Advanced noise-optimized virtual monochromatic imaging for improving the contrast of dual-energy CT pulmonary angiography

Abstract Background Pulmonary embolism (PE) is a serious condition requiring accurate imaging for timely diagnosis. While computed tomography pulmonary angiography (CTPA) is the standard tool, its quality may be limited by contrast timing or noise. Dual-energy CT enables virtual monochromatic imaging (VMI), which enhances contrast but suffers from high noise at low keV levels. Advanced VMI (AVMI) addresses this by optimizing noise while preserving contrast. The objective of this study was to evaluate the quantitative image quality of an advanced virtual monoenergetic imaging (AVMI) algorithm in comparison with the equivalent 120-kVp acquisition at dual-energy CT pulmonary angiography (DE-CTPA) in patients with suspected pulmonary embolism (PE). Methods We evaluated dual-source dual-energy CTPA examinations from 123 patients (58 men and 65 women; mean age, 54.2 ± 17.7 years; age range, 20–87 years) who had suspected pulmonary embolism (PE). Images were generated as AVMI series with energies ranging from 40- to 190-keV in 1-keV steps. Attenuation, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured in five different pulmonary arteries. AVMIs at 75-keV are considered equivalent to standard 120-kVp single energy CT (SECT) acquisition. The CNR and SNR were compared in AVMIs and 120-kVp equivalent images for each artery by repeated-measures analysis of variance (ANOVA) with post-hoc Bonferroni correction. Result The highest mean attenuation and CNR value of pulmonary arteries were observed in the 40-keV AVMI series, with significant CNR and SNR values compared to 120-kVp equivalent images ( p < 0.05). At 40-keV, energy level improvements of attenuation, SNR, and CNR by 225.8%, 31.0%, 51.2% were observed compared to 120-kVp equivalent images, respectively. Conclusion The advanced noise-optimized VMI series showed significantly higher SNR and CNR in comparison with 120-kVp equivalent images.