Using smartly trained neural networks, researchers at TU Graz have succeeded in generating precise real-time images of the beating heart from just a few MRI measurement data. Other MRI applications can also be accelerated using this procedure.
TU GrazSep 30 2024
Medical imaging using magnetic resonance imaging is very time-consuming since an image has to be compiled from data from many individual measurements. Thanks to the use of machine learning, imaging is also possible with less MRI measurement data, which saves time and costs. However, the prerequisite for this is perfect images that can be used to train the AI models.
Calibration of imaging through withheld data Martin Uecker and Moritz Blumenthal used self-supervised learning methods to train their machine learning model for MRI imaging. The basis for training the model is not pre-curated perfect images, but a subset of the initial data from which the model is to reconstruct the images. Moritz Blumenthal explains it like this: "We divided the measurement data provided by the MRI device into two portions.
This procedure can make many MRI applications faster and cheaper "Our process is ready for application," says Martin Uecker, "even if it will probably be a while before it is actually used in practice." The method can be used for many other MRI applications to make them faster and therefore cheaper. This includes quantitative MRI, for example, in which physical tissue parameters are precisely measured and quantified.
This allows radiologists to access precise data for diagnoses instead of having to interpret images based on differences in brightness using their professional experience. Up to now, however, quantitative MRI measurements have often taken a very long time. With our machine learning model, we were able to speed up these measurements considerably without any loss of quality.
Heart Imaging Magnetic Resonance Imaging Medical Imaging Research
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