Noise-optimized advanced image-based virtual monoenergetic imaging for improved visualization of lung cancer: Comparison with traditional virtual monoenergetic imaging. Issue 3 (March 2016)

• Record Type: Journal Article
• Authors: Frellesen, Claudia; Kaup, Moritz; Wichmann, Julian L.; Hüsers, Kristina; Scholtz, Jan-Erik; Albrecht, Moritz H.; Metzger, Sarah C.; Bauer, Ralf W.; Kerl, J. Matthias; Lehnert, Thomas; Vogl, Thomas J.; Bodelle, Boris
• Is Part Of: European journal of radiology. Volume 85:Issue 3(2016)
• Page Start: 665
• Page End: 672
• Publication Date: 2016-03
• Subjects: Monoenergetic -- Dual-energy CT -- Dual-source CT -- Lung cancer -- Chest; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2015.12.022
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Image noise was significantly reduced at low keV-levels with the novel monoenergetic algorithm. Tumor contrast-to-noise ratio peaked in the 40/55 keV series reconstructed with the novel algorithm. Tumor signal-to-noise and contrast-to-noise ratios were higher with noise-optimized VMI+ compared to traditional VMI. Subjective image quality ratings were highest for the 55 keV series reconstructed with the novel algorithm. The novel algorithm for monoenergetic dual-energy CT may improve visualization of lung cancer. Abstract: Purpose: To assess the effect of a noise-optimized image-based virtual monoenergetic imaging (VMI+) algorithm in direct comparison with the traditional VMI technique and standard linearly-blended images emulating 120-kVp acquisition (M_0.3) on image quality at dual-energy CT in patients with lung cancer. Materials and Methods: Dual-source dual-energy CT examinations of 48 patients with biopsy-proven primary ( n = 31) or recurrent ( n = 20) lung cancer were evaluated. Images were reconstructed as M_0.3, and VMI+ and traditional VMI series at 40, 55, and 70 keV. Attenuation of tumor, descending aorta, pulmonary trunk, latissimus muscle, and noise were measured. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Five-point scales were used by three observers to subjectively evaluate general image impression, tumor delineation, image sharpness, and image noise. Results: Background noise was consistently lower with VMI+ compared to VMI at all keV levels (all p < 0.0001) and M_0.3 (all p ≤ 0.0004). Tumor SNR and CNR peaked in the 40 keV VMI+ series, significantly higher compared to all VMI and M_0.3 series (all p < 0.0008). Observers preferred the 55 keV VMI+ series regarding general image impression and tumor delineation compared to all other series (all p < 0.0001). Image sharpness and image noise ratings were highest in the 55 keV VMI+ and 70 keV VMI and VMI+ reconstructions. Conclusions: Tumor CNR peaked at 40 keV VMI+ while observers preferred 55 keV VMI+ series overall other series for dual-energy CT of lung cancer. The noise-optimized VMI+ technique showed significantly lower background noise and higher SNR and CNR compared to the traditional VMI technique at matching keV levels.