Bi-level Guided Diffusion Models for Zero-Shot Medical Imaging Inverse Problems

In the realm of medical imaging, inverse problems aim to infer high-quality images from incomplete, noisy measurements, with the objective of minimizing expenses and risks to patients in clinical settings. The Diffusion Models have recently emerged as a promising approach to such practical challenges, proving particularly useful for the zero-shot inference of images from partially acquired measurements in Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). A central challenge in this approach, however, is how to guide an unconditional prediction to conform to the measurement information. Existing methods rely on deficient projection or inefficient posterior score approximation guidance, which often leads to suboptimal performance. In this paper, we propose \underline{\textbf{B}}i-level \underline{G}uided \underline{D}iffusion \underline{M}odels ({BGDM}), a zero-shot imaging framework that efficiently steers the initial unconditional prediction through a \emph{bi-level} guidance strategy. Specifically, BGDM first approximates an \emph{inner-level} conditional posterior mean as an initial measurement-consistent reference point and then solves an \emph{outer-level} proximal optimization objective to reinforce the measurement consistency. Our experimental findings, using publicly available MRI and CT medical datasets, reveal that BGDM is more effective and efficient compared to the baselines, faithfully generating high-fidelity medical images and substantially reducing hallucinatory artifacts in cases of severe degradation.Comment: 19 pages, 14 figure

Affine Transformation Edited and Refined Deep Neural Network for Quantitative Susceptibility Mapping

Deep neural networks have demonstrated great potential in solving dipole inversion for Quantitative Susceptibility Mapping (QSM). However, the performances of most existing deep learning methods drastically degrade with mismatched sequence parameters such as acquisition orientation and spatial resolution. We propose an end-to-end AFfine Transformation Edited and Refined (AFTER) deep neural network for QSM, which is robust against arbitrary acquisition orientation and spatial resolution up to 0.6 mm isotropic at the finest. The AFTER-QSM neural network starts with a forward affine transformation layer, followed by an Unet for dipole inversion, then an inverse affine transformation layer, followed by a Residual Dense Network (RDN) for QSM refinement. Simulation and in-vivo experiments demonstrated that the proposed AFTER-QSM network architecture had excellent generalizability. It can successfully reconstruct susceptibility maps from highly oblique and anisotropic scans, leading to the best image quality assessments in simulation tests and suppressed streaking artifacts and noise levels for in-vivo experiments compared with other methods. Furthermore, ablation studies showed that the RDN refinement network significantly reduced image blurring and susceptibility underestimation due to affine transformations. In addition, the AFTER-QSM network substantially shortened the reconstruction time from minutes using conventional methods to only a few seconds

Comparison of broiler performance, carcass yields and intestinal microflora when fed diets containing transgenic (Mon-40-3-2) and conventional soybean meal

This study was conducted to analyze the effects of transgenic glyphosate-tolerant soybeans on the performance, carcass yields and intestinal microflora of broiler chickens. Three hundred and sixty oneday- old Abor Aerec broilers were randomly divided into two dietary treatments, adding genetically modified (GM) glyphosate-tolerant soybean meal or conventional soybean meal, respectively. Broiler body weight and feed intake were recorded at regular intervals (day 0, 21 and 42). Chickens were slaughtered at day 42 for carcass yield measurement and sampling. Diversity of the ileum and cecum microflora was determined by denaturing gradient gel electrophoresis (DGGE) technique and DNA sequencing. No treatment differences (P > 0.05) were detected among dietary treatments for any measured performance and carcass parameters. The microbial population in ileum and cecum also had no significant difference between the two treatments (P>0.05). The similarity of the total ileum and cecum microflora between the two treatments was about 62 and 58%, respectively. The DNA-DGGE electrophoresis pattern bands of intestine microbe were divided into two groups because of the different diet. Fifteen DGGE DNA bands were identified, of which five of them were identified as known bacteria. The current study showed that there were no adverse effects of the transgenic soybean meal on the intestinal microflora of broilers.Key words: Broiler, glyphosate-tolerant soybean meal, intestinal microbiota, feed safety

Accelerating Quantitative Susceptibility Mapping using Compressed Sensing and Deep Neural Network

Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing method that quantifies tissue magnetic susceptibility distributions. However, QSM acquisitions are relatively slow, even with parallel imaging. Incoherent undersampling and compressed sensing reconstruction techniques have been used to accelerate traditional magnitude-based MRI acquisitions; however, most do not recover the full phase signal due to its non-convex nature. In this study, a learning-based Deep Complex Residual Network (DCRNet) is proposed to recover both the magnitude and phase images from incoherently undersampled data, enabling high acceleration of QSM acquisition. Magnitude, phase, and QSM results from DCRNet were compared with two iterative and one deep learning methods on retrospectively undersampled acquisitions from six healthy volunteers, one intracranial hemorrhage and one multiple sclerosis patients, as well as one prospectively undersampled healthy subject using a 7T scanner. Peak signal to noise ratio (PSNR), structural similarity (SSIM) and region-of-interest susceptibility measurements are reported for numerical comparisons. The proposed DCRNet method substantially reduced artifacts and blurring compared to the other methods and resulted in the highest PSNR and SSIM on the magnitude, phase, local field, and susceptibility maps. It led to 4.0% to 8.8% accuracy improvements in deep grey matter susceptibility than some existing methods, when the acquisition was accelerated four times. The proposed DCRNet also dramatically shortened the reconstruction time by nearly 10 thousand times for each scan, from around 80 hours using conventional approaches to only 30 seconds.Comment: 10 figure

Using the FY-3E satellite hyperspectral infrared atmospheric sounder to quantitatively monitor volcanic SO2

The Hyperspectral Infrared Atmospheric Sounder Type II (HIRAS-II) aboard the Fengyun 3E (FY-3E) satellite provides valuable data on the vertical distribution of atmospheric states. However, effectively extracting quantitative atmospheric information from the observations is challenging due to the large number of hyperspectral sensor channels, inter-channel correlations, associated observational errors, and susceptibility of the results to influence by trace gases. This study explores the potential of FY-3E/HIRAS-II to atmospheric loadings of SO2 from volcanic eruption. A methodology for selecting SO2 sensitive channels from the large number of hyperspectral channels recorded by FY-3E/HIRAS-II is presented. The methodology allows for the selection of SO2-sensitive channels that contain similar information on variations in atmospheric temperature and water vapor for minimizing the influence of atmospheric water vapor and temperature to SO2. A sensitivity study shows that the difference in brightness temperature between the experimentally selected SO2 sensitive channels and the background channels effectively removes interference signals from surface temperature, atmospheric temperature, and water vapor during SO2 detection and inversion. A positive difference between near-surface atmospheric temperature and surface temperature enables the infrared band to capture more SO2 information in the lower and middle layers. The efficacy of FY-3E/HIRAS-II SO2 sensitive channels in quantitively monitor volcanic SO2 is demonstrated using data from the 29 April 2024 eruption of Mount Ruang in Indonesia. Using FY-3E/HIRAS-II measurements, the spatial distribution and quantitatively information of volcanic SO2 are easily observed. The channel selection can significantly enhance the computation efficiency while maintain the accuracy of SO2 detection and retrieval, despite the large volume of data

xQSM: Quantitative Susceptibility Mapping with Octave Convolutional and Noise Regularized Neural Networks

Quantitative susceptibility mapping (QSM) is a valuable magnetic resonance imaging (MRI) contrast mechanism that has demonstrated broad clinical applications. However, the image reconstruction of QSM is challenging due to its ill-posed dipole inversion process. In this study, a new deep learning method for QSM reconstruction, namely xQSM, was designed by introducing modified state-of-the-art octave convolutional layers into the U-net backbone. The xQSM method was compared with recentlyproposed U-net-based and conventional regularizationbased methods, using peak signal to noise ratio (PSNR), structural similarity (SSIM), and region-of-interest measurements. The results from a numerical phantom, a simulated human brain, four in vivo healthy human subjects, a multiple sclerosis patient, a glioblastoma patient, as well as a healthy mouse brain showed that the xQSM led to suppressed artifacts than the conventional methods, and enhanced susceptibility contrast, particularly in the ironrich deep grey matter region, than the original U-net, consistently. The xQSM method also substantially shortened the reconstruction time from minutes using conventional iterative methods to only a few seconds.Comment: 37 pages, 10 figures, 3 tabl

Plug-and-Play Latent Feature Editing for Orientation-Adaptive Quantitative Susceptibility Mapping Neural Networks

Quantitative susceptibility mapping (QSM) is a post-processing technique for deriving tissue magnetic susceptibility distribution from MRI phase measurements. Deep learning (DL) algorithms hold great potential for solving the ill-posed QSM reconstruction problem. However, a significant challenge facing current DL-QSM approaches is their limited adaptability to magnetic dipole field orientation variations during training and testing. In this work, we propose a novel Orientation-Adaptive Latent Feature Editing (OA-LFE) module to learn the encoding of acquisition orientation vectors and seamlessly integrate them into the latent features of deep networks. Importantly, it can be directly Plug-and-Play (PnP) into various existing DL-QSM architectures, enabling reconstructions of QSM from arbitrary magnetic dipole orientations. Its effectiveness is demonstrated by combining the OA-LFE module into our previously proposed phase-to-susceptibility single-step instant QSM (iQSM) network, which was initially tailored for pure-axial acquisitions. The proposed OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a self-supervised manner on a specially-designed simulation brain dataset. Comprehensive experiments are conducted on simulated and in vivo human brain datasets, encompassing subjects ranging from healthy individuals to those with pathological conditions. These experiments involve various MRI platforms (3T and 7T) and aim to compare our proposed iQSM+ against several established QSM reconstruction frameworks, including the original iQSM. The iQSM+ yields QSM images with significantly improved accuracies and mitigates artifacts, surpassing other state-of-the-art DL-QSM algorithms.Comment: 13pages, 9figure

Research progress of integrated stress response in pathogenesis of Alzheimer's disease

Integrated stress response (ISR) is a cellular adaptive response induced by stress, which is strictly regulated by multiple phosphokinases, phosphatases and other proteins to maintain protein homeostasis. Studies have shown that ISR is abnormally activated in Alzheimer's disease, and targeted regulation of different proteins in ISR pathway inhibits the abnormal activation of ISR, leading to restoration of protein homeostasis and alleviation of the neuropathological changes and memory impairment in Alzheimer's disease models. These lines of evidence suggest that ISR has the potential to be a therapeutic target in Alzheimer's disease treatment. This paper reviews the abnormal activation and regulation mechanism of ISR in Alzheimer's disease and discusses the application of ISR as therapeutic targets to Alzheimer's disease models

Diagnostic performance and clinical impact of blood metagenomic next-generation sequencing in ICU patients suspected monomicrobial and polymicrobial bloodstream infections

IntroductionEarly and effective application of antimicrobial medication has been evidenced to improve outcomes of patients with bloodstream infection (BSI). However, conventional microbiological tests (CMTs) have a number of limitations that hamper a rapid diagnosis.MethodsWe retrospectively collected 162 cases suspected BSI from intensive care unit with blood metagenomics next-generation sequencing (mNGS) results, to comparatively evaluate the diagnostic performance and the clinical impact on antibiotics usage of mNGS.Results and discussionResults showed that compared with blood culture, mNGS detected a greater number of pathogens, especially for Aspergillus spp, and yielded a significantly higher positive rate. With the final clinical diagnosis as the standard, the sensitivity of mNGS (excluding viruses) was 58.06%, significantly higher than that of blood culture (34.68%, P<0.001). Combing blood mNGS and culture results, the sensitivity improved to 72.58%. Forty-six patients had infected by mixed pathogens, among which Klebsiella pneumoniae and Acinetobacter baumannii contributed most. Compared to monomicrobial, cases with polymicrobial BSI exhibited dramatically higher level of SOFA, AST, hospitalized mortality and 90-day mortality (P<0.05). A total of 101 patients underwent antibiotics adjustment, among which 85 were adjusted according to microbiological results, including 45 cases based on the mNGS results (40 cases escalation and 5 cases de-escalation) and 32 cases on blood culture. Collectively, for patients suspected BSI in critical condition, mNGS results can provide valuable diagnostic information and contribute to the optimizing of antibiotic treatment. Combining conventional tests with mNGS may significantly improve the detection rate for pathogens and optimize antibiotic treatment in critically ill patients with BSI

Taxifolin increased semen quality of Duroc boars by improving gut microbes and blood metabolites.

peer reviewedTaxifolin (TAX), as a natural flavonoid, has been widely focused on due to its strong anti-oxidation, anti-inflammation, anti-virus, and even anti-tumor activity. However, the effect of TAX on semen quality was unknown. The purpose of this study was to analyze the beneficial influences of adding feed additive TAX to boar semen in terms of its quality and potential mechanisms. We discovered that TAX increased sperm motility significantly in Duroc boars by the elevation of the protein levels such as ZAG, PKA, CatSper, and p-ERK for sperm quality. TAX increased the blood concentration of testosterone derivatives, antioxidants such as melatonin and betaine, unsaturated fatty acids such as DHA, and beneficial amino acids such as proline. Conversely, TAX decreased 10 different kinds of bile acids in the plasma. Moreover, TAX increased "beneficial" microbes such as Intestinimonas, Coprococcus, Butyrivibrio, and Clostridium_XlVa at the Genus level. However, TAX reduced the "harmful" intestinal bacteria such as Prevotella, Howardella, Mogibacterium, and Enterococcus. There was a very close correlation between fecal microbes, plasma metabolites, and semen parameters by the spearman correlation analysis. Therefore, the data suggest that TAX increases the semen quality of Duroc boars by benefiting the gut microbes and blood metabolites. It is supposed that TAX could be used as a kind of feed additive to increase the semen quality of boars to enhance production performance