Introduction

The creatine kinase reaction plays a vital role in muscle energetics as the conversion of phosphocreatine (PCr) into free creatine (Cr) accompanies the production of ATP as an energy source. For decades, this reaction has been probed using 31-P MRS¹. Recently, it has been shown that the CrCEST technique is an alternative that offers sensitivity benefits allowing for higher spatial resolution acquisitions². Due to the need for adequate temporal resolution to characterize recovery, limited saturation offsets are acquired, and no averaging can be done. The resulting MTR_asym maps have low SNR at 3T and require spatial averaging over large ROIs in order to achieve reliable fits to recovery curves.

Recently, it has been shown that low rank tensor approximations can offer improved denoising in CEST³ compared to low rank matrix techniques such as SVD⁴ and PCA⁵. Here, we apply that concept and exploit the additional time domain in dynamic CrCEST to show improved denoising at higher dimensionality despite acquiring a limited number of offsets and time points.

Higher SNR maps can lead to more reliable fits over smaller ROIs or even individual voxels and can allow exploration of muscle response heterogeneity within muscle groups that has not previously been achievable using 31-P spectroscopy based methods.

Methods

All subjects were scanned under an approved Institutional Review Board protocol of the University of Pennsylvania with written informed consent obtained from each volunteer. The CrCEST experiment was performed following in-magnet plantar flexion with a pneumatically-controlled foot pedal in a 3T scanner (MAGNETOM Prisma, Siemens Healthcare, Erlangen, Germany). Single slice images were acquired axially through the center of the calf muscle with resolution 1.3x1.3x10 mm³. Six saturation offsets were acquired at ±1.5, ±1.8, and ±2.1 ppm with each image acquired every 5 s for an overall temporal resolution of 30 s. Sixteen time points were measured for the recovery curves. Low rank tensor approximations computed as a Tucker decomposition⁶ were taken on the raw k-space data in two ways: 1) the saturation offsets and time course were considered as a single temporal dimension for a data set of size (k_x,k_y,n_c,temp) = (128, 128, 15, 6*16), where n_c is the number of coils, and temp is the off-tp dimension; 2) the saturation offsets and time course were kept as separate dimensions resulting in a tensor of size (k_x,k_y,n_c,off,tp). In the first method, rank reduction was applied keeping the first (90, 90, 15, 10) singular values in each dimension. In the second method, the first (90, 90, 15, 4, 3) singular values were kept. Recovery rates were obtained from a three parameter monoexponential fitting of each pixel's time course.

Results and Discussion

Figure 1 shows the CrCEST MTR_asym maps from a single subject following exercise reconstructed without enhanced denoising (top row) and with the proposed rank reduction methods (second and third row). Both rank-reduced reconstructions show similar performance over the spatial maps with considerable improvement compared to the original. Figure 2 shows the reconstructed recovery curves from a single voxel in the lateral gastrocnemius that show the exercise response. The R² goodness of fit is 0.65 for the conventional method, 0.92 for rank-reduced Method 1 that uses a single temporal dimension, and 0.98 for rank-reduced Method 2 that uses separate offset and time points. Fits over the entire lateral gastrocnemius were similar and had recovery time constants of 72, 78, and 76 s for the three methods. However, fits over the medial gastrocnemius had recovery time constants of 79, 97, 72 s, with that coming from Method 1 begin notably higher. The spatial distribution of R² values had a mean of 0.46 for the conventional method, 0.81 for Method 1, and 0.90 for Method 2 and is shown in Figure 3. Lastly, Figure 4 shows the singular values along the temporal dimensions described in Method 1 (temp) and in Method 2 (separate off and tp).

While both rank-reduced approximations show good denoising relative to the original data, only the second method in which the saturation offsets and time points are kept separate utilizes the full dimensionality of the data. This difference was not obvious in the spatial maps showing recovery but is more pronounced in the time course over a single voxel and in the R² goodness of fit. This is despite both methods having similar overall rank reduction. Recovery time constants from a medial gastrocnemius ROI showed potential bias in the results from Method 1, though this requires further investigation.

Performing the CrCEST exercise experiment at 7T would offer advantages both in terms of SNR and relative shift toward the slow exchange regime compared to 3T. However, because of the greater availability of 3T scanners and still favorable exchange rates, 3T optimization remains an important and viable goal.

Conclusion

The low rank tensor approximation of dynamic CrCEST images shows improved denoising that leads to more reliable recovery kinetic characterization. Despite the limited numbers of time points and offsets, rank reduction exploiting the full dimensionality by considering offsets and time points separate dimensions showed the best performance. Single pixel recovery curves can be reliably fit, allowing for study of muscle response heterogeneity.

Acknowledgements

No acknowledgement found.