Introduction

One disadvantage of MRI as a clinical imaging modality is the expense of system, both in terms of purchase cost and continued maintenance. The magnet must be housed in an RF shielded room, and its operation requires highly-trained technicians. These factors limit its use to late in the healthcare cycle, and its siting to major hospitals. As a result it plays little role in screening in the developed world, and it is estimated that worldwide ~70% of the population has zero access to an MRI scan. Within the past five years there has been a strong push, academically and commercially, to address this issue by developing low-field (<100 mT) MRI systems that have been designed for use in the intensive care unit (ICU), for pediatric imaging in developing countries and as point-of-care devices. Each of these systems is based upon permanent magnet technology, using variously a yoked C-shape magnet, a homogeneous Halbach configuration, or a gradient Halbach configuration. The advantages of low-field MRI include reduced patient contraindications and low SAR, which allows efficient data acquisition using, for example, long echo trains of fully-refocused turbo (fast) spin echoes (TSE). Magnetic susceptibility induced image artifacts are also much reduced compared to clinical field strengths, as are chemical shift artifacts and motion. However, the much lower gradient strengths, higher B0 inhomogeneity, higher B0 temporal drift, and increased electromagnetic interference (EMI) due to the lack of RF shielding all potentially result in greater image artifacts than at conventional field strengths. This talk describes some of these artifacts, and methods for minimizing them.

Artifacts: sources and minimization

Magnetic susceptibility: image distortions from medical implants, as well as the associated forces and torques, are much less at low field, as shown for four different structures in Figure 1. Chemical shift: the ~3 ppm difference in chemical shift between fat and water corresponds to an ~6-10 Hz difference at 50-80 mT fields, a value which is much less than the linewidth in the relatively inhomogeneous fields. Therefore, there are no chemical shift artifacts at these field strengths. Gradient non-linearity and B0 inhomogeneity: conventional image distortion correction algorithms require accurate B0 maps which are not possible to acquire directly when the B0 inhomogeneities also produce significant image distortions. One method which has proved to be quite successful is joint estimation, using shifted and non-shifted TSE sequences. For each iteration, ∆B0 maps were reconstructed from the phase difference using Tikhonov regularization, while images were reconstructed using either conjugate phase reconstruction (CPR) or model-based (MB) image reconstruction, taking the reconstructed field map into account. MB reconstructions were furthermore combined with compressed sensing (CS) to show the flexibility of this approach towards undersampling. Results are shown in Figures 2 and 3 in phantoms and in vivo, respectively. Electromagnetic interference: one of the major challenges in POC MRI is that the systems should be designed to operate outside the usual RF shielded environment (Faraday shielded room). Electromagnetic interference (EMI) created either by equipment close to the scanners or consisting of general environmental EM noise can cause artifacts which render images non-diagnostic. For portable MRI devices, some degree of shielding can be built directly around the scanner. However, at least one side of the scanner cannot be shielded to allow patient access. In addition to this opening, the human body acts as an antenna for any external EMI [12] and “guides” this EMI into the imaging region. A conductive mesh cloth can be draped over the patient to reduce the body antenna effect [13], but this set-up can be somewhat uncomfortable and relies on very good contact with any local shielding structures. Many different schemes for EMI reduction have been designed, including most recently one based on external sensors, external sensors plus deep learning or MR-silent modes of a receive coil.

Acknowledgements

This work was funded by a Horizon 2020 ERC Advanced Grant (670629).

References

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