Overcoming limitations in MRI

To understand the significance of their findings, we need to explore the principle of MRI.

Let's start at the beginning: An MR image is sensitive to the presence of protons. Water, for example, contains two protons. Fat and other body tissues contain even more. Each proton has its own tiny magnetic field due to its so-called spin. Outside an MRI scanner, the many millions of protons and their tiny magnetic fields point in different directions and cancel each other out. In magnetic terms, a person is neutral. But that changes once you put them into the magnetic field of an MRI machine. 

Then the protons with their tiny magnetic fields align with the MRI’s main magnetic field. After manipulation with high-frequency pulses (the excitation), the protons spin in step and are ready to be converted to an image. This excitation happens with a frequency that matches the spin frequency of the protons, rendering this a resonance phenomenon: Hence the name magnetic resonance imaging.

A strong, static magnetic field is produced in an MRI scanner using a superconducting magnetic coil. It weighs several tons and gives the MRI its typical shape. The gradient coils modify the magnetic field locally so that the signals from different body regions can be distinguished. 

In other words, the gradient coils alter the main magnetic field in predictable patterns and spatially encode the MR signal. This allows a precise localization of the induced pulses in the receiving coil caused by protons after excitation. The MRI system measures these signals and uses them to create an image of the patient's body. 

Gradient coils are also critical for diffusion-weighted imaging, a unique feature of MRI. With diffusion-weighted imaging you can map the diffusion-process of molecules like water in tissue and generate unique contrasts in MR images.

For years, developers studied how to best increase the strength of the magnetic gradient field. One of their dilemmas was that increasing the magnetic gradient field limits the ramp-up speed of the gradient’s strength – but some applications need both. 

Here’s just one example: A strong gradient field that’s also rapidly generated could potentially help researchers better understand neurodegenerative diseases like multiple sclerosis, even between flare-ups. Stronger gradients could visualize the relevant microstructures more easily. These structures can’t be visualized and distinguished in traditional MR images – but with more powerful diffusion images, the disease progress could potentially be better understood. Yet, only a completely redesigned gradient technology would make it possible to realize a strong gradient field that’s also rapidly generated.