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

The Numerical Invariant Measure of Stochastic Differential Equations With Markovian Switching

The existence and uniqueness of the numerical invariant measure of the backward Euler-Maruyama method for stochastic differential equations with Markovian switching is yielded, and it is revealed that the numerical invariant measure converges to the underlying invariant measure in the Wasserstein metric. Under the polynomial growth condition of drift term the convergence rate is estimated. The global Lipschitz condition on the drift coefficients required by Bao et al., 2016 and Yuan et al., 2005 is released. Several examples and numerical experiments are given to verify our theory.Comment: 25 pages, 4 figure

Strong convergence and asymptotic stability of explicit numerical schemes for nonlinear stochastic differential equations

In this article we introduce a number of explicit schemes, which are amenable to Khasminski’s technique and are particularly suitable for highly nonlinear stochastic differential equations (SDEs). We show that without additional restrictions to those which guarantee the exact solutions possess their boundedness in expectation with respect to certain Lyapunov-type functions, the numerical solutions converge strongly to the exact solutions in finite-time. Moreover, based on the convergence theorem of nonnegative semimartingales, positive results about the ability of the explicit numerical scheme proposed to reproduce the well-known LaSalle-type theorem of SDEs are proved here, from which we deduce the asymptotic stability of numerical solutions. Some examples are discussed to demonstrate the validity of the new numerical schemes and computer simulations are performed to support the theoretical results

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

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

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

The effect of hamstring donor-site block for functional outcomes and rehabilitation after anterior cruciate ligament reconstruction

PurposeTo determine the effect of local infiltration anesthesia (LIA) at the donor site combined with a femoral nerve block (FNB) on short-term postoperative pain, functional outcomes, and rehabilitation after arthroscopic hamstring tendon autograft anterior cruciate ligament reconstruction (ACLR).MethodsThis study was a single center, randomized controlled trial. Seventy-three subjects with ACL rupture were enrolled. Participants were randomly allocated to two groups, 47 in the experimental group (Group A) and 26 in the control group (Group B). All operations were performed under FNB. In Group A, 10 ml of 1% ropivacaine was injected precisely at the hamstring donor site. Patients in Group B were treated with the same amount of saline. Preoperatively and postoperatively, pain scores based on the numerical rating scale (NRS) and consumption of opioids were recorded. In addition, knee functions were assessed by the International Knee Documentation Committee Subjective Knee Form (IKDC), the Lysholm score, and the Knee injury and Osteoarthritis Outcome Score (KOOS) preoperatively and postoperatively at 1 and 3 months. In addition, we applied the KNEELAX3 arthrometer to evaluate the stability of the knee preoperatively and postoperatively so that subjective and objective knee conditions were obtained to help us assess knee recovery in a comprehensive manner.ResultsThe hamstring donor-site block reduced pain within the first 12 postoperative hours. There were no significant differences between two groups in pain intensity preoperatively and equal to or greater than 24 hours postoperatively. Furthermore, there were no differences between the groups concerning knee functions preoperatively or in the short-term follow-up at 1 and 3 months.ConclusionLIA at the donor site can effectively improve the early postoperative pain of patients after ACLR and reduce the use of opioids without affecting the functional outcomes of the surgery

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