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Artificial intelligence reduces the dose of gadolinium in MR



CHICAGO, Aza. 26, 2018 / PRNewswire / – Researchers are using artificial intelligence to reduce the dose of the contrast agent that may be left behind after MRI exams, according to current studies. Radiological Association of the United States (RSNA).

Gadolinium is a heavy metal used in contrast material that improves MRI images. Recent studies have found that trace amounts of metal remain in the body of people who have undergone some type of gadolinium. The effects of this deposition are not known, but the radiologists are proactively working to optimize the patient's safety while receiving important information on gadolinium-enhanced MRI scans.

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"There is no specific evidence of gadolinium deposits in the brain and body," said Stanford University, Stanford University, senior researcher at Enhao Gong Stanford University. "These are not clear, while minimizing the risks of the patient. It is essential to maximize the clinical value of MRI studies."

Dr. Gong and Stanford colleagues are learning a lot of learning to achieve the goal. Deep learning is a sophisticated artificial intelligence technique, such as computers. Using the so-called "computational neural network" models, the computer not only perceives images, but finds the most subtle differences in the image of non-human observer subjects.

In order to prepare a deep learning algorithm, the researchers used RM images for 200 patients, due to their improved contrast MRI analysis. For each patient three sets of images were collected: pre-contrast searches, pre-contrast administration and zero-scan scans; low-dose scanning after 10% of the administration of standard gadolinium dose; and full-dose scanning, after doses of dosage administered.

The algorithm studied roughly the total doses for measuring zero doses and low doses. After neuroradiologists, they evaluated overall improvement in contrast and overall quality.

According to the results, the quality of the image was not very significant in two-dimensional, MR-enhanced algorithm-enhancements and complementary MR-contrast enhancement doses. The initial results have also been shown to be the equivalent of equivalent dose enhancements, equivalent to contrast enhancements, without using any contrast agent.


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