Optimal Kiloelectron Volt for Noise-Optimized Virtual Monoenergetic Images of Dual-Energy Pediatric Abdominopelvic Computed Tomography: Preliminary Results

Abstract

Objective: To compare quantitative and qualitative image quality parameters in pediatric abdominopelvic dual-energy CT (DECT) using noise-optimized virtual monoenergetic image (VMI) and conventional VMI at different kiloelectron volt (keV) levels.

Materials and methods: Thirty-six consecutive abdominopelvic DECT scans were retrospectively included. Noise-optimized VMI and conventional VMI were reconstructed at seven energy levels, from 40 keV to 100 keV at 10 keV intervals. The contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of the liver, pancreas, and aorta were objectively measured and compared. Image quality was evaluated subjectively regarding image noise, image blurring of solid organ, bowel image quality and severity of beam-hardening artifacts. Optimal monoenergetic levels in keV for both algorithms were determined based on overall image quality score.

Results: The maximal CNR and SNR values for all investigated organs were observed at 40 keV in noise-optimized VMI (CNR and SNR of liver, pancreas, aorta in order [CNR; 20.93, 17.34, 46.75: SNR; 37.39, 33.80, 63.21]), at 60-70 keV and at 70 keV in conventional VMI (CNR; 8.12, 5.67, 15.97: SNR; 19.57, 16.66, 26.65). In qualitative image analysis, noise-optimized VMI and conventional VMI showed the best overall image quality scores at 60 keV and at 70 keV, respectively. Noise-optimized VMI at 60 keV showed superior CNRs, SNRs, and overall image quality scores compared to conventional VMI at 70 keV (p < 0.001).

Conclusion: Optimal energy levels for noise-optimized VMI and conventional VMI were 60 keV and at 70 keV, respectively. Noise-optimized VMI shows superior CNRs, SNRs and subjective image quality over conventional VMI, at the optimal energy level.

Keywords: Conventional VMI; Dual-energy pediatric abdominopelvic CT; Image quality; Noise-optimized VMI; Virtual monoenergetic image.

Fig. 1. Manually drawn regions of interest in liver, pancreas, aorta, paraspinal muscle, subcutaneous fat of anterior abdominal wall, and air column.

All measurements were kept constant across VMI levels by using copy-and-paste function at workstation. VMI = virtual monoenergetic image

Fig. 2. Graphs of mean attenuation of liver, pancreas, aorta, subcutaneous fat, and air in mean of noise-optimized and conventional algorithms.

HU = Hounsfield unit, keV = kiloelectron volt

Fig. 3. Graphs of image noise in noise-optimized and conventional algorithms.

Fig. 4. Graphs showing SNR values of liver, pancreas, and aorta in noise-optimized and conventional VMIs.

SNR was significantly higher in noise-optimized algorithm compared to conventional algorithm at all VMI energy levels. SNR = signal-to-noise ratio

Fig. 5. Graphs of CNR values of liver, pancreas, and aorta in noise-optimized and conventional VMIs.

CNR was significantly higher in noise-optimized algorithm compared to conventional algorithm in all VMI energy levels, except for 70 keV. CNR = contrast-to-noise ratio

Fig. 6. Images obtained by noise-optimized algorithm with 40–100 keV energy levels.

Best CNR and SNR were obtained at 40 keV. In subjective analysis, 60 keV scored best in terms of overall image quality. Taken together, 60 keV was considered to be optimal VMI energy level for noise-optimized algorithm.

Fig. 7. Images obtained by conventional algorithm with 40–100 keV energy levels.

Best CNR was obtained at 60–70 keV, and best SNR was obtained at 70 keV. Further, 70 keV achieved best overall image quality score in subjective image analysis. Therefore, 70 keV was considered to be optimal VMI energy level for conventional algorithm.