Dual-Contrast Biphasic Liver Imaging With Iodine and Gadolinium Using Photon-Counting Detector Computed Tomography: An Exploratory Animal Study

Abstract

Purpose: The aims of this study were to develop a single-scan dual-contrast protocol for biphasic liver imaging with 2 intravenous contrast agents (iodine and gadolinium) and to evaluate its effectiveness in an exploratory swine study using a photon-counting detector computed tomography (PCD-CT) system.

Materials and methods: A dual-contrast CT protocol was developed for PCD-CT to simultaneously acquire 2 phases of liver contrast enhancement, with the late arterial phase enhanced by 1 contrast agent (iodine-based) and the portal venous phase enhanced by the other (gadolinium-based). A gadolinium contrast bolus (gadobutrol: 64 mL, 8 mL/s) and an iodine contrast bolus (iohexol: 40 mL, 5 mL/s) were intravenously injected in the femoral vein of a healthy domestic swine, with the second injection initiated after 17 seconds from the beginning of the first injection; PCD-CT image acquisition was performed 12 seconds after the beginning of the iodine contrast injection. A convolutional neural network (CNN)-based denoising technique was applied to PCD-CT images to overcome the inherent noise magnification issue in iodine/gadolinium decomposition task. Iodine and gadolinium material maps were generated using a 3-material decomposition method in image space. A set of contrast samples (mixed iodine and gadolinium) was attached to the swine belly; quantitative accuracy of material decomposition in these inserts between measured and true concentrations was calculated using root mean square error. An abdominal radiologist qualitatively evaluated the delineation of arterial and venous vasculatures in the swine liver using iodine and gadolinium maps obtained using the dual-contrast PCD-CT protocol.

Results: The iodine and gadolinium samples attached to the swine were quantified with root mean square error values of 0.75 mg/mL for iodine and 0.45 mg/mL for gadolinium from the contrast material maps derived from the denoised PCD-CT images. Hepatic arteries containing iodine and veins containing gadolinium in the swine liver could be clearly visualized. Compared with the original images, better distinctions between 2 liver phases were achieved using CNN denoising, with approximately 60% to 80% noise reduction in contrast material maps acquired with the denoised PCD-CT images compared with the original images.

Conclusions: Simultaneous biphasic liver imaging in a single multienergy PCD-CT acquisition using a dual-contrast (iodine and gadolinium) injection protocol and CNN denoising was demonstrated in a swine study, where the enhanced hepatic arteries (containing iodine) and the enhanced hepatic veins (containing gadolinium) could be clearly visualized and delineated in the swine liver.

FIGURE 1.

A, Time enhancement curves for 2 liver phases. B and C, Example single-energy computed tomography images acquired with the optimal timing.

FIGURE 2.

A, Reference single-contrast imaging protocol with 2 single-energy computed tomography scans to capture optimal enhancement of iodine during late arterial and portal venous phases, respectively. B, Dual-contrast imaging protocol with 1 single photon-counting detector computed tomography (PCD-CT) scan to simultaneously capture maximum enhancement of iodine during the late arterial phase and of gadolinium during the portal venous phase.

FIGURE 3.

A, An example PCD-CT threshold-low image ([25 80] keV) showing the contrast samples attached to the pig belly and the corresponding concentration values. B and C, Iodine maps acquired based on original and denoised PCD image data. D and E, Gadolinium maps acquired based on original and denoised PCD image data. F and I, Linearity analyses performed on iodine and gadolinium contrast samples.

FIGURE 4.

Top, Original PCD-CT images reconstructed from 4 energy bins, namely, [25 35], [35 50], [50 55], and [55 80] keV showing iodine-containing hepatic arteries and gadolinium-containing hepatic veins. Middle, Corresponding convolutional neural network–denoised images indicating significant noise reductions. Bottom, Difference images between the original and the denoised images showing no visible anatomical structures.

FIGURE 5.

A, Baseline unenhanced reference image (threshold-low: [25 80] keV). B, Threshold-low image ([25 80] keV) acquired from the dual-contrast scan after sequential injections of gadolinium-based and iodine-based contrast agent; (C) iodine, (D) gadolinium, and (E) fused-color maps after material decomposition on the original PCD-CT images; (F) iodine, (G) gadolinium, and (H) fused-color maps after convolutional neural network denoising; with noise suppression in contrast material maps, veins containing gadolinium-based contrast agent (green arrows) and arteries containing iodine-based contrast agent (red arrow) could be better delineated after material decomposition. Note that bone is not one of the basis materials, so it was decomposed mostly into the iodine map and showed up mostly as iodine content, which should be ignored in this particular task.

FIGURE 6.

Maximum intensity projection of the fused contrast material map after material decomposition with (A) original and (B) denoised images.