Chest MRI of patients with COVID-19

Vasilev, Yuriy A.; Сергунова, К. А.; Bazhin, Alexander; Masri, A.G.; Vasileva, Yu N.; Семенов, Д. С.; Kudryavtsev, N. D.; Panina, Olga Yu.; Khoruzhaya, Anna N.; Зінченко, Віктор; Ахмад, Е. С.; Petraikin, A. V.; Vladzymyrskyy, Anton; Midaev, A.V.; Морозов, С. П.

doi:10.1016/j.mri.2021.03.005

Cited by 25 publications

(21 citation statements)

References 11 publications

(6 reference statements)

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“… 40 , 41 In this direction, this is particularly crucial for patients who require dynamic observation and for the examination of children and young people due to the ionizing radiation‐free nature of MR images. 41 MR image synthesis could also be applied to improve the brain tumor structures contouring in radiotherapy treatment planning by generating the missing MRI sequence information. 27 Radiotherapy treatment of brain cancer patients often requires more than one MR sequence for delineating the structures.…”

Section: Discussionmentioning

confidence: 99%

Deep learning‐based convolutional neural network for intramodality brain MRI synthesis

Osman

Tamam

2022

J Applied Clin Med Phys

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Purpose The existence of multicontrast magnetic resonance (MR) images increases the level of clinical information available for the diagnosis and treatment of brain cancer patients. However, acquiring the complete set of multicontrast MR images is not always practically feasible. In this study, we developed a state‐of‐the‐art deep learning convolutional neural network (CNN) for image‐to‐image translation across three standards MRI contrasts for the brain. Methods BRATS’2018 MRI dataset of 477 patients clinically diagnosed with glioma brain cancer was used in this study, with each patient having T1‐weighted (T1), T2‐weighted (T2), and FLAIR contrasts. It was randomly split into 64%, 16%, and 20% as training, validation, and test set, respectively. We developed a U‐Net model to learn the nonlinear mapping of a source image contrast to a target image contrast across three MRI contrasts. The model was trained and validated with 2D paired MR images using a mean‐squared error (MSE) cost function, Adam optimizer with 0.001 learning rate, and 120 epochs with a batch size of 32. The generated synthetic‐MR images were evaluated against the ground‐truth images by computing the MSE, mean absolute error (MAE), peak signal‐to‐noise ratio (PSNR), and structural similarity index (SSIM). Results The generated synthetic‐MR images with our model were nearly indistinguishable from the real images on the testing dataset for all translations, except synthetic FLAIR images had slightly lower quality and exhibited loss of details. The range of average PSNR, MSE, MAE, and SSIM values over the six translations were 29.44–33.25 dB, 0.0005–0.0012, 0.0086–0.0149, and 0.932–0.946, respectively. Our results were as good as the best‐reported results by other deep learning models on BRATS datasets. Conclusions Our U‐Net model exhibited that it can accurately perform image‐to‐image translation across brain MRI contrasts. It could hold great promise for clinical use for improved clinical decision‐making and better diagnosis of brain cancer patients due to the availability of multicontrast MRIs. This approach may be clinically relevant and setting a significant step to efficiently fill a gap of absent MR sequences without additional scanning.

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Section: Discussionmentioning

confidence: 99%

Deep learning‐based convolutional neural network for intramodality brain MRI synthesis

Osman

Tamam

2022

J Applied Clin Med Phys

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“…Ghosh et al suggest CXR as the initial imaging modality in every suspected case of COVID-19 regardless of laboratory status [42]. Finally, several studies show chest MRI to be promising in COVID-19-associated pneumonia diagnosis and severity assessment [46,47], but its limitations include poor availability, longer examination times, and high susceptibility of the MRI equipment to contamination [48].…”

Section: Discussionmentioning

confidence: 99%

Inter-Observer Agreement between Low-Dose and Standard-Dose CT with Soft and Sharp Convolution Kernels in COVID-19 Pneumonia

Blokhin¹,

Gombolevskiy²,

Chernina³

et al. 2022

JCM

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Computed tomography (CT) has been an essential diagnostic tool during the COVID-19 pandemic. The study aimed to develop an optimal CT protocol in terms of safety and reliability. For this, we assessed the inter-observer agreement between CT and low-dose CT (LDCT) with soft and sharp kernels using a semi-quantitative severity scale in a prospective study (Moscow, Russia). Two consecutive scans with CT and LDCT were performed in a single visit. Reading was performed by ten radiologists with 3-25 years’ experience. The study included 230 patients, and statistical analysis showed LDCT with a sharp kernel as the most reliable protocol (percentage agreement 74.35 ± 43.77%), but its advantage was marginal. There was no significant correlation between radiologists’ experience and average percentage agreement for all four evaluated protocols. Regarding the radiation exposure, CTDIvol was 3.6 ± 0.64 times lower for LDCT. In conclusion, CT and LDCT with soft and sharp reconstructions are equally reliable for COVID-19 reporting using the “CT 0-4” scale. The LDCT protocol allows for a significant decrease in radiation exposure but may be restricted by body mass index.

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“…RNNs were originally designed for the analysis of discrete sequence of data [ 42 ]. It will be seen of a generalisation of Multilayer perceptron since both input, output could be of various lengths, making them appropriate for applications like machine translation, where the input and output are a phrase from the source and target languages, respectively [ 43 ]. The model learns a distribution across classes from a series instead of only one vector in a classification context [ 44 ].…”

Section: Deep Modelsmentioning

confidence: 99%

CVD-HNet: Classifying Pneumonia and COVID-19 in Chest X-ray Images Using Deep Network

Suganyadevi

Seethalakshmi

2022

Wireless Pers Commun

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The use of computer-assisted analysis to improve image interpretation has been a long-standing challenge in the medical imaging industry. In terms of image comprehension, Continuous advances in AI (Artificial Intelligence), predominantly in DL (Deep Learning) techniques, are supporting in the classification, Detection, and quantification of anomalies in medical images. DL techniques are the most rapidly evolving branch of AI, and it’s recently been successfully pragmatic in a variety of fields, including medicine. This paper provides a classification method for COVID 19 infected X-ray images based on new novel deep CNN model. For COVID19 specified pneumonia analysis, two new customized CNN architectures, CVD-HNet1 (COVID-HybridNetwork1) and CVD-HNet2 (COVID-HybridNetwork2), have been designed. The suggested method utilizes operations based on boundaries and regions, as well as convolution processes, in a systematic manner. In comparison to existing CNNs, the suggested classification method achieves excellent Accuracy 98 percent, F Score 0.99 and MCC 0.97. These results indicate impressive classification accuracy on a limited dataset, with more training examples, much better results can be achieved. Overall, our CVD-HNet model could be a useful tool for radiologists in diagnosing and detecting COVID 19 instances early.

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Chest MRI of patients with COVID-19

Cited by 25 publications

References 11 publications

Deep learning‐based convolutional neural network for intramodality brain MRI synthesis

Deep learning‐based convolutional neural network for intramodality brain MRI synthesis

Inter-Observer Agreement between Low-Dose and Standard-Dose CT with Soft and Sharp Convolution Kernels in COVID-19 Pneumonia

CVD-HNet: Classifying Pneumonia and COVID-19 in Chest X-ray Images Using Deep Network

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