“Thanks to this innovation, quantitative magnetic resonance imaging could make tremendous progress,” says Sebastian Kozerke, Professor of Biomedical Imaging at ETH and the University of Zurich. He worked with Valery Vishnevskiy and Jonas Walheim
to develop a method that greatly accelerates so-called 4D flow MRIs.
“At the moment, the recording and subsequent processing of a 4D flow MRI takes up to 30 minutes. Our results show that this could be possible within five minutes in the future.” The underlying research was featured in the journal
Nature Machine Intelligence
earlier this week as article and cover of the April issue.
Magnetic resonance tomography (MRT or MRI) is a key modality in clinical diagnosis. It poses no health risks and provides precise images of the interior of the body. This method can be used to display soft body parts such as tissue and organs in 3D and with high contrast. Furthermore, special recording techniques deliver information on the dynamics of the cardiovascular system.
In particular, 4D flow MRI measurements enable the quantification of dynamic changes of blood flow. Such dynamic images are highly useful, particularly when it comes to detecting cardiovascular diseases.
However, conventional 4D flow MRI has a significant drawback: the method is very time-consuming. Nowadays, the data recording can be completed in the MRI scanner within four minutes. However, the required compressed sensing approach comes at a cost: the subsequent image reconstruction is iterative and thus takes a very long time. Doctors have to wait 25 minutes or longer for the images to appear on their computers.
Thus, the results of the measurement only become available long after the doctor has completed the examination. This is why 4D flow MRI is not yet established in everyday medical practice. Changes to blood flow are currently diagnosed primarily via ultrasound – a method that’s quicker but less precise in comparison with MRI.
Elegant and efficient algorithms
In the recently published article, the researchers from ETH and the University of Zurich illustrate a way in which image reconstruction for 4D flow MRI could be made quicker and thus more practical. “The solution consists of elegant and efficient algorithms based on neural networks,” explains Kozerke.
