Journal Article

Multi-reader multiparametric DECT study evaluating different strengths of iterative and deep learning-based image reconstruction techniques

Yuenyongsinchai, Kampon, Pierce, Theodore T., Lennartz, Simon, Cao, Jinjin, Sertic, Madeleine, Pisuchpen, Nisanard, Mroueh, Nayla, Chung, Ryan, Kongboonvijit, Sasiprang, Srinivas Rao, Shravya, Kambadakone, Avinash R., Mercaldo, Nathaniel D.

2025

Overview

Objectives To perform a multi-reader comparison of multiparametric dual-energy computed tomography (DECT) images reconstructed with deep-learning image reconstruction (DLIR) and standard-of-care adaptive statistical iterative reconstruction-V (ASIR-V). Methods This retrospective study included 100 patients undergoing portal venous phase abdominal CT on a rapid kVp switching DECT scanner. Six reconstructed DECT sets (ASIR-V and DLIR, each at three strengths) were generated. Each DECT set included 65 keV monoenergetic, iodine, and virtual unenhanced (VUE) images. Using a Likert scale, three radiologists performed qualitative assessments for image noise, contrast, small structure visibility, sharpness, artifact, and image preference. Quantitative assessment was performed by measuring attenuation, image noise, and contrast-to-noise ratios (CNR). For the qualitative analysis, Gwet’s AC2 estimates were used to assess agreement. Results DECT images reconstructed with DLIR yielded better qualitative scores than ASIR-V images except for artifacts, where both groups were comparable. DLIR-H images were rated higher than other reconstructions on all parameters ( p -value < 0.05). On quantitative analysis, there was no significant difference in the attenuation values between ASIR-V and DLIR groups. DLIR images had higher CNR values for the liver and portal vein, and lower image noise, compared to ASIR-V images ( p -value < 0.05). The subgroup analysis of patients with large body habitus (weight ≥ 90 kg) showed similar results to the study population. Inter-reader agreement was good-to-very good overall. Conclusion Multiparametric post-processed DECT datasets reconstructed with DLIR were preferred over ASIR-V images with DLIR-H yielding the highest image quality scores. Clinical relevance statement Deep-learning image reconstruction in dual-energy CT demonstrated significant benefits in qualitative and quantitative image metrics compared to adaptive statistical iterative reconstruction-V. Key Points Dual-energy CT (DECT) images reconstructed using deep-learning image reconstruction (DLIR) showed superior qualitative scores compared to adaptive statistical iterative reconstruction-V (ASIR-V) reconstructed images, except for artifacts where both reconstructions were rated comparable. While there was no significant difference in attenuation values between ASIR-V and DLIR groups, DLIR images showed higher contrast-to-noise ratios (CNR) for liver and portal vein, and lower image noise (p value < 0.05). Subgroup analysis of patients with large body habitus (weight ≥ 90 kg) yielded similar findings to the overall study population.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Abdomen / Adult / Aged / Aged, 80 and over / Attenuation / Clinical medicine / Computed Tomography / Coronary vessels / Datasets / Deep Learning / Diagnostic Radiology / Energy / Female / Group theory / Humans / Image processing / Image Processing, Computer-Assisted - methods / Image quality / Image reconstruction / Imaging / Internal Medicine / Interventional Radiology / Iodine / Liver / Male / Medical imaging / Medicine / Medicine & Public Health / Middle Aged / Neuroradiology / Noise measurement / Patients / Population studies / Portal vein / Qualitative analysis / Quantitative analysis / Radiation / Radiographic Image Interpretation, Computer-Assisted - methods / Radiography, Dual-Energy Scanned Projection - methods / Radiology / Retrospective Studies / Statistical analysis / Subgroups / Tomography, X-Ray Computed - methods / Ultrasound / Veins / Weight