Knowledge-driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un-supervised learning.

Deep learning (DL) has emerged as a leading approach in accelerating MRI. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measurements. Unlike natural image restoration problems, MRI involves physics-based imaging processes, unique data properties, and diverse imaging tasks. This domain knowledge needs to be integrated with data-driven approaches. Our review will introduce the significant challenges faced by such knowledge-driven DL approaches in the context of fast MRI along with several notable solutions, which include learning neural networks and addressing different imaging application scenarios. The traits and trends of these techniques have also been given which have shifted from supervised learning to semi-supervised learning, and finally, to unsupervised learning methods. In addition, MR vendors' choices of DL reconstruction have been provided along with some discussions on open questions and future directions, which are critical for the reliable imaging systems.

Genomic analysis and biological characterization of a novel Schitoviridae phage infecting Vibrio alginolyticus.

Vibrio alginolyticus is a Gram-negative bacterium commonly associated with mackerel poisoning. A bacteriophage that specifically targets and lyses this bacterium could be employed as a biocontrol agent for treating the bacterial infection or improving the shelf-life of mackerel products. However, only a few well-characterized V. alginolyticus phages have been reported in the literature. In this study, a novel lytic phage, named ΦImVa-1, specifically infecting V. alginolyticus strain ATCC 17749, was isolated from Indian mackerel. The phage has a short latent period of 15 min and a burst size of approximately 66 particles per infected bacterium. ΦImVa-1 remained stable for 2 h at a wide temperature (27-75 °C) and within a pH range of 5 to 10. Transmission electron microscopy revealed that ΦImVa-1 has an icosahedral head of approximately 60 nm in diameter with a short tail, resembling those in the Schitoviridae family. High throughput sequencing and bioinformatics analysis elucidated that ΦImVa-1 has a linear dsDNA genome of 77,479 base pairs (bp), with a G + C content of ~ 38.72% and 110 predicted gene coding regions (106 open reading frames and four tRNAs). The genome contains an extremely large virion-associated RNA polymerase gene and two smaller non-virion-associated RNA polymerase genes, which are hallmarks of schitoviruses. No antibiotic genes were found in the ΦImVa-1 genome. This is the first paper describing the biological properties, morphology, and the complete genome of a V. alginolyticus-infecting schitovirus. When raw mackerel fish flesh slices were treated with ΦImVa-1, the pathogen loads reduced significantly, demonstrating the potential of the phage as a biocontrol agent for V. alginolyticus strain ATCC 17749 in the food. KEY POINTS: • A novel schitovirus infecting Vibrio alginolyticus ATCC 17749 was isolated from Indian mackerel. • The complete genome of the phage was determined, analyzed, and compared with other phages. • The phage is heat stable making it a potential biocontrol agent in extreme environments.

Aliasing-free reduced field-of-view parallel imaging.

PURPOSE: To reconstruct aliasing-free full field-of-view (FOV) images for reduced FOV (rFOV) parallel imaging (PI) with Cartesian and Wave sampling, which suffers from aliasing artifacts using existing PI methods. THEORY AND METHODS: The sensitivity encoding method (SENSE) was extended to the Soft-SENSE models supporting multiple-set coil sensitivity maps (CSM) and point spread functions (PSF) for Cartesian and Wave sampled rFOV PI, respectively. The multiple-set CSM and PSF were created from full FOV CSM and PSF according to the image folding process induced by rFOV sampling. The Soft-SENSE reconstructions could be solved by the same algorithms for the conventional full FOV SENSE reconstruction. RESULTS: Soft-SENSE using multiple-set full FOV CSM and PSF successfully reconstruct aliasing-free full FOV image from rFOV PI data with Cartesian and Wave sampling. The proposed rFOV PI enables flexible control of the aliasing and achieves comparable geometry factors as the standard full FOV PI with the same net acceleration factor. Reduced FOV PI improves the computational efficiency of iterative compressed sensing (CS) and PI reconstruction, especially for high-resolution volumetric imaging, thanks to the reduced fast Fourier transform (FFT) size. Moreover, rFOV PI reconstruction provides a potential alternative to the phase oversampling for the FOV aliasing problem. CONCLUSION: The proposed Soft-SENSE using full FOV CSM and PSF could reconstruct aliasing-free full FOV image for rFOV PI, and make it a viable solution enabling more flexible PI acceleration and effectively improving the computational efficiency of iterative CSPI reconstruction.

Fast multi-parametric imaging in abdomen by B 1 + corrected dual-flip angle sequence with interleaved echo acquisition.

PURPOSE: To achieve simultaneous T1, w /proton density fat fraction (PDFF)/ R 2 ∗ mapping in abdomen within a single breadth-hold, and validate the accuracy using state-of-art measurement. THEORY AND METHODS: An optimized multiple echo gradient echo (GRE) sequence with dual flip-angle acquisition was used to realize simultaneous water T1 (T1, w )/PDFF/ R 2 ∗ quantification. A new method, referred to as "solving the fat-water ambiguity based on their T1 difference" (SORT), was proposed to address the fat-water separation problem. This method was based on the finding that compared to the true solution, the wrong (or aliased) solution to fat-water separation problem showed extra dependency on the applied flip angle due to the T1 difference between fat and water. The B 1 + measurement sequence was applied to correct the B 1 + inhomogeneity for T1, w relaxometry. The 2D parallel imaging was incorporated to enable the acquisition within a single breath-hold in abdomen. RESULTS: The multi-parametric quantification results of the proposed method were consistent with the results of reference methods in phantom experiments (PDFF quantification: R2  = 0.993, mean error 0.73%; T1, w quantification: R2  = 0.999, mean error 4.3%; R 2 ∗ quantification: R2  = 0.949, mean error 4.07 s-1 ). For volunteer studies, robust fat-water separation was achieved without evident fat-water swaps. Based on the accurate fat-water separation, simultaneous T1, w /PDFF/ R 2 ∗ quantification was realized for whole liver within a single breath-hold. CONCLUSION: The proposed method accurately quantified T1, w /PDFF/ R 2 ∗ for the whole liver within a single breath-hold. This technique serves as a quantitative tool for disease management in patients with hepatic steatosis.

DIMENSION: Dynamic MR imaging with both k-space and spatial prior knowledge obtained via multi-supervised network training.

Dynamic MR image reconstruction from incomplete k-space data has generated great research interest due to its capability in reducing scan time. Nevertheless, the reconstruction problem is still challenging due to its ill-posed nature. Most existing methods either suffer from long iterative reconstruction time or explore limited prior knowledge. This paper proposes a dynamic MR imaging method with both k-space and spatial prior knowledge integrated via multi-supervised network training, dubbed as DIMENSION. Specifically, the DIMENSION architecture consists of a frequential prior network for updating the k-space with its network prediction and a spatial prior network for capturing image structures and details. Furthermore, a multi-supervised network training technique is developed to constrain the frequency domain information and the spatial domain information. The comparisons with classical k-t FOCUSS, k-t SLR, L+S and the state-of-the-art CNN-based method on in vivo datasets show our method can achieve improved reconstruction results in shorter time.

Application of machine learning model to predict osteoporosis based on abdominal computed tomography images of the psoas muscle: a retrospective study.

BACKGROUND: With rapid economic development, the world's average life expectancy is increasing, leading to the increasing prevalence of osteoporosis worldwide. However, due to the complexity and high cost of dual-energy x-ray absorptiometry (DXA) examination, DXA has not been widely used to diagnose osteoporosis. In addition, studies have shown that the psoas index measured at the third lumbar spine (L3) level is closely related to bone mineral density (BMD) and has an excellent predictive effect on osteoporosis. Therefore, this study developed a variety of machine learning (ML) models based on psoas muscle tissue at the L3 level of unenhanced abdominal computed tomography (CT) to predict osteoporosis. METHODS: Medical professionals collected the CT images and the clinical characteristics data of patients over 40 years old who underwent DXA and abdominal CT examination in the Second Affiliated Hospital of Wenzhou Medical University database from January 2017 to January 2021. Using 3D Slicer software based on horizontal CT images of the L3, the specialist delineated three layers of the region of interest (ROI) along the bilateral psoas muscle edges. The PyRadiomics package in Python was used to extract the features of ROI. Then Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) algorithm were used to reduce the dimension of the extracted features. Finally, six machine learning models, Gaussian naïve Bayes (GNB), random forest (RF), logistic regression (LR), support vector machines (SVM), Gradient boosting machine (GBM), and Extreme gradient boosting (XGBoost), were applied to train and validate these features to predict osteoporosis. RESULTS: A total of 172 participants met the inclusion and exclusion criteria for the study. 82 participants were enrolled in the osteoporosis group, and 90 were in the non-osteoporosis group. Moreover, the two groups had no significant differences in age, BMI, sex, smoking, drinking, hypertension, and diabetes. Besides, 826 radiomic features were obtained from unenhanced abdominal CT images of osteoporotic and non-osteoporotic patients. Five hundred fifty radiomic features were screened out of 826 by the Mann-Whitney U test. Finally, 16 significant radiomic features were obtained by the LASSO algorithm. These 16 radiomic features were incorporated into six traditional machine learning models (GBM, GNB, LR, RF, SVM, and XGB). All six machine learning models could predict osteoporosis well in the validation set, with the area under the receiver operating characteristic (AUROC) values greater than or equal to 0.8. GBM is more effective in predicting osteoporosis, whose AUROC was 0.86, sensitivity 0.70, specificity 0.92, and accuracy 0.81 in validation sets. CONCLUSION: We developed six machine learning models to predict osteoporosis based on psoas muscle images of abdominal CT, and the GBM model had the best predictive performance. GBM model can better help clinicians to diagnose osteoporosis and provide timely anti-osteoporosis treatment for patients. In the future, the research team will strive to include participants from multiple institutions to conduct external validation of the ML model of this study.

Development of a nomogram to predict surgical site infection after closed comminuted calcaneal fracture.

BACKGROUND: Compared with open comminuted calcaneal fractures, less emphasis is placed on postoperative surgical site infection (SSI) of closed comminuted calcaneal fractures. This study aimed to identify the risk factors associated with SSI and build a nomogram model to visualize the risk factors for postoperative SSI. METHODS: We retrospectively collected patients with closed comminuted calcaneal fractures from the Second Affiliated Hospital of Wenzhou Medical University database from 2017 to 2020. Risk factors were identified by logistics regression analysis, and the predictive value of risk factors was evaluated by ROC (receiver operating characteristic curve). Besides, the final risk factors were incorporated into R4.1.2 software to establish a visual nomogram prediction model. RESULTS: The high-fall injury, operative time, prealbumin, aspartate aminotransferase (AST), and cystatin-C were independent predictors of SSI in calcaneal fracture patients, with OR values of 5.565 (95%CI 2.220-13.951), 1.044 (95%CI 1.023-1.064), 0.988 (95%CI 0.980-0.995), 1.035 (95%CI 1.004-1.067) and 0.010 (95%CI 0.001-0.185) (Ps < 0.05). Furthermore, ROC curve analysis showed that the AUC values of high-fall injury, operation time, prealbumin, AST, cystatin-C, and their composite indicator for predicting SSI were 0.680 (95%CI 0.593-0.766), 0.756 (95%CI 0.672-939), 0.331 (95%CI 0.243-0.419), 0.605 (95%CI 0.512-0.698), 0.319 (95%CI 0.226-0.413) and 0.860 (95%CI 0.794-0.926), respectively (Ps < 0.05). Moreover, the accuracy of the nomogram to predict SSI risk was 0.860. CONCLUSIONS: Our study findings suggest that clinicians should pay more attention to the preoperative prealbumin, AST, cystatin C, high-fall injury, and operative time for patients with closed comminuting calcaneal fractures to avoid the occurrence of postoperative SSI. Furthermore, our established nomogram to assess the risk of SSI in calcaneal fracture patients yielded good accuracy and can assist clinicians in taking appropriate measures to prevent SSI.

Highly accelerated parallel MRI using wave encoding and virtual conjugate coils.

PURPOSE: To propose a novel model incorporating virtual conjugate coil (VCC) reconstruction and wave encoding (Wave) for improved parallel MRI. THEORY AND METHODS: A novel model (VCC-Wave) incorporating VCC and Wave is proposed. The correlation matrix of the encoding operator is introduced to analyze the encoding capability. In addition, simulation experiments are conducted to gain insights into VCC-Wave. In vivo experiments are performed to compare VCC-Wave with alternative methods. RESULTS: The correlation matrix and the simulation experiments show that the proposed VCC-Wave can utilize more priors of Wave under the VCC framework. In vivo experiments show that the proposed VCC-Wave can achieve good image quality at a 6-fold acceleration in high-resolution and high-bandwidth cases, indicating an improvement over the original Wave technique. CONCLUSION: The proposed VCC-Wave can not only combine the advantages of both the VCC and Wave but also exploit more priors of Wave under the VCC framework. The improvement in VCC-Wave alleviates the limitation of Wave in high-resolution and high-bandwidth cases.

A 4-minute solution for submillimeter whole-brain T1ρ quantification.

PURPOSE: To develop a robust, accurate, and accelerated T1ρ quantification solution for submillimeter in vivo whole-brain imaging. METHODS: A multislice T1ρ mapping solution (MS-T1ρ ) was developed based on a two-acquisition scheme using turbo spin echo with RF cycling to allow for whole-brain coverage with 0.8-mm in-plane resolution. A compressed sensing-based fast imaging method, SCOPE, was used to accelerate the MS-T1ρ acquisition time to a total scan time of 3 minutes 31 seconds. A phantom experiment was conducted to assess the accuracy of MS-T1ρ by comparing the T1ρ value obtained using MS-T1ρ with the reference value obtained using the standard single-slice T1ρ mapping method. In vivo scans of 13 volunteers were acquired prospectively to validate the robustness of MS-T1ρ . RESULTS: In the phantom study, the T1ρ values obtained with MS-T1ρ were in good agreement with the reference T1ρ values (R2 = 0.9991) and showed high consistency throughout all slices (coefficient of variation = 2.2 ± 2.43%). In the in vivo experiments, T1ρ maps were successfully acquired for all volunteers with no visually noticeable artifacts. There was no significant difference in T1ρ values between MS-T1ρ acquisitions and fully sampled acquisitions for all brain tissues (p-value > .05). In the intraclass correlation coefficient and Bland-Altman analyses, the accelerated T1ρ measurements show moderate to good agreement to the fully sampled reference values. CONCLUSION: The proposed MS-T1ρ solution allows for high-resolution whole-brain T1ρ mapping within 4 minutes and may provide a potential tool for investigating neural diseases.

Pyrolytic synthesis of organosilane-functionalized carbon nanoparticles for enhanced photocatalytic degradation of methylene blue under visible light irradiation.

The use of carbon-based nanomaterials as effective photocatalysts is an ideal alternative for environmental remediation. Here, (3-aminopropyl)triethoxysilane-functionalized carbon nanoparticles (SiCNPs) were prepared using a simple pyrolysis method with sodium citrate and urea as the precursors. The samples were characterized by X-ray diffraction, transmission electron microscopy, ultraviolet-visible diffuse reflectance spectroscopy, X-ray photoelectron spectroscopy, photoluminescence spectroscopy, and photo-electrochemical measures. The obtained SiCNPs-2.0 showed a better visible light response and more effective photocatalytic activity for degradation of methylene blue (MB) compared with pure CNPs. Under visible light irradiation, 98.8% of the MB was decomposed within 75 min when SiCNPs-2.0 was used as the photocatalyst. The high photocatalytic activities of SiCNPs-2.0 could be attributed to enhanced light absorption in the visible region, and improved photogenerated electron-hole separation efficiency. A possible photocatalytic mechanism for removal of MB over SiCNPs-2.0 was proposed based on active species trapping experiments. Recycling experiments showed that SiCNPs-2.0 had good stability during photocatalysis. This work provides a new easy method to synthesize carbon-based nanomaterials and to catalytically degrade organic pollutants in water under visible light irradiation.

Parameter optimization framework on wave gradients of Wave-CAIPI imaging.

PURPOSE: To propose a parameter optimization framework on wave gradients of Wave-CAIPI imaging for decreasing g-factor penalty and reducing reconstruction artifacts. THEORY AND METHODS: The influences of parameters on g-factor are theoretically analyzed. The average g-factor is chosen as a metric for parameter optimization, and then a fast calculation method is proposed to approximately and ultra-fast calculate the average g-factor. Based on this, a set of points in the function of the average g-factor with respect to the wave gradient parameters is calculated, and the optimal wave gradient parameters are found according to these points. RESULTS: In vivo human brain experiments were performed on 3T MR scanners for the comparison experiments. The results show that the proposed parameter optimization framework is able to efficiently obtain optimal wave gradient parameters, which can achieve decreased g-factor penalty and less artifacts of reconstructions than the empirical parameters. CONCLUSION: The proposed parameter optimization framework is computationally efficient and can optimize the wave gradient parameters of Wave-CAIPI imaging for better image quality than before.

Joint intracranial and carotid vessel wall imaging in 5 minutes using compressed sensing accelerated DANTE-SPACE.

OBJECTIVES: To compare visualization of joint intracranial and carotid vessel walls between 5× compressed sensing accelerated three-dimensional DANTE-SPACE sequence (CS VWI) acquired in 5 min and the same sequence accelerated by 2.7× parallel imaging (PI VWI) which takes 9-10 min currently. METHODS: Following institutional review board approval and informed consent, 28 subjects including 20 stroke patients underwent PI and CS VWI examinations with an acquired spatial resolution of isotropic 0.55 mm and joint coverage of intracranial and carotid arteries. Quantitative wall thickness measurements of CS VWI and PI VWI were compared on healthy volunteers and patients with wall thickening respectively. Subjective wall visualizations of the two VWI methods on patients were scored by two radiologists blindly and independently using a 4-point scale followed by inter-rater reproducibility analysis. RESULTS: Linear regression analysis of wall thickness measurements showed excellent agreement between CS VWI and PI VWI in both healthy volunteers (r = 0.99) and stroke patients with wall thickening (r = 0.99). Subjective wall visualization score of CS VWI was slightly lower than PI VWI (3.13 ± 0.41 vs. 3.31 ± 0.79) but still had good diagnostic quality (> 3 based on a 4-point scale). The two radiologists' scores agreed excellently, evidenced by the intraclass correlation coefficient (ICC) values being higher than 0.75 (p < 0.001). CONCLUSIONS: Compressed sensing expedients joint intracranial and carotid VWI acquired at an isotropic resolution of 0.55 mm in 5 min without compromising quantitative vessel wall thickness measurement or diagnostic wall visualization. KEY POINTS: • CS VWI facilitates comprehensive visualization of intracranial and carotid vessel walls at an acquired isotropic resolution of 0.55 mm in a single 5-min scan. • CS VWI affords comparable vessel wall visualization and morphology measurement as PI VWI with a shortened acquisition time by 45%. • CS VWI alleviates the intensive trade-off between imaging resolution and scan time, and benefits the scan efficiency, motion robustness, and patient tolerance of high-resolution joint intracranial and carotid VWI.

Taibaiella helva sp. nov., isolated from farmland soil in China.

A yellow-coloured bacterial strain, designated F-4T, was isolated from a farmland soil sample from Qianshan, Anhui province, China. Strain F-4T was Gram-stain-negative, strictly aerobic, oval-shaped, motile (by gliding) and non-spore-forming. Growth occurred at 20-35 °C (optimum, 30 °C), at pH 6.0-8.0 (pH 7.0) and with 0-1.0 % (w/v) NaCl (0 %). Phylogenetic analysis based on 16S rRNA gene sequences showed that strain F-4T belonged to the genus Taibaiella. 16S rRNA gene sequence similarity values between strain F-4T and the type strains of the three recognized species of the genus Taibaiella, Taibaiella koreensis KACC 17171T, Taibaiella soli KCTC 42277T and Taibaiella chishuiensis JCM 19637T, were 98.1, 96.4 and 95.9 %, respectively. The predominant respiratory quinone was MK-7, with MK-8 as a minor component. The major polar lipids of strain F-4T were three unidentified lipids, two unidentified aminolipids, three unidentified phospholipids, an unidentified aminophospholipid and phosphatidylethanolamine. The major cellular fatty acids were iso-C15 : 0, iso-C15 : 1 G and iso-C17 : 0. The G+C content of the genomic DNA based on total genome calculations was 51.3 mol%. The major polyamine of strain F-4T was homospermidine. The average nucleotide identity and the digital DNA-DNA hybridization values for draft genomes between strain F-4T and strain THG-DT86T were 79.8 and 22.6 %, respectively. On the basis of the genotypic and phenotypic data presented here, strain F-4T represents a novel species of the genus Taibaiella, for which the name Taibaiella helva sp. nov. is proposed. The type strain is F-4T (=KCTC 62442T=CGMCC 1.13562T).

Improved parallel image reconstruction using feature refinement.

PURPOSE: The aim of this study was to develop a novel feature refinement MR reconstruction method from highly undersampled multichannel acquisitions for improving the image quality and preserve more detail information. THEORY AND METHODS: The feature refinement technique, which uses a feature descriptor to pick up useful features from residual image discarded by sparsity constrains, is applied to preserve the details of the image in compressed sensing and parallel imaging in MRI (CS-pMRI). The texture descriptor and structure descriptor recognizing different types of features are required for forming the feature descriptor. Feasibility of the feature refinement was validated using three different multicoil reconstruction methods on in vivo data. RESULTS: Experimental results show that reconstruction methods with feature refinement improve the quality of reconstructed image and restore the image details more accurately than the original methods, which is also verified by the lower values of the root mean square error and high frequency error norm. CONCLUSION: A simple and effective way to preserve more useful detailed information in CS-pMRI is proposed. This technique can effectively improve the reconstruction quality and has superior performance in terms of detail preservation compared with the original version without feature refinement. Magn Reson Med 80:211-223, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Paenibacillus yanchengensis sp. nov., isolated from farmland soil.

A Gram-variable, short-rod-shaped, motile, spore-forming, strictly aerobic and alkaliresistant bacterium, designed strain J-3T, was isolated from farmland soil sampled in Yancheng city, Jiangsu province, China. Optimal growth occurred at 30 °C, pH 7.0-8.0 and 0.5 % (w/v) NaCl. Phylogenetic analysis based on the 16S rRNA gene sequences showed that strain J-3T was most closely related to Paenibacillusripae HZ1T (96.8 %), followed by Paenibacillussputi KIT00200-70066-1T (94.7 %). The major cellular fatty acids were anteiso-C15 : 0 and C16 : 0. The dominant respiratory quinone was menaquinone-7 and the DNA G+C content was 41.2 mol%. The major polar lipids of strain J-3T were aminolipid, phospholipid, diphosphatidylglycerol, phosphatidylglycerol, phosphoaminolipid and phosphatidylethanolamine. The diagnostic diamino acid of the cell-wall peptidoglycan was meso-diaminopimelic acid. On the basis of genotypic and phenotypic data, strain J-3T represents a novel species of the genus Paenibacillus, for which the name Paenibacillus yanchengensis sp. nov. is proposed. The type strain is J-3T (=KCTC 33926T=CGMCC 1.16455T).

Targeting P38 Pathway Regulates Bony Formation via MSC Recruitment during Mandibular Distraction Osteogenesis in Rats.

Distraction osteogenesis (DO) is a widely used self-tissue engineering. However, complications and discomfort due to the long treatment period are still the bottleneck of DO. Novel strategies to accelerate bone formation in DO are still needed. P38 is capable of regulating the osteogenic differentiation of both mesenchymal stem cells (MSCs) and osteoblasts, which are crucial to bone regeneration. However, it is not clear whether targeting p38 could regulate bony formation in DO. The purpose of the current work was to investigate the effects of local application of either p38 agonist anisomycin or p38 inhibitor SB203580 in a rat model of DO. 30 adult rats were randomly divided into 3 groups: (A) rats injected with DMSO served as the control group; (B) rats injected with p38 agonist anisomycin; (C) rats injected with p38 inhibitor SB203580. All the rats were subjected to mandibular distraction and the injection was performed daily during this period. The distracted mandibles were harvested on days 15 and 30 after surgery and subjected to the following analysis. Micro-computed tomography and histological evaluation results showed that local application of p38 agonist anisomycin increased new bone formation in DO, whereas p38 inhibitor SB203580 decreased it. Immunohistochemical analysis suggested that anisomycin promoted MSC recruitment in the distraction gap. In conclusion, this study demonstrated that local application of p38 agonist anisomycin can increase new bone formation during DO. This study may lead to a novel cell-based strategy for the improvement of bone regeneration.

Novel method for identifying the heaviest QED atom.

QED atoms are composed of unstructured and point-like lepton pairs bound together by the electromagnetic force. The smallest and heaviest QED atom is formed by a τ+τ- pair. Currently, the only known atoms of this type are the e+e- and µ+e- atoms, which were discovered 64 years ago and remain the sole examples found thus far. We demonstrate that the Jτ (τ+τ- atom with JPC=1--) atom signal can be observed with a significance larger than 5σ including both statistical and systematic uncertainties, via. the process e+e-→X+Y-Ɇ (X,Y=e,µ,π,K, or ρ, and Ɇ is the missing energy due to unobserved neutrinos) with 1.5ab-1 data taken around the τ pair production threshold. The τ lepton mass can be measured with a precision of 1 keV with the same data sample. This is within one year's running time of the proposed super tau-charm facility in China or super charm-tau factory in Russia.

Skeletal muscle index based on CT at the 12th thoracic spine level can predict osteoporosis and fracture risk: a propensity score-matched cohort study.

Background: Multiple studies have shown that skeletal muscle index (SMI) measured on abdominal computed tomography (CT) is strongly associated with bone mineral density (BMD) and fracture risk as estimated by the fracture risk assessment tool (FRAX). Although some studies have reported that SMI at the level of the 12th thoracic vertebra (T12) measured on chest CT images can be used to diagnose sarcopenia, it is regrettable that no studies have investigated the relationship between SMI at T12 level and BMD or fracture risk. Therefore, we further investigated the relationship between SMI at T12 level and FRAX-estimated BMD and fracture risk in this study. Methods: A total of 349 subjects were included in this study. After 1∶1 propensity score matching (PSM) on height, weight, hypertension, diabetes, hyperlipidemia, hyperuricemia, body mass index (BMI), age, and gender, 162 subjects were finally included. The SMI, BMD, and FRAX score of the 162 participants were obtained. The correlation between SMI and BMD, as well as SMI and FRAX, was assessed using Spearman rank correlation. Additionally, the effectiveness of each index in predicting osteoporosis was evaluated through the receiver operating characteristic (ROC) curve analysis. Results: The BMD of the lumbar spine (L1-4) demonstrated a strong correlation with SMI (r = 0.416, p < 0.001), while the BMD of the femoral neck (FN) also exhibited a correlation with SMI (r = 0.307, p < 0.001). SMI was significantly correlated with FRAX, both without and with BMD at the FN, for major osteoporotic fractures (r = -0.416, p < 0.001, and r = -0.431, p < 0.001, respectively) and hip fractures (r = -0.357, p < 0.001, and r = -0.311, p < 0.001, respectively). Moreover, the SMI of the non-osteoporosis group was significantly higher than that of the osteoporosis group (p < 0.001). SMI effectively predicts osteoporosis, with an area under the curve of 0.834 (95% confidence interval 0.771-0.897, p < 0.001). Conclusion: SMI based on CT images of the 12th thoracic vertebrae can effectively diagnose osteoporosis and predict fracture risk. Therefore, SMI can make secondary use of chest CT to screen people who are prone to osteoporosis and fracture, and carry out timely medical intervention.

DPP: deep phase prior for parallel imaging with wave encoding.

Objective.In Magnetic Resonance (MR) parallel imaging with virtual channel-expanded Wave encoding, limitations are imposed on the ability to comprehensively and accurately characterize the background phase. These limitations are primarily attributed to the calibration process relying solely on center low-frequency Auto-Calibration Signals (ACS) data for calibration.Approach.To tackle the challenge of accurately estimating the background phase in wave encoding, a novel deep neural network model guided by deep phase priors is proposed with integrated virtual conjugate coil (VCC) extension. Concretely, within the proposed framework, the background phase is implicitly characterized by employing a carefully designed decoder convolutional neural network, leveraging the inherent characteristics of phase smoothness and compact support in the transformed domain. Furthermore, the proposed model with wave encoding benefits from additional priors, which incorporate transmission sparsity of the latent image and coil sensitivity smoothness.Main results.Ablation experiments were conducted to ascertain the proposed method's capability to implicitly represent CSM and the background phase. Subsequently, the superiority of the proposed method is demonstrated through confidence comparisons with competing methods, employing 4-fold and 5-fold acceleration experiments. In achieving 4-fold and 5-fold acceleration, the optimal quantitative metrics (PSNR/SSIM/NMSE) are 44.1359 dB/0.9863/0.0008 (4-fold) and 41.2074/0.9846/0.0017 (5-fold), respectively. Furthermore, the generalizability of the proposed method is further validated by conducting acceleration experiments with T1, T2, T2*, and various undersampling patterns. In addition, the DPP delivered much better performance than the conventional methods by exploring accelerated phase-sensitive SWI imaging. In SWI accelerated imaging, it also surpasses the optimal competing method in terms of (PSNR/SSIM/NMSE) with 0.096%/0.009%/0.0017%.Significance.The proposed method enables precise characterization of the background phase in the integrated VCC and wave encoding framework, supported via theoretical analysis and empirical findings. Our code is available at:https://github.com/sober235/DPP.