Work

Comparison of deep learning reconstruction and iterative reconstruction algorithms for virtual monoenergetic image quality in overweight and obese patients with triple-low scan protocol dual-energy carotid computed tomography angiography

Year: 2026

Type: article

Abstract: Background: ], using dual-energy CTA (DE-CTA) and virtual monoenergetic images (VMIs) at 50 keV. Methods: A prospective study was conducted involving 62 patients who were randomly assigned to either the control or experimental group. The experimental group used the adaptive statistical iterative reconstruction-V (ASIR-V) 50%, deep learning image reconstruction with low setting (DLIR-L), deep learning image reconstruction with medium setting (DLIR-M), and DLIR-H algorithms with reduced radiation exposure and contrast agent. Both objective and subjective image quality evaluations were conducted. The effective dose (ED), contrast agent dose, computed tomography values (CTV), standard deviation of the carotid artery vessels (SDV), contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were calculated and compared at four anatomical regions: the aortic arch (AA), common carotid artery (CCA) origin, carotid bifurcation (CB), and internal carotid artery (ICA) origin. Results: 41.70 (35.90, 54.70), P<0.05]. Except for the CCA level, at other anatomical levels, the DLIR-H group showed significantly lower SDV compared with the ASIR-V 50%, DLIR-L, and DLIR-M groups (P<0.05). Additionally, the DLIR-H group exhibited higher CNR and SNR than the ASIR-V 50%, DLIR-L, and DLIR-M groups at several anatomical levels (P<0.05). Conclusions: The DLIR-H algorithm significantly enhances image quality in CTA, reducing both radiation exposure and contrast agent usage in overweight and obese patients.

Source: Quantitative Imaging in Medicine and Surgery

DOI: 10.21037/qims-2025-856