Low Dose CT Image Reconstruction With Learned Sparsifying Transform

A major challenge in computed tomography (CT) is to reduce X-ray dose to a low or even ultra-low level while maintaining the high quality of reconstructed images. We propose a new method for CT reconstruction that combines penalized weighted-least squares reconstruction (PWLS) with regularization based on a sparsifying transform (PWLS-ST) learned from a dataset of numerous CT images. We adopt an alternating algorithm to optimize the PWLS-ST cost function that alternates between a CT image update step and a sparse coding step. We adopt a relaxed linearized augmented Lagrangian method with ordered-subsets (relaxed OS-LALM) to accelerate the CT image update step by reducing the number of forward and backward projections. Numerical experiments on the XCAT phantom show that for low dose levels, the proposed PWLS-ST method dramatically improves the quality of reconstructed images compared to PWLS reconstruction with a nonadaptive edge-preserving regularizer (PWLS-EP).Comment: This is a revised and corrected version of the IEEE IVMSP Workshop paper DOI: 10.1109/IVMSPW.2016.752821

Self-supervised regression learning using domain knowledge: Applications to improving self-supervised denoising in imaging

Regression that predicts continuous quantity is a central part of applications using computational imaging and computer vision technologies. Yet, studying and understanding self-supervised learning for regression tasks - except for a particular regression task, image denoising - have lagged behind. This paper proposes a general self-supervised regression learning (SSRL) framework that enables learning regression neural networks with only input data (but without ground-truth target data), by using a designable pseudo-predictor that encapsulates domain knowledge of a specific application. The paper underlines the importance of using domain knowledge by showing that under different settings, the better pseudo-predictor can lead properties of SSRL closer to those of ordinary supervised learning. Numerical experiments for low-dose computational tomography denoising and camera image denoising demonstrate that proposed SSRL significantly improves the denoising quality over several existing self-supervised denoising methods.Comment: 17 pages, 16 figures, 2 tables, submitted to IEEE T-I

Putative Interaction Proteins of the Ubiquitin Ligase Hrd1 in

The endoplasmic reticulum (ER) is the entry portal of the conventional secretory pathway where the newly synthesized polypeptides fold, modify, and assemble. The ER responses to the unfolded proteins in its lumen (ER stress) by triggering intracellular signal transduction pathways include the ER-associated degradation (ERAD) pathway and the unfolded protein response (UPR) pathway. In yeast and mammals, the ubiquitin ligase Hrd1 is indispensable for the ERAD pathway, and also Hrd1-mediated ERAD pathway plays a crucial role in maintaining homeostasis and metabolism of human beings. However, the underlying physiological roles and regulatory mechanism of the Hrd1-involved ERAD pathway in the plant pathogenic fungi are still unclear. Here, we identified the Hrd1 orthologous proteins from 9 different fungi and noticed that these Hrd1 orthologs are conserved. Through identification of MoHrd1 putative interacting proteins by co-immunoprecipitation assays and enrichment analysis, we found that MoHrd1 is involved in the secretory pathway, energy synthesis, and metabolism. Taken together, our results suggest that MoHrd1 is conserved among fungi and play an important role in cellular metabolism and infection-related development. Our finding helps uncover the mechanism of Hrd1-involved ERAD pathway in fungi and sheds a new light to understand the pathogenic mechanism of Magnaporthe oryzae