Assessment of higher-order singular value decomposition denoising methods on dynamic hyperpolarized [1-13C]pyruvate MRI data from patients with glioma.

BACKGROUND

Real-time metabolic conversion of intravenously-injected hyperpolarized [1-C]pyruvate to [1-C]lactate and [C]bicarbonate in the brain can be measured using dynamic hyperpolarized carbon-13 (HP-C) MRI. However, voxel-wise evaluation of metabolism in patients with glioma is challenged by the limited signal-to-noise ratio (SNR) of downstream C metabolites, especially within lesions. The purpose of this study was to evaluate the ability of higher-order singular value decomposition (HOSVD) denoising methods to enhance dynamic HP [1-C]pyruvate MRI data acquired from patients with glioma.

METHODS

Dynamic HP-C MRI were acquired from 14 patients with glioma. The effects of two HOSVD denoising techniques, tensor rank truncation-image enhancement (TRI) and global-local HOSVD (GL-HOSVD), on the SNR and kinetic modeling were analyzed in [1-C]lactate data with simulated noise that matched the levels of [C]bicarbonate signals. Both methods were then evaluated in patient data based on their ability to improve [1-C]pyruvate, [1-C]lactate and [C]bicarbonate SNR. The effects of denoising on voxel-wise kinetic modeling of k and k was also evaluated. The number of voxels with reliable kinetic modeling of pyruvate-to-lactate (k) and pyruvate-to-bicarbonate (k) conversion rates within regions of interest (ROIs) before and after denoising was then compared.

RESULTS

Both denoising methods improved metabolite SNR and regional signal coverage. In patient data, the average increase in peak dynamic metabolite SNR was 2-fold using TRI and 4-5 folds using GL-HOSVD denoising compared to acquired data. Denoising reduced k modeling errors from a native average of 23% to 16% (TRI) and 15% (GL-HOSVD); and k error from 42% to 34% (TRI) and 37% (GL-HOSVD) (values were averaged voxelwise over all datasets). In contrast-enhancing lesions, the average number of voxels demonstrating within-tolerance k modeling error relative to the total voxels increased from 48% in the original data to 84% (TRI) and 90% (GL-HOSVD), while the number of voxels showing within-tolerance k modeling error increased from 0% to 15% (TRI) and 8% (GL-HOSVD).

CONCLUSION

Post-processing denoising methods significantly improved the SNR of dynamic HP-C imaging data, resulting in a greater number of voxels satisfying minimum SNR criteria and maximum kinetic modeling errors in tumor lesions. This enhancement can aid in the voxel-wise analysis of HP-C data and thereby improve monitoring of metabolic changes in patients with glioma following treatment.