A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging.

Magnetic resonance imaging (MRI) is an essential diagnostic tool that suffers from prolonged scan time. To alleviate this limitation, advanced fast MRI technology attracts extensive research interests. Recent deep learning has shown its great potential in improving image quality and reconstruction speed. Faithful coil sensitivity estimation is vital for MRI reconstruction. However, most deep learning methods still rely on pre-estimated sensitivity maps and ignore their inaccuracy, resulting in the significant quality degradation of reconstructed images. In this work, we propose a Joint Deep Sensitivity estimation and Image reconstruction network, called JDSI. During the image artifacts removal, it gradually provides more faithful sensitivity maps with high-frequency information, leading to improved image reconstructions. To understand the behavior of the network, the mutual promotion of sensitivity estimation and image reconstruction is revealed through the visualization of network intermediate results. Results on in vivo datasets and radiologist reader study demonstrate that, for both calibration-based and calibrationless reconstruction, the proposed JDSI achieves the state-of-the-art performance visually and quantitatively, especially when the acceleration factor is high. Additionally, JDSI owns nice robustness to patients and autocalibration signals.

A Paired Phase and Magnitude Reconstruction for Advanced Diffusion-Weighted Imaging.

OBJECTIVE: Multi-shot interleaved echo planer imaging (Ms-iEPI) can obtain diffusion-weighted images (DWI) with high spatial resolution and low distortion, but suffers from ghost artifacts introduced by phase variations between shots. In this work, we aim at solving the ms-iEPI DWI reconstructions under inter-shot motions and ultra-high b-values. METHODS: An iteratively joint estimation model with paired phase and magnitude priors is proposed to regularize the reconstruction (PAIR). The former prior is low-rankness in the k-space domain. The latter explores similar edges among multi-b-value and multi-direction DWI with weighted total variation in the image domain. The weighted total variation transfers edge information from the high SNR images (b-value = 0) to DWI reconstructions, achieving simultaneously noise suppression and image edges preservation. RESULTS: Results on simulated and in vivo data show that PAIR can remove inter-shot motion artifacts very well (8 shots) and suppress the noise under the ultra-high b-value (4000 s/mm2) significantly. CONCLUSION: The joint estimation model PAIR with complementary priors has a good performance on challenging reconstructions under inter-shot motions and a low signal-to-noise ratio. SIGNIFICANCE: PAIR has potential in advanced clinical DWI applications and microstructure research.

Catalytic Descriptor Exploration for Ru-Based Fischer-Tropsch Catalysts: Effect of Chlorine and Sulfur Addition.

As an important factor in the design of catalysts, catalytic descriptor exploration has emerged as a novel frontier in heterogeneous catalysis. Here, the underlying structure-activity relationships of Ru-based catalysts are theoretically studied to shed light on this area. Calculations of different competing reaction paths suggest that the HCO*-mediated path─because of two synergistic active sites─is more favorable than others. In addition, compared to unadulterated Ru catalysts, the presence of Cl enhances the hydrocarbon production, whereas the presence of S decreases it. After a systematic examination of a series of structure-activity relationships (42 in total), we found that both charge transfer and average charge difference of active Ru atoms are good descriptors for the binding stability of reactants. However, for reactivity the Gibbs free energy of the reactants performs better. More interestingly, due to the quite different catalytic processes of the dissociation and hydrogenation steps, their correlations have opposite slopes.