Evaluation of A New Bolus Tracking-Based Algorithm for Predicting A Patient-Specific Time of Arterial Peak Enhancement in Computed Tomography Angiography.

OBJECTIVES

The aim of this study was to evaluate the systematic and random errors of a new bolus tracking-based algorithm that predicts a patient-specific time of peak arterial enhancement and compare its performance with a best-case scenario for the current bolus tracking technique.

MATERIALS AND METHODS

All local institutional review boards approved this retrospective study, in which the test bolus signals of cardiac computed tomography angiographies of 72 patients (46 men; median age, 62 years [range, 31-81 years]) were used to simulate contrast enhancement curves for a multitude of injection protocols with iodine delivery rates (IDRs) varying between 0.5 and 2.5 gI/s, injection durations between 4 and 30 seconds, and tube voltages of 100 and 120 kV. From these simulated curves, bolus tracking signals with statistical errors of 4 Hounsfield units (HU) (standard deviation) were derived with trigger values of 100 and 150 HU at 100 and 120 kV, respectively. The new algorithm then matched the actual bolus tracking signal with a database of expected enhancement curves for that particular injection protocol, taking into account population-averaged blood circulation characteristics with variations in patient weight and cardiac output. Posttrigger delays (PTDs) were calculated as the time difference between the last bolus tracking point and the time of peak enhancement. The systematic and random errors between the predicted and true PTDs were assessed and compared with a best-case scenario for the current bolus tracking method.

RESULTS

With the current bolus tracking technique, interpatient variations decrease with higher IDRs and earlier triggering (lower tube voltage and/or lower trigger value), and the true PTDs increase linearly with injection duration. Compared with the current bolus tracking method, the systematic and random errors of the algorithm-predicted PTDs are smaller, do not depend on the IDR, and are predictable over a large range of total iodine doses. The median difference between the true and algorithm-predicted PTD is less than ±1 second for all IDRs and injection durations, and the algorithm was able to predict patient-specific PTDs within ±2 seconds from the true PTD in more than 90% of patients for almost all injection protocols.

CONCLUSIONS

The new algorithm can robustly predict a patient-specific time of arterial peak enhancement and is better than a best-case scenario for the current bolus tracking technique because interpatient variations are taken into account. It offers a new framework for scan timing optimization and can potentially be used for personalized scan timing in real time.