Deep-learning image-reconstruction algorithm for dual-energy CT angiography with reduced iodine dose: preliminary results

Noda, Yoshifumi; Nakamura, Fumihiko; Kawamura, Tomotaka; Kawai, Nobuyuki; Kaga, Tetsuro; Miyoshi, Toshiharu; Kato, Hiroki; Hyodo, Fuminori

doi:10.1016/j.crad.2021.10.014

Cited by 17 publications

(14 citation statements)

References 19 publications

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“…Recently, aortic DECTA with a reduced iodine dose protocol had demonstrated clinical utility of DLIR algorithm in DECTA [ 19 ]. In this study, we further evaluated the utility of DLIR algorithm in head and neck DECTA at 50 keV by image objective parameters and subjective evaluation including general image quality in terms of image noise, image texture, depiction of arteries at different levels, and diagnostic performance with conventional ASIR-V. Our results indicated that DLIR-H algorithm could reduce carotid DECTA image noise and improving image quality while maintaining similar arterial depiction and diagnostic performance, compared to 80% ASIR-V.…”

Section: Discussionmentioning

confidence: 99%

“…Firstly, 80% ASIR-V is the routine image reconstruction algorithm for GE Healthcare CT system at our organization. Secondly, ASIR-V with blending factor of 40% was used for comparison in all former DLIR algorithms of DECT study [ 18 , 19 ] and the comparison of image texture was also omitted, which makes further comparison between ASIR-V with high blending factor and DLIR algorithm necessary.…”

Section: Discussionmentioning

confidence: 99%

“…DLIR allows emulating standard-dose FBP image texture while providing low image noise, artifact suppression, and highly sensitive detectability with high resolution [ 12 – 16 ]. Its utility has been demonstrated in both single-energy CT and dual-energy CT scans until most recently [ 17 – 19 ]; however, its adaption to dual-energy CTA (DECTA) has not been evaluated yet.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning image reconstruction algorithm for carotid dual-energy computed tomography angiography: evaluation of image quality and diagnostic performance

et al. 2022

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Objectives To evaluate image quality and diagnostic performance of carotid dual-energy computed tomography angiography (DECTA) using deep learning image reconstruction (DLIR) compared with images using adaptive statistical iterative reconstruction-Veo (ASIR-V). Methods Carotid DECTA datasets of 28 consecutive patients were reconstructed at 50 keV using DLIR at low, medium, and high levels (DLIR-L, DLIR-M, and DLIR-H) and 80% ASIR-V algorithms. Mean attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) at different levels of arteries were measured and calculated. Image quality for noise and texture, depiction of arteries, and diagnostic performance toward carotid plaques were assessed subjectively by two radiologists. Quantitative and qualitative parameters were compared between the ASIR-V, DLIR-L, DLIR-M, and DLIR-H groups. Results The image noise at aorta and common carotid artery, SNR, and CNR at all level arteries of DLIR-H images were significantly higher than those of ASIR-V images (p = 0.000–0.040). The quantitative analysis of DLIR-L and DLIR-M showed comparable denoise capability with ASIR-V. The overall image quality (p = 0.000) and image noise (p = 0.000–0.014) were significantly better in the DLIR-M and DLIR-H images. The image texture was improved by DLR at all level compared to ASIR-V images (p = 0.000–0.008). Depictions of head and neck arteries and diagnostic performance were comparable between four groups (p > 0.05). Conclusions Compared with 80% ASIR-V, we recommend DLIR-H for clinical carotid DECTA reconstruction, which can significantly improve the image quality of carotid DECTA at 50 keV but maintain a desirable diagnostic performance and arterial depiction.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning image reconstruction algorithm for carotid dual-energy computed tomography angiography: evaluation of image quality and diagnostic performance

et al. 2022

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“…The limits of conventional reconstruction methods for image quality enhancements in low-dose computed tomography have previously been explored [ 11 ]. More recently, however, the advent of AI-based post-processing denoising solutions show promising results for further image quality enhancement [ 12 , 13 , 14 ]. However, as conventional reconstruction methods, novel AI-based techniques have specific characteristics and caveats essential to consider, such as reduced spatial information, blurring, and possible loss of information [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

AI Denoising Significantly Improves Image Quality in Whole-Body Low-Dose Computed Tomography Staging

et al. 2022

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(1) Background: To evaluate the effects of an AI-based denoising post-processing software solution in low-dose whole-body computer tomography (WBCT) stagings; (2) Methods: From 1 January 2019 to 1 January 2021, we retrospectively included biometrically matching melanoma patients with clinically indicated WBCT staging from two scanners. The scans were reconstructed using weighted filtered back-projection (wFBP) and Advanced Modeled Iterative Reconstruction strength 2 (ADMIRE 2) at 100% and simulated 50%, 40%, and 30% radiation doses. Each dataset was post-processed using a novel denoising software solution. Five blinded radiologists independently scored subjective image quality twice with 6 weeks between readings. Inter-rater agreement and intra-rater reliability were determined with an intraclass correlation coefficient (ICC). An adequately corrected mixed-effects analysis was used to compare objective and subjective image quality. Multiple linear regression measured the contribution of “Radiation Dose”, “Scanner”, “Mode”, “Rater”, and “Timepoint” to image quality. Consistent regions of interest (ROI) measured noise for objective image quality; (3) Results: With good–excellent inter-rater agreement and intra-rater reliability (Timepoint 1: ICC ≥ 0.82, 95% CI 0.74–0.88; Timepoint 2: ICC ≥ 0.86, 95% CI 0.80–0.91; Timepoint 1 vs. 2: ICC ≥ 0.84, 95% CI 0.78–0.90; all p ≤ 0.001), subjective image quality deteriorated significantly below 100% for wFBP and ADMIRE 2 but remained good–excellent for the post-processed images, regardless of input (p ≤ 0.002). In regression analysis, significant increases in subjective image quality were only observed for higher radiation doses (≥0.78, 95%CI 0.63–0.93; p < 0.001), as well as for the post-processed images (≥2.88, 95%CI 2.72–3.03, p < 0.001). All post-processed images had significantly lower image noise than their standard counterparts (p < 0.001), with no differences between the post-processed images themselves. (4) Conclusions: The investigated AI post-processing software solution produces diagnostic images as low as 30% of the initial radiation dose (3.13 ± 0.75 mSv), regardless of scanner type or reconstruction method. Therefore, it might help limit patient radiation exposure, especially in the setting of repeated whole-body staging examinations.

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“…Daisuke Kawahara et al used deep conditional generative adversarial networks to synthesize material decomposition images [17] . Y. Noda et al proposed a deep-learning algorithm for dual-energy CT angiography to reduce iodine doses [18] . Wei Fang et al used an image-domain deep learning framework to calculate effective atom number images with high-dual-energy CT [19] .…”

Section: Introductionmentioning

confidence: 99%

A multienergy computed tomography method without image segmentation or prior knowledge of X-ray spectra or materials

Wei

Chen²,

Liu³

et al. 2022

Heliyon

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Deep-learning image-reconstruction algorithm for dual-energy CT angiography with reduced iodine dose: preliminary results

Cited by 17 publications

References 19 publications

Deep learning image reconstruction algorithm for carotid dual-energy computed tomography angiography: evaluation of image quality and diagnostic performance

Deep learning image reconstruction algorithm for carotid dual-energy computed tomography angiography: evaluation of image quality and diagnostic performance

AI Denoising Significantly Improves Image Quality in Whole-Body Low-Dose Computed Tomography Staging

A multienergy computed tomography method without image segmentation or prior knowledge of X-ray spectra or materials

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