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

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

doi:10.1007/s00330-024-10974-3

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

Eur Radiol. 2024 Jul 24. doi: 10.1007/s00330-024-10974-3. Online ahead of print.

Authors

Affiliations

¹ Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA.
² Institute for Diagnostic and Interventional Radiology, University Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany.
³ Department of Radiology, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand.
⁴ Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA. akambadakone@mgh.harvard.edu.

PMID: 39046499
DOI: 10.1007/s00330-024-10974-3

Abstract

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.

Keywords: Abdomen; Adaptive statistical iterative reconstruction; Computed tomography; Deep learning; Dual-energy CT.