When introducing Skope tools, we get asked to describe what types of experiments can be performed using a field camera. The value of a Skope field camera, whether in a stand-alone unit like the Dynamic Field Camera, or integrated into an RF coil as in the NeuroCam, is the ability to measure the dynamic magnetic field of a scanner.

Let’s step back a bit to understand what it means to measure the “dynamic magnetic field of a scanner”. An MR scanner can be thought of having two magnetic fields – the static field is generated by the main superconducting magnet (which gives the scanner its Tesla rating and is often called the B0 field) and a dynamic field generated by the gradient coils (these change strength rapidly in response to the sequence being used and are the source of the sounds you hear during a scan!) The static field gets more attention in research as it can be measured easily by the scanner with a “B0 Mapping sequence.” From this sequence you can get spatial measurements of B0 with a high degree of accuracy. This B0 information can be used to improve images through shimming the field of the scanner or by integration into post-processing. Both shimming and post-processing corrections are used in research and clinical settings daily.

In contrast to the static field, the dynamic field gets less attention in research despite the influence it has on image quality. The dynamic magnetic field is generated by the gradients and is responsible for the “image encoding” (getting data for each point in the image). The dynamic field has an ideal operational state defined by the scanner programming (called a pulse sequence) but is in actuality determined by a combination of sources: the programmed sequence, hardware limitations and interactions, electromagnetic laws, even physiologic sources play a role at UHF. This combination of sources alters the dynamic field from what was expected in the pulse sequence, resulting in the image being distorted or of lower quality than what could otherwise be achieved.

It is possible however to measure these fields during scanner operation – this is what Skope tools are built to do! Every Skope tool has multiple field measurement probes, the data from which are combined to generate a representation of the dynamic field during scanner operation. This data can be used in many different ways, which gets us back to the original topic of what types of experiments can be performed using Skope tools.

The type of experiment that you would use a Skope tool for is directly related to the type of research you conduct. Types of experiments include:

  • Neuroscience and clinical researchers will be interested to utilize field monitoring during imaging experiments to capture and account for scanner and physiological fluctuations – improving image quality and the power of their studies.
  • MR physics and engineering, in which you make precise measurements of the scanner’s performance to either characterize a development (i.e. a new gradient coil or new scanner) or as input to the development (i.e. improving pre-emphasis or enabling real-time adjustment of the scan.)
  • MR sequence developers will be interested to use scanner performance to characterize the scanner to improve sequence operation (i.e. improving cardiac imaging) or image reconstruction (concurrent, post-monitored, or GIRF reconstruction). Additionally, field monitoring tools can be used to quickly verify the operation of a new sequence while it is being developed, saving debugging time!

While each experiment warrants a longer discussion, let’s take a brief look at two, namely writing new sequences and quality improvement of neuroimaging.

Writing a new sequence often requires iterative testing and creative debugging when it doesn’t perform as expected. The resulting images do not often contain a direct pointer to the issue causing artifacts, just that there is an issue. One such example was in the development of a new sequence (a dual echo spiral for B0 Mapping.) In the initial iteration, the gradient programming (waveform) was miscalculated, resulting in it not returning to the center of the image k-space before acquiring the next. Lars Mueller, the developer, was able to make a quick, direct measurement of the sequence which showed the programming error (A) – instead of spending time generating hypotheses on why the image was incorrectly encoded. He recalculated the gradients (B) and was able to get a corrected image. Only one imaging session was required to get a definite answer as to what was wrong with the sequence.

 

Image Courtesy Dr. Lars Mueller, PhD, University of Leeds

In neuroscience, diffusion imaging is useful for many analyses of the brain to understand normal physiology or disease progression. Getting the best diffusion images is challenging since the process of measuring the diffusion (called diffusion encoding) can distort the image in a way that is unique to each diffusion direction. The left image below shows diffusion images without field measurements by Skope tools. On the right are images reconstructed using field measurements made using the NeuroCam 3T and reconstructed in skope-i. Notice that the images on the right have the same shape (not being stretched or skewed) and remain in the same location (translation). This is a direct result of having measured the field fluctuations during the scan. As a result of these images having the same shape and location, they do not need to be registered to each other and will produce higher quality derived parameter maps.

Images Skope MRT

These are but a few of the ways a Skope camera can be used. As this series of posts continues, we will dive into many of these methods, exploring the ways in which field monitoring can improve your research!