Comparison of image quality in 40 keV virtual monoenergetic images of dual-energy CT pulmonary angiography using deep learning and iterative reconstruction algorithms under optimized low dose scanning protocols
Year: 2025
Type: article
Abstract: Background: Pulmonary embolism is a potentially fatal cardiovascular condition that demands prompt and accurate diagnostic imaging. Traditional single-energy computed tomography pulmonary angiography (CTPA), while widely used, is associated with high radiation doses and substantial volumes of contrast agents, which may increase the risks of radiation-induced tissue damage and contrast-induced nephropathy (CIN), respectively. Dual-energy CTPA (DE-CTPA) presents a promising alternative, though challenges, including elevated image noise at low kilo-electron volt (keV) levels (e.g., 40 keV), persist. The primary aim of this study is to evaluate and compare the image quality of 40 keV virtual monoenergetic images (VMI) reconstructed using deep learning image reconstruction (DLIR) and Adaptive Statistical Iterative Reconstruction-V (ASIR-V) algorithms within the context of low-dose DE-CTPA protocols. Methods: This prospective study enrolled patients who underwent DE-CTPA between January and April 2025. Using a Revolution CT scanner, 40 keV VMI were reconstructed with four distinct algorithms: ASIR-V 50%, ASIR-V 70%, Deep learning image reconstruction with medium setting (DLIR-M), and deep learning image reconstruction with high setting (DLIR-H). Iodixanol (350 mgI/mL) was administered at a dose of 0.4 mL/kg. The image quality was assessed through both objective measures [image noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR)] and subjective evaluation via a Likert scale. Statistical analysis was conducted using SPSS 27.0, employing analysis of variance (ANOVA) for normally distributed data and the Kruskal-Wallis test for non-normally distributed data. Results: 23.71±8.47 at RPA) (all P<0.001). Subjectively, DLIR-H achieved the highest median scores (5.0) for noise, spatial resolution, noise texture, and overall image quality, significantly outperforming both ASIR-V 50% and 70% (P<0.001). Conclusions: The DLIR-H algorithm significantly enhances the image quality of 40 keV VMI images under low-dose DE-CTPA scanning protocols. It outperforms DLIR-M, ASIR-V 50%, and ASIR-V 70%, making it a promising tool for improving image quality in CTPA, particularly in clinical settings where minimizing radiation dose and contrast agent volume is essential.
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4416613522