3D deuterium metabolic imaging (DMI) of the human liver at 7 T using low-rank and subspace model-based reconstruction

Purpose: To implement a low-rank and subspace model-based reconstruction for 3D deuterium metabolic imaging (DMI) and compare its performance against Fourier transform-based (FFT) reconstruction in terms of spectral fitting reliability. Methods: Both reconstruction methods were applied on simulated and experimental DMI data. Numerical simulations were performed to evaluate the effect of increasing acceleration factors. The impact on spectral fitting results, SNR, and the overall normalized root mean square error (NRMSE) compared to ground-truth data were calculated. A comparative analysis was performed on DMI data acquired from the human liver, including both natural abundance and post-deuterated glucose intake data at 7 T. Results: Simulation showed the Cramer-Rao lower bound [%] of water, glucose, sum of glutamate and glutamine (Glx), and lipid signals for the low-rank and subspace model-based reconstruction at R = 1.0 was 12.4, 14.7, 17.3, and 11.0 times lower than FFT. At R = 1.1, NRMSE was 1.4%, 1.3%, 0.8%, and 4.2% lower for the water, glucose, Glx, and lipid, respectively, compared to FFT. However, the NRMSE of the Glx and lipid increased by 0.4% and 3.2% at R = 1.3. For the in vivo DMI experiment, SNR was 2.5–3.0 times higher compared to FFT. The fitted amplitude of water and glucose peaks showed Cramer-Rao lower bound [%] values that were approximately 2.3 times lower than FFT. Conclusion: Simulations and in vivo experiments on the human liver demonstrate that low-rank and subspace model-based reconstruction with undersampled data mitigates noise and enhances spectral fitting quality.