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. Medical imaging using magnetic resonance imaging (MRI) 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. Such perfect training images do not exist for certain applications, such as real-time (moving image) MRI, as such images are always somewhat blurred.
An international research team led by Martin Uecker and Moritz Blumenthal from the Institute of Biomedical Imaging at Graz University of Technology (TU Graz) has now succeeded in generating precise live MRI images of the beating heart even without such training images and with very little MRI data with the help of smartly trained neural networks. Thanks to these improvements, real-time MRI could be used more frequently in practice in the future. 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 w.