Image quality comparison of two adaptive statistical iterative reconstruction (ASiR, ASiR-V) algorithms and filtered back projection in routine liver CT

Chen, Lihong; Jin, Chao; Li, Jianying; Wang, Geliang; Jia, Yongjun; Duan, Haifeng; Pan, Ning; Guo, Jianxin

doi:10.1259/bjr.20170655

Cited by 41 publications

(25 citation statements)

References 16 publications

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“…Images were reconstructed using a new DL reconstruction TrueFidelity (TF) algorithm at low, medium and high strength, and a partial model-based iterative reconstruction (adaptive statistical iterative reconstruction-V, ASiR-V) at 60 % strength [19]. According to previous studies, ASiR-V 60 % was a superior algorithm compared to FBP, ASIR and other ASiR-V levels [34][35][36]. Based on this results and after a dedicated institutional optimization procedure, it was decided that 60 % ASiR-V was used in clinical routine for abdominal acquisitions.…”

Section: Experimental Design Image Acquisition and Reconstruction Protocolmentioning

confidence: 99%

Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compared to partial model-based iterative reconstruction

Racine

Brat²,

Dufour³

et al. 2021

European Journal of Radiology

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To compare deep learning (True Fidelity, TF) and partial model based Iterative Reconstruction (ASiR-V) algorithm for image texture, low contrast lesion detectability and potential dose reduction. Methods: Anthropomorphic phantoms (mimicking non-overweight and overweight patient), containing lesions of 6 mm in diameter with 20HU contrast, were scanned at five different dose levels (2,6,10,15,20 mGy) on a CT system, using clinical routine protocols for liver lesion detection. Images were reconstructed using ASiR-V 0% (surrogate for FBP), 60 % and TF at low, medium and high strength. Noise texture was characterized by computing a normalized Noise Power Spectrum filtered by an eye filter. The similarity against FBP texture was evaluated using peak frequency difference (PFD) and root mean square deviation (RMSD). Low contrast detectability was assessed using a channelized Hotelling observer and the area under the ROC curve (AUC) was used as figure of merit. Potential dose reduction was calculated to obtain the same AUC for TF and ASiR-V. Results: FBP-like noise texture was more preserved with TF (PFD from -0.043mm-1 to -0.09mm-1, RMSD from 0.12mm-1 to 0.21mm-1) than with ASiR-V (PFD equal to 0.12 mm-1, RMSD equal to 0.53mm-1), resulting in a sharper image. AUC was always higher with TF than ASIR-V. In average, TF compared to ASiR-V, enabled a radiation dose reduction potential of 7%, 25 % and 33 % for low, medium and high strength respectively. Conclusion: Compared to ASIR-V, TF at high strength does not impact noise texture and maintains low contrast liver lesions detectability at significant lower dose.

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Section: Experimental Design Image Acquisition and Reconstruction Protocolmentioning

confidence: 99%

Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compared to partial model-based iterative reconstruction

Racine

Brat²,

Dufour³

et al. 2021

European Journal of Radiology

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“…[ 25 ] In the case of ASIR-V, blending with the traditional FBP was performed in 10% increments according to the user preference. [ 8 ] In previous reports comparing IR of different levels, [ 16 , 26 ] higher levels of IR induced exaggerated noise reduction, leading to excessive image smoothing or unnatural images compared with the FBP, which radiologists favor and are used to. Consequently, high level IR algorithms have limited usage in clinical practice.…”

Section: Discussionmentioning

confidence: 99%

“…The DLIR can be done in 3 selectable strength levels (low, medium, and high), which varies in the degree of noise. [ 16 ] To the best of our knowledge, the effect of DLIR algorithms in abdominal CTs at different tube voltages and tube currents has yet to be evaluated. This is important because it may help optimize radiation doses in abdominal CT examinations which have the highest radiation dose compared with other types of CT scans.…”

Section: Introductionmentioning

confidence: 99%

The potential for reduced radiation dose from deep learning-based CT image reconstruction

et al. 2021

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The purpose of this phantom study is to compare radiation dose and image quality of abdominal computed tomography (CT) scanned with different tube voltages and tube currents, reconstructed with filtered back projection (FBP), hybrid iterative reconstruction (IR) and deep learning image reconstruction (DLIR) algorithms. A total of 15 CT scans of whole body phantoms were taken with 3 different tube voltages and 5 different tube currents. The images were reconstructed with FBP, 30% and 50% hybrid IR adaptive statistical iterative reconstruction (ASIR-V), and low, medium and high strength DLIR algorithms. The image scanned with tube voltage/tube current of 120 kV/ 200 mA and reconstructed with FBP algorithm was chosen as the reference image. Five radiologists independently analyzed the images individually and also compared it with the reference image in overall, using the visual grading analysis. The mean score of each image was calculated and compared. Using DLIR algorithms, the radiation dose was reduced by 65.5% to 68.1% compared with the dose used in the reference image, while maintaining comparable image quality. Using the DLIR algorithm of medium strength, the image quality was even better than the reference image with a reduced radiation dose up to 36.2% to 50.0%. The DLIR algorithms generated better quality images than ASIR-V algorithms in all the data sets. In addition, among the data sets reconstructed with DLIR algorithms, image quality was the best at the medium strength level, followed by low and high. This phantom study suggests that DLIR algorithms may be considered as a new reconstruction technique by reducing radiation dose while maintaining the image quality of abdominal CTs.

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“…Post-ASiR-V can significantly reduce the image noise and streak artifacts to improve image detail and quality. Previous studies have reported that post-ASiR-V with a higher percentage results in an improved image over smooth images, which affect the detection of small lesions [28][29][30] . Choosing an appropriate percentage of post-ASiR-V is therefore a key to maximizing image quality.…”

Section: Discussionmentioning

confidence: 99%

Comparison of image quality and radiation dose using different pre-ASiR-V and post-ASiR-V levels in coronary computed tomography angiography

Zhao¹,

Li²,

Geng³

et al. 2020

Preprint

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Objective: To characterize the optimal pre-(ASiR-V and post-ASiR-V for improving image quality and reducing the radiation dose in CCTA. Methods: The study was divided into two parts. In part I, 150 patients for CCTA were prospectively enrolled and randomly divided into five groups (A, B, C, D, and E), with progressive scanning from 40% to 80% pre-ASiR-V with 10% intervals and reconstructing with 70% post-ASiR-V. The SNR and CNR were calculated. Subjective image quality was assessed using a five-point scale. The radiation dose was recorded and calculated after statistical analysis by optimizing for the best pre-ASiR-V value with the lowest radiation dose while maintaining overall image quality. In part II, the images were reconstructed with the recommended optimal pre-ASiR-V values in part I (D group) was divided into six subgroups (D0-D5 with 40%-90% post-ASiR-V and interval 10%, respectively.).The SNR and CNR of D0-D5 subgroups were calculated and analyzed using one-way analysis of variance, and the consistency of the subjective scores used the k test. Results: There was no significant difference in the SNRs, CNRs, and image quality scores among A, B, C, and D groups. The SNR, CNR, and image quality scores of the E group were lower than those of the A, B, C, and D groups. The mean EDs in the B, C, and D groups were reduced by 7.01%, 13.37%, and 18.87%, respectively, when compared with that of the A group. The SNR and CNR of the D4-D5 subgroups were higher than the D0-D3 subgroups, and the image quality scores of the D4 subgroups were higher than the other subgroups. Conclusion: The wide-detector combined with 70% pre-ASiR-V and 80% post-ASiR-V significantly reduced the radiation dose of CCTA while maintaining overall image quality compared with the manufacture's recommendation.

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Image quality comparison of two adaptive statistical iterative reconstruction (ASiR, ASiR-V) algorithms and filtered back projection in routine liver CT

Cited by 41 publications

References 16 publications

Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compared to partial model-based iterative reconstruction

Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compared to partial model-based iterative reconstruction

The potential for reduced radiation dose from deep learning-based CT image reconstruction

Comparison of image quality and radiation dose using different pre-ASiR-V and post-ASiR-V levels in coronary computed tomography angiography

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