Noise characteristics of virtual monoenergetic images from a novel detector-based spectral CT scanner. Issue 98 (January 2018)

• Record Type: Journal Article
• Authors: Kalisz, Kevin; Rassouli, Negin; Dhanantwari, Amar; Jordan, David; Rajiah, Prabhakar
• Is Part Of: European journal of radiology. Issue 98(2018)
• Page Start: 118
• Page End: 125
• Publication Date: 2018-01
• Subjects: Spectral -- Dual energy -- CT -- Noise -- Abdomen; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2017.11.005
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: High noise in virtual monoenergetic images (VMI) is a consequence of the steps used in image generation. The dual layer detector-based spectral scanner uses anti-correlated noise reduction in the reconstruction process. VMI obtained from detector-based spectral CT have low noise across the entire energy spectrum. The SNR and CNR of these VMI are significantly improved compared to conventional polyenergetic images. VMI can be used for increasing vascular contrast, improving lesion conspicuity and decreasing artifacts. Abstract: Aim: To evaluate the noise characteristics of virtual monoenergetic images (VMI) obtained from a recently introduced dual-layer detector-based spectral CT (SDCT), both in a phantom and patients. Materials and methods: A cylindrical Catphan ® 600 phantom (The Phantom Library, Salem NY, USA) was scanned using the SDCT. Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured in VMI from 40 to 200 keV as well as conventional 120 kVp images. One hundred consecutive patients who had an abdominal CT on the SDCT were then recruited in the study. Noise, SNR and CNR were measured in the liver, pancreas, spleen, kidney, abdominal aorta, portal vein, muscle, bone, and fat, both in VMI (40–200 keV) and conventional 120 kVp images. Qualitative image analysis was performed by an independent reader for vascular enhancement and image quality on a 5 point scale (1-worst, 5-best). Results: On phantom studies, noise was low at all energies of VMI. Noise was highest at 40 keV (5.3 ± 0.2 HU), gradually decreased up to 70 keV (3.6 ± 0.2 HU), after which it remained constant up to 200 keV (3.5 ± 0.2 HU). In the patient cohort, noise was low (<25 HU) at all the energy levels of VMI for all the regions, with the exception of bone. For example, noise in the liver was highest at 40 keV (13.2 ± 4.6 HU), steadily decreased up to 70 keV (12.0 ± 4.4 HU) and then remained constantly low up to 200 keV (11.6 ± 4.3HU). For liver, pancreas, portal vein, aorta, muscle and fat, noise at all levels of VMI was lower than of conventional images (p < 0.01). For all organs, SNR, and CNR were highest at 40 keV (6.8–34.9; 18.3–44.9, respectively) after which they gradually decreased up to 120 keV (3.4–6.5; 9.5–13.0) and then remained constant to 200 keV (2.6–5.5; 8.5–12.5). Qualitative scores of VMI up to 70 keV were significantly higher than the conventional images (p ≤ 0.01), whereas for VMI ≥ 80 keV, they were lower than conventional images (p < 0.001). Conclusion: VMI obtained from the novel SDCT scanner have low noise across the entire spectrum of energies. There are significant SNR and CNR improvements compared to conventional 120 kVp images.