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A First: COVID-19 Brain MRI and Lung CT Imaging Correlation Identified


This correlation can help providers pinpoint which patients will develop more neurological abnormalities, helping them plan interventions to improve outcomes.

One year into the pandemic, researchers have finally been able to see the correlation between the severity of COVID-19 on a lung CT and the neurological severity of the virus on brain MRI.

By examining what shows up on lung CT scans, said researchers from the University of Cincinnati, providers can predict how badly a COVID-19-positive patient will suffer neurological problems that can present on MRIs. In doing so, they can potentially improve patient outcomes and symptoms identification and management.

Abdelkader Mahammedi, M.D., assistant professor of radiology at UC and a UC Health neuroradiologist.

CREDIT: University of Cincinnati

Abdelkader Mahammedi, M.D., assistant professor of radiology at UC and a UC Health neuroradiologist.

CREDIT: University of Cincinnati

“We’ve seen patients with COVID-19 experience stroke, brain bleeds, and other disorders affecting the brain,” said study co-leader Abdelkader Mahammedi, M.D., assistant professor of radiology at the University of Cincinnati. “So, we’re finding, through patient experiences, that neurological symptoms are correlating to those with more severe respiratory disease; however, little information has been available on identifying potential associations between imaging abnormalities in the brain and lungs in COVID-19 patients.”

Related Content: Brain MRI Reveals Leukoencephalopathy Vulnerability in COVID-19 Patients

Imaging, he said, is proof. It gives physicians confidence in the severity of an illness and how it forms, and it contributes to decision-making about a patient’s care. The team led by Mahammedi and Achala Vagal, M.D., professor of radiology, published their findings March 12 in the American Journal of Neuroradiology. They will also present their results during the 59th annual meeting of the American Society of Neuroradiology.

Along with colleagues from institutions in Spain, Italy, and Brazil, UC’s team reviewed the electronic medical records and images of 135 COVID-19-positive patients who were hospitalized between March 3, 2020, and June 25, 2020, who were experienced both respiratory and neurological complications.

Related Content: Windows of Infection: MRI Reveals COVID-19-Linked Eye Abnormalities

Of this group, 49 patients – 36 percent – had both abnormal CT lungs scans and neurological symptoms. Specifically, those patients had significantly higher mean CT lung severity scores – 9.9 compared to 5.8 from patients without acute abnormal neuroimaging findings. They were also more likely to present with ischemic stroke (40 patients versus 11 patients), the team said, and they were more likely to have either ground-glass opacities or consolidation.

Based on the team’s analysis, a CT lung severity score threshold of at least 8 was 74-percent sensitive and 65-percent specific for acute abnormal neuroimaging findings. Specifically, 28 percent had acute ischemic infarct, 10 percent had intracranial hemorrhage (including microhemorrhages), and 11 percent had leukoencephalopathy with or without restricted diffusion. Peripheral ground-glass opacities with or without consolidation were the predominant CT chest findings.

Related Content: Post-COVID-19, CT Reveals Potentially Lifetime Lung Damage in One-Thirds of Patients

These findings, Mahammedi said, can potentially be a powerful tool for providers, allowing them to use the severity of disease identified on a patient’s CT scan to categorize those individuals into groups that are more likely to develop abnormalities on brain imaging. Being able to do so could strengthen the physician’s abilities to implement therapies to improve outcomes, such as stroke prevention.

“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,” he said. “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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