and £7.19 respectively.

CONCLUSION

Using a weight-adapted method of contrast media administration was shown to be non-inferior to a fixed dose method of contrast media administration. Patients weighing 76 kg, or less, received a lower contrast dose which may have associated cost savings. A weight-adapted contrast media protocol should be implemented for portal-venous phase abdomino-pelvic CT for oncology patients with adequate renal function (>70 ml/min/1.73 m²).

Assessment of Cirrhotic Liver Enhancement With Multiphasic Computed Tomography Using a Faster Injection Rate, Late Arterial Phase, and Weight-Based Contrast Dosing

PURPOSE

This study aimed to update our liver computed tomography (CT) protocol according to published guidelines, and to quantitatively evaluate the effect of these modifications.

METHODS

The modified liver CT protocol employed a faster injection rate (5 vs 3 mL/s), later arterial phase (20-second vs 10-second postbolus trigger), and weight-based dosing of iodinated contrast (1.7 mL/kg vs 100 mL fixed dose). Liver and vascular attenuation values were measured on CTs of patients with cirrhosis from January to September 2015 (old protocol, n = 49) and from October to December 2015 (modified protocol, n = 31). CTs were considered adequate if liver enhancement exceeded 50 Hounsfield units (HU) in portal venous phase, or when the unenhanced phase was unavailable, if a minimum iodine concentration of 500 mg I/kg was achieved. Attenuations and iodine concentrations were compared using the t test and the number of suboptimal studies was compared with Fisher's exact test.

RESULTS

CTs acquired with the modified protocol demonstrated higher aortic (P = .001) and portal vein (P < .0001) attenuations in the arterial phase as well as greater hepatic attenuation on all postcontrast phases (P = .0006, .002, and .003 for arterial, venous, and equilibrium phases, respectively). Hepatic enhancement in the portal venous phase (61 ± 15 HU vs 51 ± 16 HU; P = .0282) and iodine concentrations (595 ± 88 mg I/kg vs 456 ± 112 mg I/kg; P < .0001) were improved, and the number of suboptimal studies was reduced from 57% to 23% (P = .01).

CONCLUSIONS

A liver CT protocol with later arterial phase, faster injection rate, and weight-based dosing of intravenous contrast significantly improves liver enhancement and iodine concentrations in patients with cirrhosis, resulting in significantly fewer suboptimal studies.

Double-low protocol for hepatic dynamic CT scan: Effect of low tube voltage and low-dose iodine contrast agent on image quality

The radiation-induced carcinogenesis from computed tomography (CT) and iodine contrast agent induced nephropathy has attracted international attention. The reduction of the radiation dose and iodine intake in CT scan is always a direction for researchers to strive. The purpose of this study was to evaluate the feasibility of a "double-low" (i.e., low tube voltage and low-dose iodine contrast agent) scanning protocol for dynamic hepatic CT with the adaptive statistical iterative reconstruction (ASIR) in patients with a body mass index (BMI) of 18.5 to 27.9 kg/m.A total of 128 consecutive patients with a BMI between 18.5 and 27.9 kg/m were randomly assigned into 3 groups according to tube voltage, iodine contrast agent, and reconstruction algorithms. Group A (the "double-low" protocol): 100 kVp tube voltage with 40% ASIR, iodixanol at 270 mg I/mL, group B: 120 kVp tube voltage with filtered back projection (FBP), iodixanol at 270 mg I/ mL, and group C: 120 kVp tube voltage with FBP, ioversol at 350 mg I/ mL.The volume CT dose index (CTDIvol) and effective dose (ED) in group A were lower than those in group B and C (all P < 0.01). The iodine intake in group A was decreased by approximately 26.5% than group C, whereas no statistical difference was observed between group A and B (P > 0.05). There was no significant difference of the CT values between group A and C (P > 0.05), which both showed higher CT values than that in group B (P < 0.001). However, no statistic difference was observed in the contrast-to-noise ratio (CNR), the signal-to-noise ratio (SNR), and image-quality scores among the 3 groups (all P > 0.05). Near-perfect consistency of the evaluation for group A, B, and C (Kenall's W = 0.921, 0.874, and 0.949, respectively) was obtained by the 4 readers with respect to the overall image quality.These results suggested that the "double-low" protocol with ASIR algorithm for multi-phase hepatic CT scan can dramatically decrease radiation dose and iodine intake with adequate image quality in patients with BMI of 18.5 to 27.9 kg/m.

Contrast medium administration and image acquisition parameters in renal CT angiography: what radiologists need to know

Over the last decade, exponential advances in computed tomography (CT) technology have resulted in improved spatial and temporal resolution. Faster image acquisition enabled renal CT angiography to become a viable and effective noninvasive alternative in diagnosing renal vascular pathologies. However, with these advances, new challenges in contrast media administration have emerged. Poor synchronization between scanner and contrast media administration have reduced the consistency in image quality with poor spatial and contrast resolution. Comprehensive understanding of contrast media dynamics is essential in the design and implementation of contrast administration and image acquisition protocols. This review includes an overview of the parameters affecting renal artery opacification and current protocol strategies to achieve optimal image quality during renal CT angiography with iodinated contrast media, with current safety issues highlighted.