Contrast medium administration with a body surface area protocol in step-and-shoot coronary computed tomography angiography with dual-source scanners

Evaluation of a Tube Voltage-Based Contrast Media Adaptation in Coronary Computed Tomography Angiography Using Personalized Triphasic Injection Protocols: A Matched Case-Control Study

RATIONALE AND OBJECTIVES

Coronary CT angiography (CCTA) has recently been established as a first-line test in patients with suspected coronary artery disease (CAD). Due to the increased use of CCTA, strategies to reduce radiation and contrast medium (CM) exposure are of high importance. The aim of this study was to evaluate the performance of automated tube voltage selection (ATVS)-adapted CM injection protocol for CCTA compared to a clinically established triphasic injection protocol in terms of image quality, radiation exposure, and CM administration MATERIAL AND METHODS: Patients undergoing clinically indicated CCTA were prospectively enrolled from July 2021 to July 2023. Patients underwent CCTA using a modified triphasic CM injection protocol tailored to the tube voltage by the ATVS algorithm, in a range of 70 to 130 kV with a 10 kV interval. The injection protocol consisted of two phases of mixed CM and saline boluses with different proportions to assure a voltage-specific iodine delivery rate, followed by a third phase of saline flush. This cohort was compared to a control group identified retrospectively and scanned on the same CT system but with a standard triphasic CM protocol. Radiation and contrast dose, subjective and objective image quality (contrast-to-noise-ratio [CNR] and signal-to-noise-ratio [SNR]) were compared between the two groups.

RESULTS

The final population consisted of 120 prospective patients matched with 120 retrospective controls, with 20 patients in each kV group. The 120 kV group was excluded from the statistical analysis due to insufficient sample size. A significant CM reduction was achieved in the prospective group overall (46.0 [IQR 37.0-52.0] vs. 51.3 [IQR 40.1-73.0] mL, p < 0.001) and at all kV levels too (all pairwise p < 0.001). There were no significant differences in radiation dose (6.13 ± 4.88 vs. 5.97 ± 5.51 mSv, p = 0.81), subjective image quality (median score of 4 [3-5] vs. 4 [3-5], p = 0.40), CNR, and SNR in the aorta and the left anterior descending coronary artery (all p > 0.05).

CONCLUSION

ATVS-adapted CM injection protocol allows for diagnostic quality CCTA with reduced CM volume while maintaining similar radiation exposure, subjective and objective image quality.

Artificial Intelligence-Based Prediction of Contrast Medium Doses for Computed Tomography Angiography Using Optimized Clinical Parameter Sets

OBJECTIVES

 In this paper, an artificial intelligence-based algorithm for predicting the optimal contrast medium dose for computed tomography (CT) angiography of the aorta is presented and evaluated in a clinical study. The prediction of the contrast dose reduction is modelled as a classification problem using the image contrast as the main feature.

METHODS

 This classification is performed by random decision forests (RDF) and k-nearest-neighbor methods (KNN). For the selection of optimal parameter subsets all possible combinations of the 22 clinical parameters (age, blood pressure, etc.) are considered using the classification accuracy and precision of the KNN classifier and RDF as quality criteria. Subsequently, the results of the evaluation were optimized by means of feature transformation using regression neural networks (RNN). These were used for a direct classification based on regressed Hounsfield units as well as preprocessing for a subsequent KNN classification.

RESULTS

 For feature selection, an RDF model achieved the highest accuracy of 84.42% and a KNN model achieved the best precision of 86.21%. The most important parameters include age, height, and hemoglobin. The feature transformation using an RNN considerably exceeded these values with an accuracy of 90.00% and a precision of 97.62% using all 22 parameters as input. However, also the feasibility of the parameter sets in routine clinical practice has to be considered, because some of the 22 parameters are not measured in routine clinical practice and additional measurement time of 15 to 20 minutes per patient is needed. Using the standard feature set available in clinical routine the best accuracy of 86.67% and precision of 93.18% was achieved by the RNN.

CONCLUSION

 We developed a reliable hybrid system that helps radiologists determine the optimal contrast dose for CT angiography based on patient-specific parameters.

Dedicated CCTA Followed by High-Pitch Scanning versus TRO-CT for Contrast Media and Radiation Dose Reduction: A Retrospective Study

We aimed to compare dedicated coronary computed tomography angiography (CCTA) followed by high-pitch scanning and triple-rule-out computed tomography angiography (TRO-CTA) in terms of radiation dose, contrast media (CM) use, and image quality. Patients with acute chest pain were retrospectively enrolled and assigned to group A (n = 55; scanned with dedicated CCTA followed by high-pitch scanning) or group B (n = 45; with TRO-CTA). Patient characteristics, radiation dose, CM use, and quantitative parameters (CT value, image noise, signal-to-noise ratio, contrast-to-noise ratio, and image quality score) of pulmonary arteries (PAs), thoracic aortae (TAs), and coronary arteries (CAs) were compared. The total effective dose was significantly lower in group A (6.25 ± 2.94 mSv) than B (8.93 ± 4.08 mSv; p < 0.001). CM volume was significantly lower in group A (75.7 ± 8.9 mL) than B (95.0 ± 0 mL; p < 0.001). PA and TA image quality were significantly better in group B, whereas that of CA was significantly better in group A. Qualitative image scores of PA and TA scans rated by radiologists were similar, whereas that of CA scans was significantly higher in group A than B (p < 0.001). Dedicated CCTA followed by high-pitch scanning demonstrated lower radiation doses and CM volume without debasing qualities of PA, TA, and CA scans than did TRO-CTA.