Synopsis

In order to use simultaneous transmission and acquisition in clinical MRI for living subjects, robustness to load and environmental changes has to be established, especially for uncooperative subjects. High isolation can be achieved with active cancellation methods, but maintaining it over a long time is a challenge. A look-up table based method is proposed with a smart search algorithm that enables fast dynamic decoupling of transmit/receive coils using an active decoupling circuit. Experiments with a birdcage coil used as a transceiver show that maintaining >80 dB isolation is possible even under the presence of load variation.

Introduction

In conventional MRI, excitation and acquisition is interleaved due to coupling of transmit signal to MR signal. Simultaneous transmission and reception in MRI requires high isolation (>100 dB) due to small MR signal amplitude, but it has the benefits such as acquiring signal from short T2 tissues and less RF power consumption.¹ Although there are active and passive decoupling methods proposed^2-6 in order to get rid of the leakage signal, shortcomings of these methods hinder the possibility of clinical usage. Sensitivity of decoupling to load variation and environmental change is one such challenge.³ There are optimization-based active decoupling methods proposed using a self-interference cancellation circuit, yet they are not suitable for dynamic decoupling where abrupt changes of coil parameters may occur.^4,5

In this study, a novel algorithm is proposed where STAR (Simultaneous Transmit and Receive) circuit parameters are controlled to counteract environmental and load changes that deteriorate decoupling performance over time.

Methods

With the implemented STAR circuit topology (Fig.1), $$$m$$$ delay-attenuation lines with $$$n$$$ bit attenuators can generate $$$2^{nxm}$$$ possible vectors. Through the optimization of attenuation control parameters, a candidate vector can be found which cancels out the leak signal.^4,5 However, there is no guarantee that problem will converge to global optimum, since the problem has many local minima.

Proposed algorithm can find one such candidate vector without doing any optimization by utilizing an offline look-up table. Therefore, without optimization process, high isolation can be achieved quickly and sustained even in the presence of load variation.

Considering the whole set of vectors that can be generated being finite, it is possible to store them, assuming the system is time invariant. Instead of measuring them all, which is not feasible due to time constraint, every possible output of the circuit can be modeled as superposition of signals coming from each line separately and stored beforehand. However, it is still time consuming to search in this offline stored look-up table due to its size. Proposed algorithm speeds up search process by sorting and sectioning this look-up table in a smart way (Fig. 2).

As result of this operation, first, a list is generated, where each element consists of a complex number (output of the circuit: Amplitude and phase) and a control input vector which will generate that output. Second, a matrix is generated that stores region boundaries that is determined by phase and amplitude intervals. When a new measurement acquired, the algorithm can point out the indices that include global optimum in the look-up table directly from this matrix. Using these outputs, leak signal can be estimated and cancelled out in a loop, correcting any bias in the characterization measurements.

Results

Simulation performance of implemented STAR topology can be seen in Fig 3.

Using a network analyzer in place of MR scanner, decoupling experiments are conducted. A two port birdcage coil with a diameter of 20 cm is used as a transmit/receive coil. Experiment setup is shown in detail in Fig. 4. Circuit characterization is done when everything, including coils, are connected to circuit in order to demonstrate algorithm’s ability of eliminating bias coming from look-up table. Using these measurements, a look-up table is constructed with intervals of $$$Δφ=0.25°$$$, and $$$Δ|S_{21}|=10^{-5}$$$. Algorithm performance is tested over a time period under both stable and unstable environment. It can be seen that abrupt changes of load can be compensated in a few iterations (Fig. 5).

Discussion

Ability to decouple the coils in a reactive manner makes it feasible to image living subjects. With this system, even if load changes slightly, it is possible to decouple coils dynamically and continue the scan, which can sustain high decoupling without sacrificing too much scan time.

One of the main drawbacks of this approach is high storage space requirement due to large look-up table. However, as storage space is getting cheaper, this drawback proves to be unimportant. Another drawback is very narrow band for decoupling, which can be solved by using a band pass filter as suggested by Sohn.³

Conclusion

Acknowledgements

References

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