Artificial intelligence may reduce dose of gadolinium in MRI scans
Artificial intelligence can be used to reduce the dose of gadolinium -- a contrast agent that may be left behind in the body after MRI scans, according to a new study presented at the annual meeting of the Radiological Society of North America (RSNA).
Gadolinium is a heavy metal used in contrast material for enhancing and improving the quality of MRI images. Recent studies have found the presence of trace amount of the metal in the body of people who have undergone MRI with gadolinium as a contrast agent. The effects of this deposition are not known, but radiologists are working proactively to optimize patient safety while preserving the important information provided by gadolinium-enhanced MRI scans.
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Enhao Gong, a researcher at Stanford University in Stanford, California, and colleagues conducted the study with a goal to mitigate potential patient risks that gadolinium deposits might pose while maximizing the clinical value of the MRI exams through deep learning.
Deep learning is a sophisticated artificial intelligence technique that teaches computers by examples. Through the use of models called convolutional neural networks, the computer can not only recognize images but also find subtle distinctions among the imaging data that a human observer might not be capable of discerning.
To train the deep learning algorithm, the researchers used MR images from 200 patients who had received contrast-enhanced MRI exams for a variety of indications. They collected three sets of images for each patient: pre-contrast scans, done prior to contrast administration and referred to as the zero-dose scans; low-dose scans, acquired after 10 percent of the standard gadolinium dose administration; and full-dose scans, acquired after 100 percent dose administration.
The algorithm learned to approximate the full-dose scans from the zero-dose and low-dose images. Neuroradiologists then evaluated the images for contrast enhancement and overall quality.
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- The image quality was not significantly different between the low-dose, algorithm-enhanced MR images and the full-dose, contrast-enhanced MR images.
- The initial results also demonstrated the potential for creating the equivalent of full-dose, contrast-enhanced MR images without any contrast agent use.
According to Dr. Gong, these findings suggest the method's potential for dramatically reducing gadolinium dose without sacrificing diagnostic quality.
"Low-dose gadolinium images yield significant untapped clinically useful information that is accessible now by using deep learning and AI," said Dr. Gong.
"We're not trying to replace existing imaging technology," Dr. Gong said. "We're trying to improve it and generate more value from the existing information while looking out for the safety of our patients."
"Future research will include evaluation of the algorithm across a broader range of MRI scanners and with different types of contrast agents," he concluded.