Self-Gating Strategies
Ruixi Zhou1
1Beijing University of Posts and Telecommunications, China

Synopsis

Keywords: Image acquisition: Motion correction

A number of techniques have been proposed to address the motion problem in MRI. One such technique is the self-gating strategy, which attempts to take advantage of MR data itself to deal with motion. This lecture will cover the key concepts in self-gating strategy and how to extract self-gating signals with different sampling patterns. This lecture will especially focus on cardiac applications, where both respiratory and cardiac motion exist, and will also cover the state-of-the-art self-gating techniques when signal intensity is changing during acquisition.

Introduction

Motion artifacts have been a persistent challenge in magnetic resonance imaging for a long time. To address this problem, a number of techniques have been proposed. The most clinically practical solution is to ask patients to hold their breath while scanning, in the meantime utilizing some external devices, such as electrocardiograph and pulse oximetry signals, to synchronize/track other motion. To advance further, several strategies have been proposed to enable motion-free imaging, such as using navigators (1–5) to guide image acquisition, performing real-time imaging (6,7), or implementing motion correction techniques (8–10) in the processing and reconstruction steps. In this session, we will be exploring one of the solutions to deal with motion, which is self-gating strategies.

Self-gating strategies have been proposed to deal with motion artifacts in MRI since early 90s (11,12). These methods are usually based on continuous acquisition, and greatly ease the patients’ burden and increase the acquisition efficiency and robustness. Depending on which organ is being imaged, different types of motion can result in various artifacts, thus requiring particular solutions. For brain imaging, voluntary head motion and sudden involuntary movements can have a bad effect on image quality. These movements are usually non-periodic and unpredictable, making it challenging to deal with using self-gating strategies. For body imaging, the most dominant motions are respiratory motion and cardiac motion, which are usually involuntary and periodic, and can be addressed by self-gating strategies.

Part 1: Key concepts of self-gating strategy

‘Wireless’ gating techniques involve the acquisition and processing of additional MR signals to derive cardiac cycle timing information. By comparison, some other techniques have been proposed to use imaging data itself to track motion. Previously reported approaches have used the k-space center point (13,14), center line (15–18), or some kinds of projections (19,20) as self-gating signals. Some techniques also used image-based methods (15,21) to extract motion information.

Part 2: Self-gating strategy with sampling pattern consideration

The nature of taking advantage of MR imaging data itself places high demands on the acquisition trajectory in order to obtain efficient and robust self-gating signals. Compared to Cartesian sampling (18,22), radial (15,21), rosette (23,24) and spiral trajectories (25,26) have more flexibility when tracing k-space. Especially for spiral trajectory, it is not only more time-efficient considering it can cover a greater extent of k-space each TR, but also provides the ability to carefully designed the k-space center sampling points to meet the requirements for self-gating strategy.

Part 3: Challenges in self-gating strategy

Although self-gating strategies have been implemented and utilized in a number of studies, there are still some challenges in certain applications. Firstly, for cardiac imaging, where both respiratory and cardiac motion exist, it is of great importance to appropriately distinguish the two motion signals. Determined by the structure and physical placement of coils, different coils are sensitive to different motions. The ones close to the diaphragm acquire more information related to respiratory motion, while the ones near the heart demonstrate more cardiac motion details. One strategy is to choose particular coils for corresponding motion, as was done in GRASP (27,28). Since these motions are known to lie in certain frequency ranges, the motion signal in the coil-element with the highest peak in the frequency range will be selected to represent that motion. To take a step further and make use of most coil-elements, principal component analysis (PCA) (25) and independent component analysis (ICA) (29) have been proposed to extract the useful motion information among all the coils.

Furthermore, dynamic imaging applications, such as perfusion, T1 mapping, T2 mapping etc., pose a significant challenge for self-gating strategies. In those cases, signal intensity fluctuations will disturb the extraction of motion information from self-gating signals. In this lecture, we will survey the current state-of-the-art self-gating techniques when signal intensity is changing during acquisition (30–32).

Part 4 Future and summary

With the rapid development of artificial intelligence (AI) in medical imaging, techniques used in AI have been increasingly applied in the field of image reconstruction and post-processing, including MRI self-gating strategy. Researchers have proposed a supervised learning strategy to obtain robust cardiac self-gating signals from recorded ECG signals for both single-contrast and multi-contrast imaging (33).

In the end, we will summarize the advantages of self-gating strategies when dealing with motion, as well as the requirements and limitations when applying self-gating strategies. Moreover, we will extend to consider the obstacles towards clinical translation.

Acknowledgements

We are extremely grateful to all colleagues who have helped and contributed slides for this education lecture.

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Proc. Intl. Soc. Mag. Reson. Med. 31 (2023)