Dose Reduction While Preserving Diagnostic Quality in Head CT: Advancing the Application of Iterative Reconstruction Using a Live Animal Model

Raslau, Flavius D.; Escott, Edward J.; Smiley, Jeffrey R.; Adams, Chad; Feigal, David W.; Ganesh, Halemane; Wang, Chi; Zhang, Jie

doi:10.3174/ajnr.a6258

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…hybrid and MBIR algorithms have previously shown potential for dose reductions in head CT. 16,[24][25][26][27] This lack in technically revised data acquisitions is not only due to the retrospective character of this study, but also because of ethical considerations. Correct ASPECTS rating has far-reaching consequences and outweigh radiation protection in the acute emergency setting.…”

Section: Discussionmentioning

confidence: 99%

“…Upcoming studies in larger cohorts may confirm our present findings. Furthermore, we did not incorporate dose reductions into the routine CT acquisition protocol, although hybrid and MBIR algorithms have previously shown potential for dose reductions in head CT 16,24–27 . This lack in technically revised data acquisitions is not only due to the retrospective character of this study, but also because of ethical considerations.…”

Section: Discussionmentioning

confidence: 99%

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Improved Reliability of Automated ASPECTS Evaluation Using Iterative Model Reconstruction from Head CT Scans

Löffler

Sollmann

Mönch

et al. 2021

Journal of Neuroimaging

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Iterative model reconstruction (IMR) has shown to improve computed tomography (CT) image quality compared to hybrid iterative reconstruction (HIR). Alberta Stroke Program Early CT Score (ASPECTS) assessment in early stroke is particularly dependent on high-image quality. Purpose of this study was to investigate the reliability of ASPECTS assessed by humans and software based on HIR and IMR, respectively. METHODS: Forty-seven consecutive patients with acute anterior circulation large vessel occlusions (LVOs) and successful endovascular thrombectomy were included. ASPECTS was assessed by three neuroradiologists (one attending, two residents) and by automated software in noncontrast axial CT with HIR (iDose4; 5 mm) and IMR (5 and 0.9 mm). Two expert neuroradiologists determined consensus ASPECTS reading using all available image data including MRI. Agreement between four raters (three humans, one software) and consensus were compared using square-weighted kappa (κ). RESULTS: Human raters achieved moderate to almost perfect agreement (κ = .557-.845) with consensus reading. The attending showed almost perfect agreement for 5 mm HIR (κ HIR = .845), while residents had mostly substantial agreements without clear trends across reconstructions. Software had substantial to almost perfect agreement with consensus, increasing with IMR 5 and 0.9 mm slice thickness (κ HIR = .751, κ IMR = .777, and κ IMR0.9 = .814). Agreements inversely declined for these reconstructions for the attending (κ HIR = .845, κ IMR = .763, and κ IMR0.9 = .681). CONCLUSIONS: Human and software rating showed good reliability of ASPECTS across different CT reconstructions. Human raters performed best with the reconstruction algorithms they had most experience with (HIR for the attending). Automated software benefits from higher resolution with better contrasts in IMR with 0.9 mm slice thickness.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Improved Reliability of Automated ASPECTS Evaluation Using Iterative Model Reconstruction from Head CT Scans

Löffler

Sollmann

Mönch

et al. 2021

Journal of Neuroimaging

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“…It explains that there are differences in CT values of brain regions of different ages (Yates TS, et al, 2021). The change of CT value can reflect the change of fluid in tissue, mainly the change of blood volume (Raslau FD, et al, 2019; Shi D, et al, 2020). Crucially, obtaining accurate baseline values, for the application of new innovative techniques in both experimental and clinical studies.…”

Section: Discussionmentioning

confidence: 99%

CT imaging character of different brain regions in different ages of Diannan small‐ear pigs

Zhou

Niu

Zhou

et al. 2021

Ibrain

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Objective: To collect and establish normal data of the brain regions of Diannan small-ear (DSE pigs, basically throughout measuring and comparing computed tomography values of the barin. Methods: 12 ordinary DSE pigs were divided into juvenile, adult, old groups based on the physiological ages (n=4) A SOMATOM Definition AS 64-row 128-slice 4D spiral CT scanner (SOMATOM Definition AS, Germany) was used to collect CT images of DSE pigs, record and analyze the scanning data. Results: Compared with the juvenile group, the CT values of the right frontal lobe, right parietal lobe, right temporal lobe, left temporal lobe, and right occipital lobe were signiicantly higher in the elderly group. Compared with the adult group, the CT values of the right frontal lobe, left frontal lobe, right parietal lobe, right temporal lobe, left temporal lobe, and right occipital lobe of the elderly group were signiicantly higher. Conclusion:The results of the study can be used to evaluate the changes in CT values of various brain regions in piglets of different ages and future pig head injury models or other studies that require CT-based analysis.

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Deep learning versus iterative reconstruction on image quality and dose reduction in abdominal CT: a live animal study

Zhang¹,

Ganesh²,

Raslau³

et al. 2022

Phys. Med. Biol.

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Objective: While simulated low-dose CT images and phantom studies cannot fully approximate subjective and objective effects of deep learning (DL) denoising on image quality, live animal models may afford this assessment. This study is to investigate the potential of DL in CT dose reduction on image quality compared to iterative reconstruction (IR). Approach: The upper abdomen of a live 4-year-old sheep was scanned on a CT scanner at different exposure levels. Images were reconstructed using FBP and ADMIRE with 5 strengths. A modularized DL network with 5 modules was used for image reconstruction via progressive denoising. Radiomic features were extracted from a region over the liver. Concordance correlation coefficient (CCC) was applied to quantify agreement between any two sets of radiomic features. Coefficient of Variation (CV) was calculated to measure variation in a radiomic feature series. Structural similarity index (SSIM) was used to measure the similarity between any two images. Diagnostic quality, low-contrast detectability, and image texture were qualitatively evaluated by two radiologists. Pearson correlation coefficient was computed across all dose-reconstruction/denoising combinations. Results: A total of 66 image sets, with 405 radiomic features extracted from each, are analyzed. IR and DL can improve diagnostic quality and low-contrast detectability and similarly modulate image texture features. In terms of SSIM, DL has higher potential in preserving image structure. There is strong correlation between SSIM and radiologists’ evaluations for diagnostic quality (0.559) and low-contrast detectability (0.635) but moderate correlation for texture (0.313). There is moderate correlation between CCC of radiomic features and radiologists’ evaluation for diagnostic quality (0.397), low-contrast detectability (0.417), and texture (0.326), implying that improvement of image features may not relate to improvement of diagnostic quality. Conclusion: DL shows potential to further reduce radiation dose while preserving structural similarity, while IR is favored by radiologists and more predictably alters radiomic features.

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Dose Reduction While Preserving Diagnostic Quality in Head CT: Advancing the Application of Iterative Reconstruction Using a Live Animal Model

Cited by 3 publications

References 33 publications

Improved Reliability of Automated ASPECTS Evaluation Using Iterative Model Reconstruction from Head CT Scans

Improved Reliability of Automated ASPECTS Evaluation Using Iterative Model Reconstruction from Head CT Scans

CT imaging character of different brain regions in different ages of Diannan small‐ear pigs

Deep learning versus iterative reconstruction on image quality and dose reduction in abdominal CT: a live animal study

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