Synopsis

Gradient echo based pulse sequences at 3T may lack the required contrast to distinguish blood from muscle. To overcome this, T₂ preparation (T2-prep) modules are used in cardiac imaging to distinguish the blood pool from the myocardium. To suppress unwanted fat signals, we exploited the additional degrees of freedom that offer the multiple radiofrequency pulses of an adiabatic T2-prep and we used particle swarm optimization (PSO) to develop a T2-prep with robust fat suppression capabilities that works in the presence of flow. Its robustness against B₁ and B₀ inhomogeneities were predicted by the Bloch simulations for a range of fatty tissue frequencies, and could be confirmed experimentally both in phantoms and in volunteers.

Introduction

Gradient echo (GRE) based pulse sequences at 3T may lack the required contrast to distinguish blood from muscle. To overcome this, T₂ preparation (T₂-prep) modules [1] are used in cardiac imaging to distinguish the blood pool from the myocardium. To suppress unwanted fat signals, additional lengthy and specific absorption rate (SAR)-demanding modules are often necessary, as their brightness may distort image quality, or hinder anatomical visualization due to chemical shift artifacts. However, higher magnetic field strengths may complicate conventional fat saturation techniques, such as CHESS [2], which are sensitive to B₁ and B₀ field inhomogeneities.

In this study, the aim was to exploit the additional degrees of freedom that the multiple radiofrequency (RF) pulses of an adiabatic T₂-prep offer and to use particle swarm optimization (PSO) to develop a T₂-prep with robust fat suppression capabilities that works in the presence of flow [3]. The new PSO adiabatic T2-prep (PSOA-T₂-prep) was compared with a conventional adiabatic T₂-prep (CA-T2-prep) [4] (Fig. 1A) and a water-selective adiabatic T₂-prep [5] (WSA-T2-prep) (Fig. 1B) through numerical simulations, phantom experiments, and cardiac experiments in volunteers.

Methods

Simulations: The duration, frequency and amplitude of the RF excitation and restoration pulses, as well as the initial phase of the RF excitation pulses were optimized using particle swarm optimization (PSO)[6,7]. Bloch equation simulations of the T₂-prep were performed in Matlab (The Mathworks) to compute its fitness function, which was defined as follows:

$f = SD(F_z,res - 0.05) + SD(W_z,exc) + SD(W_z,res - 1)$

where SD is the standard deviation, F_z and W_z are the longitudinal magnetizations of fat (resonance frequencies spanning from -600 Hz to -200 Hz) and water (frequency = 0 Hz) , subscript exc and res indicates the magnetization after excitation and restoration respectively. For each simulated tissue frequency, the B₁ strength was varied between 80% and 120%.

Acquisitions: The three different T₂-preps were implemented in a Cartesian, 2D segmented k-space GRE sequence at 3T system (Prisma, Siemens). The developed PSOA-T₂-prep was tested against the CA-T₂-prep and the WSA-T₂-prep on a 3-compartment phantom that mimics the magnetic properties of blood, fat and myocardium. Sequence parameters: pixel size 1.1x1.1mm² resolution, 14 k-space lines acquired per T₂-prep (duration of 40 ms) spaced by 800 ms, RF excitation angle 15°, TE = 3.71 ms, TR = 8.07 ms, slice thickness = 5mm. In addition, ECG-triggered 2D breath-held acquisitions of the heart and thigh with parameters identical to the phantom scan were acquired on volunteers (N=3). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were computed in signal compartments containing blood, myocardium, chest fat, and epicardial fat. The specific absorption rate (SAR) was monitored during the scans. Differences were tested via a Student’s t-test for paired data, with a p-value < 0.05 considered as significant.

Results

Simulations: The excitation and restoration pulses parameters obtained by PSO (Fig. 1C) show a complete T₂ preparation of water, while suppressing a range of fat frequencies (See animation uploaded as Fig. 5). Bloch simulations predicted an increased robustness in fat suppression of the PSOA-T2-prep in comparison to the CA-T₂-prep and the WSA-T₂-prep (Fig. 2).

Acquisitions: The PSOA-T₂-prep visually reduced fat signal compared to CA-T₂-prep and WSA-T₂-prep (Fig. 4). Compared to the CA-T₂-prep, the PSOA-T₂-prep reduced chest and epicardial fat SNR by 90.0% (p=0.01) and 64.8% (p=0.002) respectively (Fig. 3). Compared to the WSA-T₂-prep, the PSOA-T₂-prep decreased chest and epicardial fat signal by 59.9% (p=0.044) and by 44.9% non-significantly (p=0.051) respectively. The PSOA-T₂-prep reduced blood-myocardium CNR by 7.3% (p=0.036) compared to the CA-T₂-prep, but increased it by 26.3% (p=0.001) compared to the WSA-T₂-prep.

Discussion

Conclusion

Acknowledgements

References

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