Deep learning image reconstruction algorithm: impact on image quality in coronary computed tomography angiography

Abstract

Purpose To perform a comprehensive intraindividual objective and subjective image quality evaluation of coronary CT angiography (CCTA) reconstructed with deep learning image reconstruction (DLIR) and to assess correlation with routinely applied hybrid iterative reconstruction algorithm (ASiR-V). Material and methods Fifty-one patients (29 males) undergoing clinically indicated CCTA from April to December 2021 were prospectively enrolled. Fourteen datasets were reconstructed for each patient: three DLIR strength levels (DLIR_L, DLIR_M, and DLIR_H), ASiR-V from 10% to 100% in 10%-increment, and filtered back-projection (FBP). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) determined objective image quality. Subjective image quality was assessed with a 4-point Likert scale. Concordance between reconstruction algorithms was assessed by Pearson correlation coefficient. Results DLIR algorithm did not impact vascular attenuation (P ≥ 0.374). DLIR_H showed the lowest noise, comparable with ASiR-V 100% (P = 1) and significantly lower than other reconstructions (P ≤ 0.021). DLIR_H achieved the highest objective quality, with SNR and CNR comparable to ASiR-V 100% (P = 0.139 and 0.075, respectively). DLIR_M obtained comparable objective image quality with ASiR-V 80% and 90% (P ≥ 0.281), while achieved the highest subjective image quality (4, IQR: 4–4; P ≤ 0.001). DLIR and ASiR-V datasets returned a very strong correlation in the assessment of CAD (r = 0.874, P = 0.001). Conclusion DLIR_M significantly improves CCTA image quality and has very strong correlation with routinely applied ASiR-V 50% dataset in the diagnosis of CAD.

Coronary Computed Tomography Angiography with Deep Learning Image Reconstruction: A Preliminary Study to Evaluate Radiation Exposure Reduction

“…For this reason, it will be challenging to consider the contribution of each individual algorithm [ 18 ]. As a result of this study, we found that CT automatisms were adjusted with a median noise index of 13.9 for Group B, without the intervention of the operator who carried out the examination.…”

Perfect Match: Radiomics and Artificial Intelligence in Cardiac Imaging

“…2D indicates 2 dimension; 3D, 3 dimension; CT, computed tomography; and MRI, magnetic resonance imaging. image reconstruction can reduce noise in coronary CT angiography compared with an adaptive statistical iterative reconstruction by 19% 12 and reduce radiation dose by 40% while improving signal-to-noise ratio and contrast-to-noise ratio by about 50%. 13 In CMR, DL is increasingly being used to accelerate the inherently slow data sampling process.…”

“…The majority of DL-supported image reconstruction methods proposed for CT aim at deriving images with a higher signal-to-noise ratio compared with standard filtered back projection, thereby allowing reductions in overall radiation dose. It has been shown that DL-based image reconstruction can reduce noise in coronary CT angiography compared with an adaptive statistical iterative reconstruction by 19% 12 and reduce radiation dose by 40% while improving signal-to-noise ratio and contrast-to-noise ratio by about 50%. 13…”

Improved image quality in CT pulmonary angiography using deep learning-based image reconstruction

“…Previously, DLIR has been shown to improve image denoising and increase image quality beyond the levels of achieved with iterative reconstruction techniques in various cardiovascular CT applications including coronary CTA 18 – 23 , CT for planning of transcatheter aortic valve repair 24 , 25 , CT of the aorta 26 , head and neck CT angiography 27 and triple-rule-out CT 28 . There are very limited data about DLIR in CTPA despite this being one of the most commonly performed cardiovascular CT examinations.…”