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.

BACKGROUND

Overweight and obesity are significant risk factors for carotid atherosclerosis in patients with metabolic syndrome and type 2 diabetes mellitus, and carotid computed tomography angiography (CTA) plays a critical role in assessing vascular health. However, obese patients often require higher doses of radiation and contrast agents, which can pose risks. The deep learning image reconstruction with high setting (DLIR-H) algorithm offers the potential to enhance image quality while minimizing exposure. The objective of this study was to evaluate the effectiveness of the DLIR-H algorithm in improving CTA image quality under a triple-low scan protocol (low radiation dose, low contrast agent usage, and low injection rate) for overweight and obese patients [body mass index (BMI) >25 kg/m2], 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

The DLIR-H algorithm demonstrated image quality comparable to that of the ASIR-V algorithm. The experimental group exhibited a 49.4% reduction in ED (calculated from the dose length product, DLP) and a 13.5% reduction in contrast agent usage compared to the control group. At the AA level, the DLIR-H group had a significantly lower CTV than the control group [561.90 (516.90, 661.00) vs. 649.30 (572.60, 745.50), P<0.05]. At the CCA level, the DLIR-H group demonstrated a significantly lower SDV than the control group [35.90 (29.20, 43.80) vs. 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.