Combination of Deep Learning–Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation

Hata, Akinori; Yanagawa, Masahiro; Yoshida, Yuki; Miyata, Tomo; Tsubamoto, Mitsuko; Honda, Osamu; Tomiyama, Noriyuki

doi:10.2214/ajr.19.22680

Cited by 33 publications

(38 citation statements)

References 28 publications

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“…Previous studies have described similar results with higher image quality enhancement potentials for wFBP than ADMIRE reconstructions. Hata et al, for example, described relatively smaller image quality improvements for model-based iterative reconstruction input images than for wFBP images when using denoising algorithms [ 27 ]. In conjunction with the results of previous studies, they argued wFBP images have a greater room for improvement than iteratively reconstructed images [ 28 ].…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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“…Hata et al found that TFI was associated with significantly less noise, a higher SNR/CNR, and finer image texture than ASiR-V [ 9 ]. They also demonstrated that the combination of deep learning–based denoising and IR improved image quality and Lung Imaging Reporting and Data System (Lung-RADS) evaluation on ultra–low-dose CT [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

“…Recently, several studies have revealed improved image quality—for all organs and body parts—achieved with deep learning–based image reconstruction relative to that achieved with IR of CT scans [ 6 – 18 ]. These studies have shown deep learning–based reconstruction (TrueFidelity [TFI]) to be superior to IR (adaptive statistical IR, ASiR-V) in terms of image noise and sharpness [ 6 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Accuracy of two deep learning–based reconstruction methods compared with an adaptive statistical iterative reconstruction method for solid and ground-glass nodule volumetry on low-dose and ultra–low-dose chest computed tomography: A phantom study

et al. 2022

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No published studies have evaluated the accuracy of volumetric measurement of solid nodules and ground-glass nodules on low-dose or ultra–low-dose chest computed tomography, reconstructed using deep learning–based algorithms. This is an important issue in lung cancer screening. Our study aimed to investigate the accuracy of semiautomatic volume measurement of solid nodules and ground-glass nodules, using two deep learning–based image reconstruction algorithms (Truefidelity and ClariCT.AI), compared with iterative reconstruction (ASiR-V) in low-dose and ultra–low-dose settings. We performed computed tomography scans of solid nodules and ground-glass nodules of different diameters placed in a phantom at four radiation doses (120 kVp/220 mA, 120 kVp/90 mA, 120 kVp/40 mA, and 80 kVp/40 mA). Each scan was reconstructed using Truefidelity, ClariCT.AI, and ASiR-V. The solid nodule and ground-glass nodule volumes were measured semiautomatically. The gold-standard volumes could be calculated using the diameter since all nodule phantoms are perfectly spherical. Subsequently, absolute percentage measurement errors of the measured volumes were calculated. Image noise was also calculated. Across all nodules at all dose settings, the absolute percentage measurement errors of Truefidelity and ClariCT.AI were less than 11%; they were significantly lower with Truefidelity or ClariCT.AI than with ASiR-V (all P<0.05). The absolute percentage measurement errors for the smallest solid nodule (3 mm) reconstructed by Truefidelity or ClariCT.AI at all dose settings were significantly lower than those of this nodule reconstructed by ASiR-V (all P<0.05). Furthermore, the lowest absolute percentage measurement errors for ground-glass nodules were observed with Truefidelity or ClariCT.AI at all dose settings. The absolute percentage measurement errors for ground-glass nodules reconstructed with Truefidelity at ultra–low-dose settings were significantly lower than those of all sizes of ground-glass nodules reconstructed with ASiR-V (all P<0.05). Image noise was lowest with Truefidelity (all P<0.05). In conclusion, the deep learning–based algorithms were more accurate for volume measurements of both solid nodules and ground-glass nodules than ASiR-V at both low-dose and ultra–low-dose settings.

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“…According to several comparisons and phantom studies [107,[165][166][167][168][169][170][171][172][173][174][175][176][177][178][179], DL-based image reconstruction is superior to other conventional reconstruction techniques for image quality and lesion detection.…”

Section: Effects Of Using Deep Learning Methodsmentioning

confidence: 99%

The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review

Zhang

Yang

Shi

2022

Complex Intell. Syst.

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Conventional reconstruction techniques, such as filtered back projection (FBP) and iterative reconstruction (IR), which have been utilised widely in the image reconstruction process of computed tomography (CT) are not suitable in the case of low-dose CT applications, because of the unsatisfying quality of the reconstructed image and inefficient reconstruction time. Therefore, as the demand for CT radiation dose reduction continues to increase, the use of artificial intelligence (AI) in image reconstruction has become a trend that attracts more and more attention. This systematic review examined various deep learning methods to determine their characteristics, availability, intended use and expected outputs concerning low-dose CT image reconstruction. Utilising the methodology of Kitchenham and Charter, we performed a systematic search of the literature from 2016 to 2021 in Springer, Science Direct, arXiv, PubMed, ACM, IEEE, and Scopus. This review showed that algorithms using deep learning technology are superior to traditional IR methods in noise suppression, artifact reduction and structure preservation, in terms of improving the image quality of low-dose reconstructed images. In conclusion, we provided an overview of the use of deep learning approaches in low-dose CT image reconstruction together with their benefits, limitations, and opportunities for improvement.

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Combination of Deep Learning–Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation

Cited by 33 publications

References 28 publications

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

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

Accuracy of two deep learning–based reconstruction methods compared with an adaptive statistical iterative reconstruction method for solid and ground-glass nodule volumetry on low-dose and ultra–low-dose chest computed tomography: A phantom study

The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review

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