Multi-Energy Evaluation of Image Quality in Spectral CT Pulmonary Angiography Using Different Strength Deep Learning Spectral Reconstructions.

Rationale And Objectives: To evaluate and compare image quality of different energy levels of virtual monochromatic images (VMIs) using standard versus strong deep learning spectral reconstruction (DLSR) on dual-energy CT pulmonary angiogram (DECT-PA).

Materials And Methods: A retrospective study was performed on 70 patients who underwent DECT-PA (15 PE present; 55 PE absent) scans. VMIs were reconstructed at different energy levels ranging from 35 to 200 keV using standard and strong levels with deep learning spectral reconstruction. Quantitative assessment was performed using region of interest (ROI) analysis of eleven different anatomical areas, measuring absolute attenuation, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). In addition, CNR of clot compared to normally opacified lumen was calculated in cases that were positive for PE. For qualitative analysis, four different keV levels (40-60-80-100) were evaluated.

Results: The image noise was significantly lower, and the cardiovascular SNR (24.9 ± 5.85 vs. 21.98 ± 5.49) and CNR (23.72 ± 8.00 vs. 20.31 ± 6.44) were significantly higher, on strong Deep Learning Spectral reconstruction (DLSR) than standard DLSR (p < 0.0001). PE-specific CNR (8.58 ± 4.47 vs. 6.25 ± 3.19) was significantly higher on strong DLSR than standard (p < 0.0001). The subjective image quality scores were diagnostically acceptable at four different keV levels (40-60-80-100 keV) evaluated using both standard and strong DLSR, with no qualitative differences observed at those energies.

Conclusion: Strong DLSR improves image quality with an increase of the SNR and CNR in DECT-PA compared to standard DLSR.