Spectral CT: Current Liver Applications

Abstract

Using two different energy levels, dual-energy computed tomography (DECT) allows for material differentiation, improves image quality and iodine conspicuity, and allows researchers the opportunity to determine iodine contrast and radiation dose reduction. Several commercialized platforms with different acquisition techniques are constantly being improved. Furthermore, DECT clinical applications and advantages are continually being reported in a wide range of diseases. We aimed to review the current applications of and challenges in using DECT in the treatment of liver diseases. The greater contrast provided by low-energy reconstructed images and the capability of iodine quantification have been mostly valuable for lesion detection and characterization, accurate staging, treatment response assessment, and thrombi characterization. Material decomposition techniques allow for the non-invasive quantification of fat/iron deposition and fibrosis. Reduced image quality with larger body sizes, cross-vendor and scanner variability, and long reconstruction time are among the limitations of DECT. Promising techniques for improving image quality with lower radiation dose include the deep learning imaging reconstruction method and novel spectral photon-counting computed tomography.

Keywords: dual-energy CT; dual-layer detector CT; dual-source CT; fast kVp switching; image quality; liver disease; pancreatic disease; photon counting; spectral CT; split-filter.

Figure 1

Rho/Z map application. Effective atomic number (Z_eff) and electron density (Rho) maps allow for the semiquantitative assessment of materials through measurements drawn within a region of interest (ROI), providing Z_eff and HU_Rho measurements which can be used to calculate the electron density relative to water (ρe). In the Siemens platform, the electron density values (HU_Rho) are converted into the Hounsfield unit scale, in which water has a value of 0 HU and air has a value of −1000 HU. The effective atomic number (Z) is presented in units of 1. The numbers provided in the measured ROI in this figure refer to the attenuation at the Au120 filter/attenuation at Sn120 filter/Rho value/Z value.

Figure 2

Iodine subtraction and overlay. Iodine may be subtracted, generating virtual non-contrast images (upper image), or superimposed onto grayscale images, generating iodine maps, at a configurable percentage of overlay (50% and 100% in the middle and bottom images, respectively).

Figure 3

Blended dual-energy image reconstruction. The blended energy values may be chosen by the user, balancing the greater conspicuity of differences in contrast enhancement (at the cost of higher image noise) with low energy values, with the opposite effect of high-energy images. In this example from a TwinBeam DECT scanner (Siemens Healthineers), low- and high-energy beams are derived from a split-filter of gold (Au) and tin (Sn) at the X-ray tube.

Figure 4

Virtual monochromatic images (Monoenergetic Plus advanced noise-optimized algorithm) from 40 keV to 190 keV in the axial plane (portal venous phase) and mixed 120-kVp-equivalent image (120-Eq). Window settings were kept constant for a more realistic comparability. Note the greater noise with low-keV images and the similarity of the 70 keV VMIs with the blended 120-kVp-equivalent image (120-Eq).

Figure 5

Metal artifact reduction with high-energy VMIs. Virtual monochromatic images (Monoenergetic Plus advanced noise-optimized algorithm) at 70, 100 and 120 keV in the axial plane (portal venous phase) show improved luminal depiction of a metallic biliary stent (arrow) with higher monoenergetic levels due to metal artifact reduction.

Figure 6

Spectral attenuation curves obtained from iodinated blood in aorta (1), liver parenchyma (2), bile in the gallbladder lumen (3), and abdominal wall fat (4). These are plots of X-ray beam attenuation measurements across a range of monochromatic energy levels, which may be helpful in the characterization of specific materials based on the curve morphology. Note the increasing attenuation of iodine (high atomic number material) at lower energies, as opposed to water materials (stable) and fat (decreasing).

Figure 7

Hypervascular liver metastasis from a pancreatic neuroendocrine tumor, better depicted with low-energy VMIs (70, 50 and 40 keV) obtained from contrast-enhanced DECT in the arterial phase. Postprocessed low-energy VMIs (Monoenergetic Plus advanced noise-optimized algorithm) show the improved conspicuity of the lesion (arrow) compared to the blended 120-kVp-equivalent image (120-Eq) at the cost of increased image noise.

Figure 8

Multifocal hepatocellular carcinoma, better depicted with low-energy VMIs (70, 50 and 40 keV) obtained from contrast-enhanced DECT in the arterial phase. Postprocessed low-energy VMIs (Monoenergetic Plus advanced noise-optimized algorithm) show improved conspicuity of the larger lesion (arrow) compared to the blended 120-kVp-equivalent image (120-Eq) and allow the depiction of a smaller and more subtle lesions at lower keV (arrowhead) at the cost of increased image noise.

Figure 9

Iodine quantification of hepatocellular carcinomas (HCC). Intratumoral iodine concentration (left image) and normalized iodine concentration (right image) of HCC in two different patients, measured in the arterial phase. Higher values of preoperative iodine concentrations in the arterial phase predict early recurrence after resection, meaning that they can be valuable predictive biomarkers.

Figure 10

Improved depiction of a hypovascular liver lesion with low-energy VMIs. Contrast-enhanced DECT images in the portal venous phase show a hypovascular liver lesion (arrows). Postprocessed low-energy VMIs (Monoenergetic Plus advanced noise-optimized algorithm by Siemens Healthineers) show improved conspicuity of the lesion margins compared to the blended 120-kVp-equivalent image (120-Eq) at the cost of increased image noise.

Figure 11

Iodine maps for characterization of hypodense liver lesions. Blended and iodine overlay images from contrast-enhanced DECT images in the portal venous phase in three different patients show small hypodense lesions with iodine densities of 2.6 mg/mL (a) and 1.2 mg/mL (b) in the cases of liver metastasis from carcinoid tumor and colorectal cancer, respectively, as opposed to simple liver cysts in the bottom images (c), which present less than 1 mg/mL of iodine concentration.

Figure 12

Spectral attenuation curves to confirm the presence of enhancement. Iodine overlay images from contrast-enhanced DECT images in the arterial phase show two small hypodense lesions with iodine density of 1 mg/mL. The superior lesion shows an exponential increase in attenuation at lower energy levels (white curve and circle), as opposed to pseudoenhancement from a cystic lesion, which exhibits a flatter curve (blue curve and circle). The yellow curve and circle correspond to liver parenchyma measurement and is shown for reference.

Figure 13

Iodine quantification and spectral attenuation curve analysis obtained from DECT during the arterial phase after transarterial chemoembolization of hepatocellular carcinoma with doxorubicin-eluting microspheres in two different patients. Top images show a hypodense area with iodine density of −0.4 mg/mL and a flat spectral attenuation curve (white curve and circle), consistent with necrotic area. The bottom images show a hypodense area with iodine density of 0.9 mg/mL and a flat spectral attenuation curve (blue curve and circle), with a peripheral thick rim exhibiting iodine density of 4.1 mg/mL and a steep descending spectral attenuation curve (white curve and circle), indicative of active tumor. Yellow curves and circles correspond to liver parenchyma measurements, for reference.

Figure 14

Spectral attenuation curve analysis obtained from DECT during late arterial phase after transarterial chemoembolization (TACE) of hepatocellular carcinoma (HCC) with doxorubicin-eluting microspheres. Recurrent HCC in the left lobe (1) exhibits a steep descending spectral attenuation curve, as opposed to the liver parenchyma (2) and the necrotic area of previous TACE (3).

Figure 15

Liver fat quantification using dual-energy CT in two different patients. Fat fraction color-coded maps generated from DECT images in the axial plane show no fat infiltration in the first patient (a) and different fat fractions obtained from three regions of interest in another patient (b).

Figure 16

Liver fibrosis staging with dual-energy CT. The extracellular space (ECS) expansion induced by fibrosis may be quantified by measuring iodine concentration (IC), which increases in the delayed phase. Additionally, reduced portal flow with hepatic arterial buffer response in liver fibrosis leads to reduced IC during portal venous phase and increased IC during the arterial phase. NIC—normalized IC.

Figure 17

Improved vascular depiction with low-energy VMIs (Monoenergetic Plus advanced noise-optimized algorithm) in a scan with poor intrahepatic contrast enhancement due to the inadequate timing of acquisition.