CEST MR-Fingerprinting: Practical considerations and insights for acquisition schedule design and improved reconstruction

Abstract

Purpose: To understand the influence of various acquisition parameters on the ability of CEST MR-Fingerprinting (MRF) to discriminate different chemical exchange parameters and to provide tools for optimal acquisition schedule design and parameter map reconstruction.

Methods: Numerical simulations were conducted using a parallel computing implementation of the Bloch-McConnell equations, examining the effect of TR, TE, flip-angle, water $T_1$ and $T_2$ , saturation-pulse duration, power, and frequency on the discrimination ability of CEST-MRF. A modified Euclidean distance matching metric was evaluated and compared to traditional dot product matching. L-Arginine phantoms of various concentrations and pH were scanned at 4.7T and the results compared to numerical findings.

Results: Simulations for dot product matching demonstrated that the optimal flip-angle and saturation times are ${30}^{\circ }$ and 1100 ms, respectively. The optimal maximal saturation power was 3.4 μT for concentrated solutes with a slow exchange rate, and 5.2 μT for dilute solutes with medium-to-fast exchange rates. Using the Euclidean distance matching metric, much lower maximum saturation powers were required (1.6 and 2.4 μT, respectively), with a slightly longer saturation time (1500 ms) and ${90}^{\circ }$ flip-angle. For both matching metrics, the discrimination ability increased with the repetition time. The experimental results were in agreement with simulations, demonstrating that more than a 50% reduction in scan-time can be achieved by Euclidean distance-based matching.

Conclusions: Optimization of the CEST-MRF acquisition schedule is critical for obtaining the best exchange parameter accuracy. The use of Euclidean distance-based matching of signal trajectories simultaneously improved the discrimination ability and reduced the scan time and maximal saturation power required.

Keywords: chemical exchange rate; chemical exchange saturation transfer (CEST); magnetic resonance fingerprinting (MRF); optimization; pH; quantitative imaging.

FIGURE 1

Dependence of the dot product loss on the acquisition parameters. The surface plots with projected loss iso-contours describe the effect of the maximal saturation power and the saturation time (T_sat) (A-C), the flip angle (FA) and TR (D-F), and the TE and saturation frequency offset (G-I), on the DP_loss, for scenarios A (left column), B (center column), and C (right column). In all images, the z-axis represents the DP_loss, which is also color coded from blue to yellow. The optimal combination for each examined parameter pair is given in the surface plot

FIGURE 2

Dependence of the Euclidean distance loss on the acquisition parameters. The surface plots with projected loss iso-contours describe the effect of the maximal saturation power and the saturation time (T_sat) (A-C), the FA and TR (D-F), and the TE and saturation frequency offset (G-I), on the ED_loss, for scenarios A (left column), B (center column), and C (right column). In all images, the z-axis represents the ED_loss, which is also color coded from blue to yellow. The optimal combination for each examined parameter pair is given in the surface plot

FIGURE 3

Exemplary trajectories for different combinations of acquisition schedule parameters normalized by the 2-norm (for dot product matching). In (A-C), the exchange rate was fixed at 400 Hz, and the solute concentration varied from 10 to 100 mM. In (d-f), the solute concentration was fixed on 50 mM, and the exchange rate varied from 50 to 800 Hz. The left column represents a close to optimal acquisition parameters combination (TR was set to 4s instead of 8s for speed considerations), with saturation time (T_sat) = 2600 ms, FA = 60°, saturation offset = 2.6 ppm, TE = 21 ms, maximum saturation power = 6 μT. The center column represents the baseline acquisition schedule (T_sat = 3000 ms, saturation offset = 3 ppm, see Section 2.5), and the right column represents the same schedule, but with shorter saturation time and excitation flip angle (FA = 15°, and T_sat = 1500 ms). The same CEST properties of scenario “C” were used for all trajectories

FIGURE 4

Exemplary trajectories for different combinations of acquisition schedule parameters normalized by M₀ (for Euclidean distance matching). In (A-C), the exchange rate was fixed at 400 Hz, and the solute concentration varied from 10 to 100 mM. In (D-F), the solute concentration was fixed on 50 mM, and the exchange rate varied from 50 to 800 Hz. The left column represents a close to optimal acquisition parameters combination (TR was set to 4s instead of 8s for speed considerations), with saturation time (T_sat) = 1600 ms, FA = 90°, saturation offset = 2.9 ppm, TE = 21 ms, maximum saturation power = 5.2 μT. The center column represents the baseline acquisition schedule (T_sat = 3000 ms, saturation offset = 3 ppm, see Section 2.5), and the right column represents the same schedule, but with shorter saturation time and excitation flip angle (FA = 15°, and T_sat = 1500 ms). The same CEST properties of scenario “C” were used for all trajectories

FIGURE 5

Optimization of the schedule length. A, DP_loss values for the baseline schedule with varied lengths. Note the step-like shape, predicting noticeable performance improvement at N_t = 11, with further improvement when additional iterations are added. B, Solute concentration (M0s) RMSE, comparing the dot product and Euclidean distance-based matching. C, Proton exchange rate (k_sw) RMSE for both matching metrics

FIGURE 6

Dot product matching of CEST-MRF phantom images. A, Reference of known solute concentration (top) and QUESP proton exchange rate images (bottom) for the 9 imaged vials. B-D, Dot product reconstructed CEST-MRF images for 4, 11, and 30 iterations, respectively. The same color map and dynamic range (top-right) were used for all images

FIGURE 7

Euclidean distance matching of CEST-MRF phantom images. A, Reference of known solute concentration (top) and QUESP proton exchange rate images (bottom) for the 9 imaged vials. B-D, Euclidean distance reconstructed CEST-MRF images for 4, 11, and 30 iterations, respectively. The same color map and dynamic range (top-right) were used for all images

FIGURE 8

Phantom study quantitative analysis. RMSE values for solute concentration (M0s) (A), and chemical exchange rate (k_sw) (B), using the baseline schedule with varied lengths. Note the significant performance improvement at N_t = 11 for Euclidean distance M0s matching (A), significantly lower than the dot product respective values. No significant improvement occurs for the Euclidean distance at N_t = 30, suggesting the schedule can be shortened without harming performance