Development of a dynamic flow imaging phantom for dynamic contrast‐enhanced CT
Authors: Driscoll B. , Keller H. , Coolens C. 
Published: August 2011

Purpose:

Dynamic contrast enhanced CT (DCE‐CT) studies with modeling of blood flow and tissue perfusion are becoming more prevalent in the clinic, with advances in wide volume CT scanners allowing the imaging of an entire organ with sub‐second image frequency and sub‐millimeter accuracy. Wide‐spread implementation of perfusion DCE‐CT, however, is pending fundamental validation of the quantitative parameters that result from dynamic contrast imaging and perfusion modeling. Therefore, the goal of this work was to design and construct a novel dynamic flow imaging phantom capable of producing typical clinical time‐attenuation curves (TACs) with the purpose of developing a framework for the quantification and validation of DCE‐CT measurements and kinetic modeling under realistic flow conditions.

Methods:

The phantom is based on a simple two‐compartment model and was printed using a 3D printer. Initial analysis of the phantom involved simple flow measurements and progressed to DCE‐CT experiments in order to test the phantoms range and reproducibility. The phantom was then utilized to generate realistic input TACs. A phantom prediction model was developed to compute the input and output TACs based on a given set of five experimental (control) parameters: pump flow rate, injection pump flow rate, injection contrast concentration, and both control valve positions. The prediction model is then inversely applied to determine thecontrol parameters necessary to generate a set of desired input and output TACs. A protocol was developed and performed using the phantom to investigate image noise, partial volume effects and CT number accuracy under realistic flow conditions

Results:

This phantom and its surrounding flow system are capable of creating a wide range of physiologically relevant TACs, which are reproducible with minimal error between experiments (σ/μ < 5% for all metrics investigated). The dynamic flow phantom was capable of producing input and output TACs using either step function based or typical clinical arterial input function (AIF) inputs. The measured TACs were in excellent agreement with predictions across all comparison metrics with goodness of fit (R2) for the input function between 0.95 and 0.98, while the maximum enhancement differed by no more than 3.3%. The predicted output functions were similarly accurate producing R2 values between 0.92 and 0.99 and maximum enhancement to within 9.0%. The effect of ROI size on the arterial input function (AIF) was investigated in order to determine an operating range of ROI sizes which were minimally affected by noise for small dimensions and partial volume effects for large dimensions. It was possible to establish the measurement sensitivity of both the Toshiba (ROI radius range from 1.5 to 3.2 mm “low dose”, 1.4 to 3.0 mm “high dose”) and GE scanner (1.5 to 2.6 mm “low dose”, 1.1 to 3.4 mm “high dose”). This application of the phantom also provides the ability to evaluate the effect of the AIF error on kinetic model parameter predictions.

Conclusions:

The dynamic flow imaging phantom is capable of producing accurate and reproducible results which can be predicted and quantified. This results in a unique tool for perfusion DCE‐CT validation under realistic flow conditions which can be applied not only to compare different CT scanners and imaging protocols but also to provide a ground truth across multimodality dynamic imaging given its MRI and PET compatibility.