Deep learning network for integrated coil inhomogeneity correction and brain extraction of mixed MRI data.

Magnetic Resonance Imaging (MRI) has been widely used to acquire structural and functional information about the brain. In a group- or voxel-wise analysis, it is essential to correct the bias field of the radiofrequency coil and to extract the brain for accurate registration to the brain template. Although automatic methods have been developed, manual editing is still required, particularly for echo-planar imaging (EPI) due to its lower spatial resolution and larger geometric distortion. The needs of user interventions slow down data processing and lead to variable results between operators. Deep learning networks have been successfully used for automatic postprocessing. However, most networks are only designed for a specific processing and/or single image contrast (e.g., spin-echo or gradient-echo). This limitation markedly restricts the application and generalization of deep learning tools. To address these limitations, we developed a deep learning network based on the generative adversarial net (GAN) to automatically correct coil inhomogeneity and extract the brain from both spin- and gradient-echo EPI without user intervention. Using various quantitative indices, we show that this method achieved high similarity to the reference target and performed consistently across datasets acquired from rodents. These results highlight the potential of deep networks to integrate different postprocessing methods and adapt to different image contrasts. The use of the same network to process multimodality data would be a critical step toward a fully automatic postprocessing pipeline that could facilitate the analysis of large datasets with high consistency.

Magnetic Resonance-Based Synthetic Computed Tomography Using Generative Adversarial Networks for Intracranial Tumor Radiotherapy Treatment Planning.

The purpose of this work is to develop a reliable deep-learning-based method that is capable of synthesizing needed CT from MRI for radiotherapy treatment planning. Simultaneously, we try to enhance the resolution of synthetic CT. We adopted pix2pix with a 3D framework, which is a conditional generative adversarial network, to map the MRI data domain into the CT data domain of our dataset. The original dataset contains paired MRI and CT images of 31 subjects; 26 pairs were used for model training and 5 were used for model validation. To identify the correctness of the synthetic CT of models, all of the synthetic CTs were calculated by the quantized image similarity formulas: cosine angle distance, Euclidean distance, mean square error, peak signal-to-noise ratio, and mean structural similarity. Two radiologists independently evaluated the satisfaction score, including spatial, detail, contrast, noise, and artifacts, for each imaging attribute. The mean (±standard deviation) of the structural similarity indices (CAD, L2 norm, MSE, PSNR, and MSSIM) between five real CT scans and the synthetic CT scans were 0.96 ± 0.015, 76.83 ± 12.06, 0.00118 ± 0.00037, 29.47 ± 1.35, and 0.84 ± 0.036, respectively. For synthetic CT, radiologists rated the results as evincing excellent satisfaction in spatial geometry and noise level, good satisfaction in contrast and artifacts, and fair imaging details. The similarity index and clinical evaluation results between synthetic CT and original CT guarantee the usability of the proposed method.

ACSL3 and GSK-3ß are essential for lipid upregulation induced by endoplasmic reticulum stress in liver cells.

The endoplasmic reticulum (ER) is essential for lipid biosynthesis, and stress signals in this organelle are thought to alter lipid metabolism. Elucidating the mechanisms that underlie the dysregulation of lipid metabolism in hepatocytes may lead to novel therapeutic approaches for the treatment of lipid accumulation. We first tested the effects of several inhibitors on lipid dysregulation induced by tunicamycin, an ER stress inducer. Triacsin C, an inhibitor of long-chain acyl-CoA synthetase (ACSL) 1, 3, and 4, was the most potent among these inhibitors. We then analyzed the expression of the ACSL family during ER stress. The expression of ACSL3 was induced by ER stress in HuH-7 cells and in mice livers. ACSL3 shRNA, but not ACSL1 shRNA, inhibited the induction of lipid accumulation. GSK-3ß inhibitors attenuated ACSL3 expression and the lipid accumulation induced by ER stress in HuH-7 cells. shRNA that target GSK-3ß also inhibited the upregulation of ACSL3 and lipid accumulation in HuH-7 and HepG2 cells. The hepatitis B virus mutant large surface protein, which is known to induce ER stress, increased the lipid content of cells. Similarly, Triacsin C, and GSK-3ß inhibitors abrogated the lipid dysregulation caused by the hepatitis B virus mutant large surface protein. Altogether, ACSL3 and GSK-3ß represent novel therapeutic targets for lipid dysregulation by ER stress.