Highly-accelerated CEST MRI using frequency-offset-dependent k-space sampling and deep-learning reconstruction.

PURPOSE: To develop a highly accelerated CEST Z-spectral acquisition method using a specifically-designed k-space sampling pattern and corresponding deep-learning-based reconstruction. METHODS: For k-space down-sampling, a customized pattern was proposed for CEST, with the randomized probability following a frequency-offset-dependent (FOD) function in the direction of saturation offset. For reconstruction, the convolution network (CNN) was enhanced with a Partially Separable (PS) function to optimize the spatial domain and frequency domain separately. Retrospective experiments on a self-acquired human brain dataset (13 healthy adults and 15 brain tumor patients) were conducted using k-space resampling. The prospective performance was also assessed on six healthy subjects. RESULTS: In retrospective experiments, the combination of FOD sampling and PS network (FOD + PSN) showed the best quantitative metrics for reconstruction, outperforming three other combinations of conventional sampling with varying density and a regular CNN (nMSE and SSIM, p < 0.001 for healthy subjects). Across all acceleration factors from 4 to 14, the FOD + PSN approach consistently outperformed the comparative methods in four contrast maps including MTRasym, MTRrex, as well as the Lorentzian Difference maps of amide and nuclear Overhauser effect (NOE). In the subspace replacement experiment, the error distribution demonstrated the denoising benefits achieved in the spatial subspace. Finally, our prospective results obtained from healthy adults and brain tumor patients (14×) exhibited the initial feasibility of our method, albeit with less accurate reconstruction than retrospective ones. CONCLUSION: The combination of FOD sampling and PSN reconstruction enabled highly accelerated CEST MRI acquisition, which may facilitate CEST metabolic MRI for brain tumor patients.

Dynamic hyperpolarized 13 C MR spectroscopic imaging using SPICE in mouse kidney at 9.4 T.

This study aims to investigate the feasibility of dynamic hyperpolarized 13 C MR spectroscopic imaging (MRSI) using the SPectroscopic Imaging by exploiting spatiospectral CorrElation (SPICE) technique and an estimation of the spatially resolved conversion constant rate (kpl ). An acquisition scheme comprising a single training dataset and several imaging datasets was proposed considering hyperpolarized 13 C circumstances. The feasibility and advantage of the scheme were investigated in two parts: (a) consistency of spectral basis over time and (b) accuracy of the estimated kpl . The simulations and in vivo experiments support accurate kpl estimation with consistent spectral bases. The proposed method was implemented in an enzyme phantom and via in vivo experiments. In the enzyme phantom experiments, spatially resolved homogeneous kpl maps were observed. In the in vivo experiments, normal diet (ND) mice and high-fat diet (HFD) mice had kpl (s-1 ) values of medullar (ND: 0.0119 ± 0.0022, HFD: 0.0195 ± 0.0005) and cortical (ND: 0.0148 ±0.0023, HFD: 0.0224 ±0.0054) regions which were higher than vascular (ND: 0.0087 ±0.0013, HFD: 0.0132 ±0.0050) regions. In particular, the kpl value in the medullar region exhibited a significant difference between the two diet groups. In summary, the feasibility of using modified SPICE for dynamic hyperpolarized 13 C MRSI was demonstrated via simulations and in vivo experiments. The consistency of spectral bases over time and the accuracy of the estimated kpl values validate the proposed acquisition scheme, which comprises only a single training dataset. The proposed method improved the spatial resolution of dynamic hyperpolarized 13 C MRSI, which could be used for kpl estimation using high signal-to-noise ratio spectral bases.