Maximizing Iodine Contrast-to-Noise Ratios in Abdominal CT Imaging through Use of Energy Domain Noise Reduction and Virtual Monoenergetic Dual-Energy CT

Abstract

Purpose: To determine the iodine contrast-to-noise ratio (CNR) for abdominal computed tomography (CT) when using energy domain noise reduction and virtual monoenergetic dual-energy (DE) CT images and to compare the CNR to that attained with single-energy CT at 80, 100, 120, and 140 kV.

Materials and methods: This HIPAA-compliant study was approved by the institutional review board with waiver of informed consent. A syringe filled with diluted iodine contrast material was placed into 30-, 35-, and 45-cm-wide water phantoms and scanned with a dual-source CT scanner in both DE and single-energy modes with matched scanner output. Virtual monoenergetic images were generated, with energies ranging from 40 to 110 keV in 10-keV steps. A previously developed energy domain noise reduction algorithm was applied to reduce image noise by exploiting information redundancies in the energy domain. Image noise and iodine CNR were calculated. To show the potential clinical benefit of this technique, it was retrospectively applied to a clinical DE CT study of the liver in a 59-year-old male patient by using conventional and iterative reconstruction techniques. Image noise and CNR were compared for virtual monoenergetic images with and without energy domain noise reduction at each virtual monoenergetic energy (in kiloelectron volts) and phantom size by using a paired t test. CNR of virtual monoenergetic images was also compared with that of single-energy images acquired with 80, 100, 120, and 140 kV.

Results: Noise reduction of up to 59% (28.7 of 65.7) was achieved for DE virtual monoenergetic images by using an energy domain noise reduction technique. For the commercial virtual monoenergetic images, the maximum iodine CNR was achieved at 70 keV and was 18.6, 16.6, and 10.8 for the 30-, 35-, and 45-cm phantoms. After energy domain noise reduction, maximum iodine CNR was achieved at 40 keV and increased to 30.6, 25.4, and 16.5. These CNRs represented improvement of up to 64% (12.0 of 18.6) with the energy domain noise reduction technique. For single-energy CT at the optimal tube potential, iodine CNR was 29.1 (80 kV), 21.2 (80 kV), and 11.5 (100 kV). For patient images, 39% (24 of 61) noise reduction and 67% (0.74 of 1.10) CNR improvement were observed with the energy domain noise reduction technique when compared with standard filtered back-projection images.

Conclusion: Iodine CNR for adult abdominal CT may be maximized with energy domain noise reduction and virtual monoenergetic DE CT.

Figure 1:

Phantom set-up. Three torso-shaped water phantoms were used to simulate the attenuation of adult abdomens, and syringes filled with iodine solution were used to simulate contrast-enhanced lesions.

Figure 2:

Virtual monoenergetic images of the 35-cm phantom at, A, D, 50; B, E, 70; and, C, F, 100 keV before (A–C) and after (D–F ) energy domain noise reduction. Locations of circular regions of interest over the 10 mg/mL iodine solution and water background are shown.

Figure 3:

Graph shows image noise of DE virtual monoenergetic images before and after energy domain noise reduction for three phantom sizes and eight energy settings. Error bars = standard deviation, W/O = without.

Figure 4:

Graph shows iodine CNR of DE virtual monoenergetic images before and after domain noise reduction for three phantom sizes and at eight image energies. Error bars = standard deviation, W/O = without.

Figure 5:

Graph shows the iodine CNR of single-energy images at each of the four tube potentials (80–140 kV ) for each of the three phantom sizes. The maximum iodine CNR for any given phantom size achieved with single-energy scanning is less than the maximum CNR achieved by using virtual monoenergetic DE CT images with energy domain noise reduction (horizontal lines). Error bars = standard deviation.

Figure 6a:

Clinical CT images in a 59-year-old male patient with hyperattenuating liver lesion (arrow) obtained with the same window width and level settings (520/130). (a) Source low-energy (top) and high-energy (bottom) images. (b) Mixed image. (c, d) Virtual 50-keV monoenergetic images without (c) and with (d) energy domain noise reduction obtained with a standard FBP reconstruction algorithm.

Figure 6b:

Clinical CT images in a 59-year-old male patient with hyperattenuating liver lesion (arrow) obtained with the same window width and level settings (520/130). (a) Source low-energy (top) and high-energy (bottom) images. (b) Mixed image. (c, d) Virtual 50-keV monoenergetic images without (c) and with (d) energy domain noise reduction obtained with a standard FBP reconstruction algorithm.

Figure 6c:

Clinical CT images in a 59-year-old male patient with hyperattenuating liver lesion (arrow) obtained with the same window width and level settings (520/130). (a) Source low-energy (top) and high-energy (bottom) images. (b) Mixed image. (c, d) Virtual 50-keV monoenergetic images without (c) and with (d) energy domain noise reduction obtained with a standard FBP reconstruction algorithm.

Figure 6d:

Clinical CT images in a 59-year-old male patient with hyperattenuating liver lesion (arrow) obtained with the same window width and level settings (520/130). (a) Source low-energy (top) and high-energy (bottom) images. (b) Mixed image. (c, d) Virtual 50-keV monoenergetic images without (c) and with (d) energy domain noise reduction obtained with a standard FBP reconstruction algorithm.

Figure 7a:

Virtual 50-keV monoenergetic images obtained with an iterative reconstruction algorithm (a) without and (b) with energy domain noise reduction.

Figure 7b:

Virtual 50-keV monoenergetic images obtained with an iterative reconstruction algorithm (a) without and (b) with energy domain noise reduction.