AI Case Study
Stanford researchers reduce heavy metal dose in MRIs using convolutional neural networks
Researchers at Stanford university are conducting research that aims to reduce the amount of gadolinium, a heavy metal, that remains in the body after an MRI scan. For this purpose the team trained convolutional neural networks to distinguish between scans where no no gadolinium was used for contrast-enhanced images, scans where 10% of the full dose was used and scans where a standard dose was used. When evaluating the images for contrast enhancement and overall quality, the neuroradiologists found no significant differences in quality between the three. This means that less gadolinium could be used without compromising output quality.
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"There is concrete evidence that gadolinium deposits in the brain and body," said study lead author Enhao Gong, Ph.D., researcher at Stanford University in Stanford, Calif. "While the implications of this are unclear, mitigating potential patient risks while maximizing the clinical value of the MRI exams is imperative."
Dr. Gong and colleagues at Stanford have been studying deep learning as a way to achieve this goal. Deep learning is a sophisticated artificial intelligence technique that teaches computers by examples. Through 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.
Results showed that 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."
The study's results show that the suggested method can dramatically reduce gadolinium dose in MRIs without compromising diagnostic quality.
"Deep learning is a sophisticated artificial intelligence technique that teaches computers by examples. Through 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."
"Gadolinium is a heavy metal used in contrast material that enhances images on MRI. Recent studies have found that trace amounts of the metal remain in the bodies of people who have undergone exams with certain types of gadolinium. The effects of this deposition are not known, but radiologists are working proactively to optimize patient safety while preserving the important information that gadolinium-enhanced MRI scans provide."
MR images from 200 patients