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In December 2019, Wuhan, China, reported the first COVID-19 caused by SARS-CoV-2
.
As of March 2021, more than 118 million people have been infected with COVID-19, of which 2.
COVID-19 infection screening diagnosis and treatment
In predicting the progression of the COVID-19 disease, clearly specifying and early identifying the areas that will be affected by pneumonia remains a huge challenge
.
Recently, the journal Eur J Nucl Med Mol Imaging published a joint research paper by Huazhong University of Science and Technology and the State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics.
The study intends to use CT ventilation imaging (CTVI) to predict the progression of early lung lesions in patients with COVID-19 Situation
.
However, the conventional CTVI method is to perform two CT scans on the same day, while the CT scans of COVID-19 patients are usually performed at different times
Recently, the journal Eur J Nucl Med Mol Imaging published a joint research paper by Huazhong University of Science and Technology and the State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics.
The concept of predicting the progression of lung lesions in COVID-19 patients
.
First, use the extended CTVI method to generate c lung ventilation images using a CT1 and b CT2 images
The concept of predicting the progression of lung lesions in COVID-19 patients
CT1 scan, CT2 scan, CT3 scan, as well as the prediction atlas of 3 typical COVID-19 patients
.
The prediction map of the first patient (female, 39 years old) mainly contains P1 voxel (blue)
CT1 scan, CT2 scan, CT3 scan, as well as the prediction atlas of 3 typical COVID-19 patients
The predicted location and size of the lesion are almost the same as the real lesion displayed on the third CT.
Quantitatively analyze the accuracy of the forecast results
.
a COVID-19 patient's true lesion volume and maximum lesion slice predicted lesion volume grouping results
Quantitatively analyze the accuracy of the forecast results
The relationship between CT3-CT2 scan time interval and relative error of lesion volume, and the relationship between CT3-CT2 scan time interval and relative error of lesion increase
.
.
In summary, the study used lung ventilation maps based on two chest CT scans to predict the progression of early lung lesions in patients with COVID-19
.
The prediction map generated by the ventilation map can divide lung voxels into three types according to regional function and tissue changes, and then predict potential lesions
.
Therefore, this method can help doctors predict and visualize the progression of lung lesions in COVID-19 patients, enabling them to clearly explain and quickly determine the area affected by pneumonia
.
.
The prediction map generated by the ventilation map can divide lung voxels into three types according to regional function and tissue changes, and then predict potential lesions
.
Therefore, this method can help doctors predict and visualize the progression of lung lesions in COVID-19 patients, enabling them to clearly explain and quickly determine the area affected by pneumonia
.
The study used lung ventilation maps based on two chest CT scans to predict the progression of early lung lesions in patients with COVID-19
.
The prediction map generated by the ventilation map can divide lung voxels into three types according to regional function and tissue changes, and then predict potential lesions
.
Therefore, this method can help doctors predict and visualize the progression of lung lesions in COVID-19 patients, enabling them to clearly explain and quickly determine the area affected by pneumonia
.
Original source
Wang, C.
, Huang, L.
, Xiao, S.
et al.
Early prediction of lung lesion progression in COVID-19 patients with extended CT ventilation imaging.
Eur J Nucl Med Mol Imaging (2021).
https://doi.
org/10.
1007/s00259-021-05435-8
, Huang, L.
, Xiao, S.
et al.
Early prediction of lung lesion progression in COVID-19 patients with extended CT ventilation imaging.
Eur J Nucl Med Mol Imaging (2021).
https://doi.
org/10.
1007/s00259-021-05435-8 Wang, C.
, Huang, L.
, Xiao, S.
et al.
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