Stacked U-Nets with self-assisted priors towards robust correction of rigid motion artifact in brain MRI.

Magnetic Resonance Imaging (MRI) is sensitive to motion caused by patient movement due to the relatively long data acquisition time. This could cause severe degradation of image quality and therefore affect the overall diagnosis. In this paper, we develop an efficient retrospective 2D deep learning method called stacked U-Nets with self-assisted priors to address the problem of rigid motion artifacts in 3D brain MRI. The proposed work exploits the usage of additional knowledge priors from the corrupted images themselves without the need for additional contrast data. The proposed network learns the missed structural details through sharing auxiliary information from the contiguous slices of the same distorted subject. We further design a refinement stacked U-Nets that facilitates preserving the spatial image details and improves the pixel-to-pixel dependency. To perform network training, simulation of MRI motion artifacts is inevitable. The proposed network is optimized by minimizing the loss of structural similarity (SSIM) using the synthesized motion-corrupted images from 83 real motion-free subjects. We present an intensive analysis using various types of image priors: the proposed self-assisted priors and priors from other image contrast of the same subject. The experimental analysis proves the effectiveness and feasibility of our self-assisted priors since it does not require any further data scans. The overall image quality of the motion-corrected images via the proposed motion correction network significantly improves SSIM from 71.66% to 95.03% and declines the mean square error from 99.25 to 29.76. These results indicate the high similarity of the brain's anatomical structure in the corrected images compared to the motion-free data. The motion-corrected results of both the simulated and real motion data showed the potential of the proposed motion correction network to be feasible and applicable in clinical practices.

Accelerated 3D myelin water imaging using joint spatio-temporal reconstruction.

PURPOSE: To enable acceleration in 3D multi-echo gradient echo (mGRE) acquisition for myelin water imaging (MWI) by combining joint parallel imaging (JPI) and joint deep learning (JDL). METHODS: We implemented a multistep reconstruction process using both advanced parallel imaging and deep learning network which can utilize joint spatiotemporal components between the multi-echo images to further accelerate 3D mGRE acquisition for MWI. In the first step, JPI was performed to estimate missing k-space lines. Next, JDL was implemented to reduce residual artifacts and produce high-fidelity reconstruction by using variable splitting optimization consisting of spatiotemporal denoiser block, data consistency block, and weighted average block. The proposed method was evaluated for MWI with 2D Cartesian uniform under-sampling for each echo, enabling scan times of up to approximately 2 min for 2 mm × 2 mm × 2 mm $2\ {\rm mm} \times 2\ {\rm mm} \times 2\ {\rm mm}$ 3D coverage. RESULTS: The proposed method showed acceptable MWI quality with improved quantitative values compared to both JPI and JDL methods individually. The improved performance of the proposed method was demonstrated by the low normalized mean-square error and high-frequency error norm values of the reconstruction with high similarity to the fully sampled MWI. CONCLUSION: Joint spatiotemporal reconstruction approach by combining JPI and JDL can achieve high acceleration factors for 3D mGRE-based MWI.

Optimization of Maillard reaction with ribose for enhancing anti-allergy effect of fish protein hydrolysates using response surface methodology.

Halibut is served on sushi and as sliced raw fish fillets. We investigated the optimal conditions of the Maillard reaction (MR) with ribose using response surface methodology to reduce the allergenicity of its protein. A 3-factored and 5-leveled central composite design was used, where the independent variables were substrate (ribose) concentration (X1, %), reaction time (X2, min), and pH (X3), while the dependent variables were browning index (Y1, absorbance at 420nm), DPPH scavenging (Y2, EC50 mg/mL), FRAP (Y3, mM FeSO4/mg extract) and ß-hexosaminidase release (Y4, %). The optimal conditions were obtained as follows: X1, 28.36%; X2, 38.09min; X3, 8.26. Maillard reaction products of fish protein hydrolysate (MFPH) reduced the amount of nitric oxide synthesis compared to the untreated FPH, and had a significant anti-allergy effect on ß-hexosaminidase and histamine release, compared with that of the FPH control. We concluded that MFPH, which had better antioxidant and anti-allergy activities than untreated FPH, can be used as an improved dietary source.