Lung CT Scans of COVID-19 Patients Can Predict Neurological Problems Revealed Later by Brain MRIs

For the first time, a multicenter study has found a visual correlation between the severity of the disease in the lungs using CT scans and the severity of effects on patient’s brains, using MRI scans.

The results of the study led by University of Cincinnati (Cincinnati, OH, USA) show that by looking at lung CT scans of patients diagnosed with COVID-19, physicians may be able to predict just how badly they can experience other neurological problems that could show up on brain MRIs, helping improve patient outcomes and identify symptoms for earlier treatment. CT imaging can detect illness in the lungs better than an MRI, another medical imaging technique. However, MRI can detect many problems in the brain, particularly in COVID-19 patients, that cannot be detected on CT images.


In the study, researchers reviewed electronic medical records and images of hospitalized COVID-19 patients. Patients who were diagnosed with COVID-19, experienced neurological issues and who had both lung and brain images available were included. Out of 135 COVID-19 patients with abnormal CT lung scans and neurological symptoms, 49, or 36%, were also found to develop abnormal brain scans and were more likely to experience stroke symptoms.

The researchers believe that the study will help physicians classify patients, based on the severity of disease found on their CT scans, into groups more likely to develop brain imaging abnormalities. Additionally, the correlation could be important for implementing therapies, particularly in stroke prevention, to improve outcomes in patients with COVID-19.

“These results are important because they further show that severe lung disease from COVID-19 could mean serious brain complications, and we have the imaging to help prove it,” said Abdelkader Mahammedi, MD, assistant professor of radiology, UC Health radiologist and member of the UC Gardner Neuroscience Institute. “Future larger studies are needed to help us understand the tie better, but for now, we hope these results can be used to help predict care and ensure that patients have the best outcomes.”

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