Contrast Gradient-Based Blood Velocimetry With Computed Tomography: Theory, Simulations, and Proof of Principle in a Dynamic Flow Phantom


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Abstract

ObjectivesThe aim of this study was to introduce a new theoretical framework describing the relationship between the blood velocity, computed tomography (CT) acquisition velocity, and iodine contrast enhancement in CT images, and give a proof of principle of contrast gradient-based blood velocimetry with CT.Materials and MethodsThe time-averaged blood velocity (vblood) inside an artery along the axis of rotation (z axis) is described as the mathematical division of a temporal (Hounsfield unit/second) and spatial (Hounsfield unit/centimeter) iodine contrast gradient. From this new theoretical framework, multiple strategies for calculating the time-averaged blood velocity from existing clinical CT scan protocols are derived, and contrast gradient-based blood velocimetry was introduced as a new method that can calculate vblood directly from contrast agent gradients and the changes therein. Exemplarily, the behavior of this new method was simulated for image acquisition with an adaptive 4-dimensional spiral mode consisting of repeated spiral acquisitions with alternating scan direction. In a dynamic flow phantom with flow velocities between 5.1 and 21.2 cm/s, the same acquisition mode was used to validate the simulations and give a proof of principle of contrast gradient-based blood velocimetry in a straight cylinder of 2.5 cm diameter, representing the aorta.ResultsIn general, scanning with the direction of blood flow results in decreased and scanning against the flow in increased temporal contrast agent gradients. Velocity quantification becomes better for low blood and high acquisition speeds because the deviation of the measured contrast agent gradient from the temporal gradient will increase. In the dynamic flow phantom, a modulation of the enhancement curve, and thus alternation of the contrast agent gradients, can be observed for the adaptive 4-dimensional spiral mode and is in agreement with the simulations. The measured flow velocities in the downslopes of the enhancement curves were in good agreement with the expected values, although the accuracy and precision worsened with increasing flow velocities.ConclusionsThe new theoretical framework increases the understanding of the relationship between the blood velocity, CT acquisition velocity, and iodine contrast enhancement in CT images, and it interconnects existing blood velocimetry methods with research on transluminary attenuation gradients. With these new insights, novel strategies for CT blood velocimetry, such as the contrast gradient-based method presented in this article, may be developed.

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