The eventual rejection of the linear no‐threshold theory will lead to a drastic reduction in the demand for diagnostic medical physics services

Jones, A. Kyle; O’Connor, Michael K.; Zaidi, Habib

doi:10.1002/mp.13619

Cited by 1 publication

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“…Despite the controversies and heated debates around the potential haphazardous effects of low levels of ionizing radiation and the linear-no-threshold theory [ 29 ], concerns from radiation exposure are still current [ 30 ]. Since CT imaging is widely used in clinical diagnosis, prognosis, and assessment of response to treatment and follow-up of a number of diseases, it is an incremental source of radiation dose to patients in modern healthcare [ 7 , 31 ].…”

Section: Discussionmentioning

confidence: 99%

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Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network

et al. 2020

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Objectives The current study aimed to design an ultra-low-dose CT examination protocol using a deep learning approach suitable for clinical diagnosis of COVID-19 patients. Methods In this study, 800, 170, and 171 pairs of ultra-low-dose and full-dose CT images were used as input/output as training, test, and external validation set, respectively, to implement the full-dose prediction technique. A residual convolutional neural network was applied to generate full-dose from ultra-low-dose CT images. The quality of predicted CT images was assessed using root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Scores ranging from 1 to 5 were assigned reflecting subjective assessment of image quality and related COVID-19 features, including ground glass opacities (GGO), crazy paving (CP), consolidation (CS), nodular infiltrates (NI), bronchovascular thickening (BVT), and pleural effusion (PE). Results The radiation dose in terms of CT dose index (CTDI vol ) was reduced by up to 89%. The RMSE decreased from 0.16 ± 0.05 to 0.09 ± 0.02 and from 0.16 ± 0.06 to 0.08 ± 0.02 for the predicted compared with ultra-low-dose CT images in the test and external validation set, respectively. The overall scoring assigned by radiologists showed an acceptance rate of 4.72 ± 0.57 out of 5 for reference full-dose CT images, while ultra-low-dose CT images rated 2.78 ± 0.9. The predicted CT images using the deep learning algorithm achieved a score of 4.42 ± 0.8. Conclusions The results demonstrated that the deep learning algorithm is capable of predicting standard full-dose CT images with acceptable quality for the clinical diagnosis of COVID-19 positive patients with substantial radiation dose reduction. Key Points • Ultra-low-dose CT imaging of COVID-19 patients would result in the loss of critical information about lesion types, which could potentially affect clinical diagnosis. • Deep learning–based prediction of full-dose from ultra-low-dose CT images for the diagnosis of COVID-19 could reduce the radiation dose by up to 89%. • Deep learning algorithms failed to recover the correct lesion structure/density for a number of patients considered outliers, and as such, further research and development is warranted to address these limitations. Electronic supplementary material The online version of this article (10.1007/s00330-020-07225-6) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

“…Despite the controversies and heated debates around the potential haphazardous effects of low levels of ionizing radiation and the linear-no-threshold theory [29], concerns from…”

Section: Discussionmentioning

confidence: 99%