Whole-body magnetic resonance imaging at 0.05 Tesla.

Despite a half-century of advancements, global magnetic resonance imaging (MRI) accessibility remains limited and uneven, hindering its full potential in health care. Initially, MRI development focused on low fields around 0.05 Tesla, but progress halted after the introduction of the 1.5 Tesla whole-body superconducting scanner in 1983. Using a permanent 0.05 Tesla magnet and deep learning for electromagnetic interference elimination, we developed a whole-body scanner that operates using a standard wall power outlet and without radiofrequency and magnetic shielding. We demonstrated its wide-ranging applicability for imaging various anatomical structures. Furthermore, we developed three-dimensional deep learning reconstruction to boost image quality by harnessing extensive high-field MRI data. These advances pave the way for affordable deep learning-powered ultra-low-field MRI scanners, addressing unmet clinical needs in diverse health care settings worldwide.

Estimating pathological prognostic factors in epithelial ovarian cancers using apparent diffusion coefficients of functional tumor volume.

PURPOSE: To assess the utility of apparent diffusion coefficients (ADCs) of whole tumor volume (WTV) and functional tumor volume (FTV) in determining the pathologicalprognostic factors in epithelial ovarian cancers (EOCs). METHODS: A total of 155 consecutive patients who were diagnosed with EOC between January 2017 and August 2022 and underwent both conventional magnetic resonance imaging and diffusion-weighted imaging were assessed in this study. The maximum, minimum, and mean ADC values of the whole tumor (ADCwmax, ADCwmin, and ADCwmean, respectively) and functional tumor (ADCfmax, ADCfmin, and ADCfmean, respectively) as well as the WTV and FTV were derived from the ADC maps. The univariate and multivariate logistic regression analyses and receiver operating characteristic curve (ROC) analysis were used to assess the correlation between these ADC values and the pathological prognostic factors, namely subtypes, lymph node metastasis (LNM), Ki-67 index, and p53 expression. RESULTS: The ADCfmean value was significantly lower in type II EOC, LNM-positive, and high-Ki-67 index groups compared to the type I EOC, LNM-negative, and low-Ki-67 index groups (p ≤ 0.001). Similarly, the ADCwmean and ADCfmean values were lower in the mutant-p53 group compared to the wild-type-p53 group (p ≤ 0.001). Additionally, the ADCfmean showed the highest area under the ROC curve (AUC) for evaluating type II EOC (0.725), LNM-positive (0.782), and high-Ki-67 index (0.688) samples among the given ROC curves, while both ADCwmean and ADCfmean showed high AUCs for assessing p53 expression (0.694 and 0.678, respectively). CONCLUSION: The FTV-derived ADC values, especially ADCfmean, can be used to assess preoperative prognostic factors in EOCs.

Effect of a novel dwarfing mutant site on chromosome 4B on agronomic traits in common wheat.

The introduction of dwarfing genes triggered a wave of "green revolution". A number of wheats dwarfing genes have been reported in previous studies, and only a small fraction of these have been applied to production practices. Therefore, the development of novel dwarfing genes for wheat is of great value. In this study, a novel dwarfing site, Rht-yz, identified in the Yanzhan mutation, is located on chromosome 4B (30-33MB) and its mechanism of action is different from that of Rht-B1b (C-T mutation), but whether it affects the Rht-B1a (TraesCS4B02G043100) or other genes is unclear. Exogenously applied GA3 experiments showed that Rht-yz is one of the gibberellin-insensitive dwarf genes. The effects of the dwarf gene Rht-yz on agronomic traits in wheat were evaluated in the field using Yanzhan, Yanzhan mutations, F2:3 and F3:4 lines. The results showed that Rht-yz improved lodging resistance by reducing plant height, increasing diameter, wall thickness and mechanical strength of the basal stem. In terms of yield traits, Rht-yz had negative effects on tiller number plant-1, biomass plant-1 and yield plant-1, but had no significant effect on harvest index, 1000-kernel weight and spike traits. In addition, Rht-yz significantly increased crude protein, wet gluten and starch content. Therefore, the rational use of the new dwarfing site Rht-yz has potential and value in dwarf wheat breeding.

The volumetric ADC histogram analysis in differentiating stage IA endometrial carcinoma from endometrial polyp.

OBJECTIVE: This study aimed to explore the value of apparent diffusion coefficient (ADC) histogram based on whole lesion volume in distinguishing stage IA endometrial carcinoma from the endometrial polyp. METHODS: MRI of 108 patients with endometrial lesions confirmed by pathology were retrospectively analysed, including 65 cases of stage IA endometrial carcinoma and 43 cases of endometrial polyp. The volumetric ADC histogram metrics and general imaging features were evaluated and measured simultaneously. All the features were compared between the 2 groups. The receiver operating characteristic curve was utilized to evaluate the diagnostic performance. RESULTS: The mean, max, min, and percentiles (10th, 25th, 50th, 75th, 95th) ADC values of endometrial carcinoma were significantly lower than that of polyp (all P < .05). The skewness and kurtosis of ADC values in the endometrial carcinoma group were significantly higher than those in the endometrial polyp group, and the variance of ADC values in the endometrial carcinoma group was lower than those in the endometrial polyp group (all P < .05). Endometrial carcinoma demonstrated more obvious myometrial invasion combined with intralesion haemorrhage than polyp (all P < .05). The 25th percentile of ADC values achieved the largest areas under the curve (0.861) among all the ADC histogram metrics and general imaging features, and the sensitivity and specificity were 83.08% and 76.74%, with the cut-off value of 1.01 × 10-3 mm2/s. CONCLUSION: The volumetric ADC histogram analysis was an effective method in differentiating endometrial carcinoma from an endometrial polyp. The 25th percentile of ADC values has satisfactory performance for detecting malignancy in the endometrium. ADVANCES IN KNOWLEDGE: The ADC histogram metric based on whole lesion is a promising imaging-maker in differentiating endometrial benign and malignant lesions.

Solid Electrochemiluminescence Sensor by Immobilization of Emitter Ruthenium(II)tris(bipyridine) in Bipolar Silica Nanochannel Film for Sensitive Detection of Oxalate in Serum and Urine.

Convenient and highly sensitive detection of oxalate ions in body fluids is of crucial significance for disease prevention, diagnosis, and monitoring of treatment effectiveness. Establishing a simple solid-state electrochemiluminescence (ECL) sensing system for highly sensitive detection of oxalate ions is highly desirable. In this work, a solid ECL sensor was fabricated by immobilizing the commonly used emitter ruthenium(II)tris(bipyridine) (Ru(bpy)32+) on a double-layered bipolar silica nanochannel array film (bp-SNA)-modified electrode, enabling sensitive detection of oxalate ions in serum or urine samples. Cost-effective and readily available indium tin oxide (ITO) was used as the supporting electrode. Convenient fabrication of multiple negatively charged SNA (n-SNA)-modified ITO electrodes was achieved through the one-step Stöber solution growth method. Subsequently, a positive outer layer film (p-SNA) was rapidly prepared using an electrochemical-assisted self-assembly method. The double-layered bipolar silica nanochannel array film achieved stable immobilization of Ru(bpy)32+ on the electrode surface, facilitated by the electrostatic adsorption of Ru(bpy)32+ by n-SNA and the electrostatic repulsion by p-SNA. Utilizing oxalate ions as a co-reactant for Ru(bpy)32+, combined with the electrostatic enrichment of oxalate ions by p-SNA, the constructed sensor enabled highly sensitive detection of oxalate ions ranging from 1 nM to 25 µM and from 25 µM to 1 mM, with a detection limit (LOD) of 0.8 nM. The fabricated ECL sensor exhibited high selectivity and good stability, making it suitable for ECL detection of oxalate ions in serum and urine samples.

Robust EMI elimination for RF shielding-free MRI through deep learning direct MR signal prediction.

PURPOSE: To develop a new electromagnetic interference (EMI) elimination strategy for RF shielding-free MRI via active EMI sensing and deep learning direct MR signal prediction (Deep-DSP). METHODS: Deep-DSP is proposed to directly predict EMI-free MR signals. During scanning, MRI receive coil and EMI sensing coils simultaneously sample data within two windows (i.e., for MR data and EMI characterization data acquisition, respectively). Afterward, a residual U-Net model is trained using synthetic MRI receive coil data and EMI sensing coil data acquired during EMI signal characterization window, to predict EMI-free MR signals from signals acquired by MRI receive and EMI sensing coils. The trained model is then used to directly predict EMI-free MR signals from data acquired by MRI receive and sensing coils during the MR signal-acquisition window. This strategy was evaluated on an ultralow-field 0.055T brain MRI scanner without any RF shielding and a 1.5T whole-body scanner with incomplete RF shielding. RESULTS: Deep-DSP accurately predicted EMI-free MR signals in presence of strong EMI. It outperformed recently developed EDITER and convolutional neural network methods, yielding better EMI elimination and enabling use of few EMI sensing coils. Furthermore, it could work well without dedicated EMI characterization data. CONCLUSION: Deep-DSP presents an effective EMI elimination strategy that outperforms existing methods, advancing toward truly portable and patient-friendly MRI. It exploits electromagnetic coupling between MRI receive and EMI sensing coils as well as typical MR signal characteristics. Despite its deep learning nature, Deep-DSP framework is computationally simple and efficient.

Flavonoid 4,4'-dimethoxychalcone selectively eliminates senescent cells via activating ferritinophagy.

Flavonoids are bioactive natural polyphenolic compounds with health benefits, including anti-tumor, anti-inflammatory and anti-aging effects. Our previous studies revealed that a flavonoid 4,4'-dimethoxychalcone (DMC) induced ferroptosis via inhibiting ferrochelatase (FECH). However, the effect of DMC on cellular senescence is unknown. In the present study, we found that DMC treatment selectively eliminated senescent cells, and DMC alone or a combination of DMC and quercetin or dasatinib showed high efficiency in the clearance of senescent cells. We identified FECH was highly expressed in senescent cells compared to non-senescent cells. Mechanistically, we found that DMC inhibited FECH and induced ferritinophagy, which led to an increase of labile iron pool, triggering ferroptosis of senescent cells. Importantly, we found that DMC treatment prevented hair loss, improved motor coordination, and reduced the expression of several senescence-associated secretory phenotype factors (IL-6, IL-1ß, CXCL-10, and MMP12) in the liver of old mice. Collectively, we revealed that, through the induction of ferroptosis, DMC holds the promise as a new senolytics to prevent age-related pathologies.

Evaluation of pelvic structural abnormalities in primiparous women with stress urinary incontinence.

INTRODUCTION AND HYPOTHESIS: The objective was to evaluate the morphological characteristics of pelvic floor structure specific to de novo stress urinary incontinence (SUI) in primiparous women using three-dimensional (3D) reconstruction fusion technology based on static MRI combined with dynamic MRI. METHODS: Eighty-one primiparous women after the first vaginal delivery were studied, 40 with SUI and 41 without SUI. 3D reconstruction models based on static MRI were used to describe the anatomical abnormalities of pelvic floor tissues. Dynamic MRI was used to describe segmental activities of the urethra and vagina. The relationship between the morphometry and postpartum SUI was evaluated by logistic regression analysis and receiver operator characteristic curve. RESULTS: The differences in the distance from the bladder neck to the pubic symphysis (BSD), the angle between the posterior wall of the urethra and the anterior wall of the vagina, the width of the distal region of the vagina, urethral length, urethral compression muscle volume (CUV), and pubovisceral muscle volume, puborectal muscle volume, were measured, and except for the extremity of the anterior urethral wall, the total displacements (TDs) of the other sites between the two groups were statistically significant (p < 0.05). Logistic regression analysis showed that the BSD decreased, the CUV decreased, the TDs of the first site and the eighth site increment correlated significantly with postpartum SUI occurrence (p < 0.05). CONCLUSIONS: 3D reconstruction fusion technology provides an important support for a precise assessment of the pelvic floor dysfunction. The BSD, CUV, and iliococcygeus muscle volume have certain values in predicting de novo SUI after first vaginal birth.

Early cellular and molecular signatures correlate with severity of West Nile virus infection.

Infection with West Nile virus (WNV) drives a wide range of responses, from asymptomatic to flu-like symptoms/fever or severe cases of encephalitis and death. To identify cellular and molecular signatures distinguishing WNV severity, we employed systems profiling of peripheral blood from asymptomatic and severely ill individuals infected with WNV. We interrogated immune responses longitudinally from acute infection through convalescence employing single-cell protein and transcriptional profiling complemented with matched serum proteomics and metabolomics as well as multi-omics analysis. At the acute time point, we detected both elevation of pro-inflammatory markers in innate immune cell types and reduction of regulatory T cell activity in participants with severe infection, whereas asymptomatic donors had higher expression of genes associated with anti-inflammatory CD16+ monocytes. Therefore, we demonstrated the potential of systems immunology using multiple cell-type and cell-state-specific analyses to identify correlates of infection severity and host cellular activity contributing to an effective anti-viral response.

Deep learning enabled fast 3D brain MRI at 0.055 tesla.

In recent years, there has been an intensive development of portable ultralow-field magnetic resonance imaging (MRI) for low-cost, shielding-free, and point-of-care applications. However, its quality is poor and scan time is long. We propose a fast acquisition and deep learning reconstruction framework to accelerate brain MRI at 0.055 tesla. The acquisition consists of a single average three-dimensional (3D) encoding with 2D partial Fourier sampling, reducing the scan time of T1- and T2-weighted imaging protocols to 2.5 and 3.2 minutes, respectively. The 3D deep learning leverages the homogeneous brain anatomy available in high-field human brain data to enhance image quality, reduce artifacts and noise, and improve spatial resolution to synthetic 1.5-mm isotropic resolution. Our method successfully overcomes low-signal barrier, reconstructing fine anatomical structures that are reproducible within subjects and consistent across two protocols. It enables fast and quality whole-brain MRI at 0.055 tesla, with potential for widespread biomedical applications.

Pelvic floor parameters predict postpartum stress urinary incontinence: a prospective MRI study.

OBJECTIVE: To investigate the pelvic floor changes in primiparas with postpartum stress urinary incontinence (SUI) after vaginal delivery using pelvic floor MRI. MATERIALS AND METHODS: Fifty-two women were enrolled in the primiparous stress urinary incontinent (PSUI) group and 51 in the primiparous continent (PC) group. Thirty nulliparas were also recruited as the nulliparous control (NC) group. Levator ani muscle (LAM) injury, levator hiatus area (LHA), H-line, M-line, the distance from the bladder neck and cervix to the pubococcygeal line (B-PCL and U-PCL), levator plate angle, the anterior angle of the urethra, bladder neck descent, retrovesicourethral angle, functional urethral length, and a bladder neck funnel were evaluated on MRI images. Univariate and multivariate logistic regression analyses were used to explore anatomical predictors for SUI. RESULTS: The primiparas in the PSUI group showed more obvious LAM injuries than in the PC groups (p = 0.001). LAM function assessment: the PSUI group had larger LHA and shorter B-PCL and U-PCL than the other groups during straining. Assessment of urethral mobility and function: the PSUI group had larger anterior angle of the urethra, bladder neck descent, retrovesicourethral angle, and shorter functional urethral length than the other two groups (all p < 0.05). Up to 88.5% of primiparas in the PSUI group showed bladder funnel (p < 0.001). The logistic regression analysis showed that retrovesicourethral angle, functional urethral length, and the presence of bladder funnel were significantly associated with postpartum SUI (p < 0.05). CONCLUSIONS: Increased retrovesicourethral angle, shortened functional urethral length, and the presence of bladder funnel may be anatomical predictors for SUI in the early postpartum period. Urethral sphincter dysfunction plays an essential role in developing postpartum SUI. CRITICAL RELEVANCE STATEMENT: This study used several measurements to reflect the anatomical structure and functional changes of the pelvic floor to identify the best anatomical predictors associated with postpartum stress urinary incontinence (SUI), aiming to provide new insights into treatment strategies for postpartum SUI. KEY POINTS: • Increased retrovesicourethral angle, shortened functional urethral length, and the presence of bladder funnel are more commonly seen in primiparas with SUI. • The combination of retrovesicourethral angle, functional urethral length, and bladder funnel had the highest diagnostic performance in predicting postpartum SUI (AUC=0.947). • Urethral sphincter dysfunction may be the main pathophysiological foundation in SUI development.

The first chromosome-level Fallopia multiflora genome assembly provides insights into stilbene biosynthesis.

Fallopia multiflora (Thunb.) Harald, a vine belonging to the Polygonaceae family, is used in traditional medicine. The stilbenes contained in it have significant pharmacological activities in anti-oxidation and anti-aging. This study describes the assembly of the F. multiflora genome and presents its chromosome-level genome sequence containing 1.46 gigabases of data (with a contig N50 of 1.97 megabases), 1.44 gigabases of which was assigned to 11 pseudochromosomes. Comparative genomics confirmed that F. multiflora shared a whole-genome duplication event with Tartary buckwheat and then underwent different transposon evolution after separation. Combining genomics, transcriptomics, and metabolomics data to map a network of associated genes and metabolites, we identified two FmRS genes responsible for the catalysis of one molecule of p-coumaroyl-CoA and three molecules of malonyl-CoA to resveratrol in F. multiflora. These findings not only serve as the basis for revealing the stilbene biosynthetic pathway but will also contribute to the development of tools for increasing the production of bioactive stilbenes through molecular breeding in plants or metabolic engineering in microbes. Moreover, the reference genome of F. multiflora is a useful addition to the genomes of the Polygonaceae family.

Pushing the limits of low-cost ultra-low-field MRI by dual-acquisition deep learning 3D superresolution.

PURPOSE: Recent development of ultra-low-field (ULF) MRI presents opportunities for low-power, shielding-free, and portable clinical applications at a fraction of the cost. However, its performance remains limited by poor image quality. Here, a computational approach is formulated to advance ULF MR brain imaging through deep learning of large-scale publicly available 3T brain data. METHODS: A dual-acquisition 3D superresolution model is developed for ULF brain MRI at 0.055 T. It consists of deep cross-scale feature extraction, attentional fusion of two acquisitions, and reconstruction. Models for T1 -weighted and T2 -weighted imaging were trained with 3D ULF image data sets synthesized from the high-resolution 3T brain data from the Human Connectome Project. They were applied to 0.055T brain MRI with two repetitions and isotropic 3-mm acquisition resolution in healthy volunteers, young and old, as well as patients. RESULTS: The proposed approach significantly enhanced image spatial resolution and suppressed noise/artifacts. It yielded high 3D image quality at 0.055 T for the two most common neuroimaging protocols with isotropic 1.5-mm synthetic resolution and total scan time under 20 min. Fine anatomical details were restored with intrasubject reproducibility, intercontrast consistency, and confirmed by 3T MRI. CONCLUSION: The proposed dual-acquisition 3D superresolution approach advances ULF MRI for quality brain imaging through deep learning of high-field brain data. Such strategy can empower ULF MRI for low-cost brain imaging, especially in point-of-care scenarios or/and in low-income and mid-income countries.

Parallel imaging reconstruction using spatial nulling maps.

PURPOSE: To develop a robust parallel imaging reconstruction method using spatial nulling maps (SNMs). METHODS: Parallel reconstruction using null operations (PRUNO) is a k-space reconstruction method where a k-space nulling system is derived using null-subspace bases of the calibration matrix. ESPIRiT reconstruction extends the PRUNO subspace concept by exploiting the linear relationship between signal-subspace bases and spatial coil sensitivity characteristics, yielding a hybrid-domain approach. Yet it requires empirical eigenvalue thresholding to mask the coil sensitivity information and is sensitive to signal- and null-subspace division. In this study, we combine the concepts of null-subspace PRUNO and hybrid-domain ESPIRiT to provide a more robust reconstruction method that extracts null-subspace bases of calibration matrix to calculate image-domain SNMs. Multi-channel images are reconstructed by solving an image-domain nulling system formed by SNMs that contain both coil sensitivity and finite image support information, therefore, circumventing the masking-related procedure. The proposed method was evaluated with multi-channel 2D brain and knee data and compared to ESPIRiT. RESULTS: The proposed hybrid-domain method produced quality reconstruction highly comparable to ESPIRiT with optimal manual masking. It involved no masking-related manual procedure and was tolerant of the actual division of null- and signal-subspace. Spatial regularization could be also readily incorporated to reduce noise amplification as in ESPIRiT. CONCLUSION: We provide an efficient hybrid-domain reconstruction method using multi-channel SNMs that are calculated from coil calibration data. It eliminates the need for coil sensitivity masking and is relatively insensitive to subspace separation, therefore, presenting a robust parallel imaging reconstruction procedure in practice.

Dynamic evolution of brain structural patterns in liver transplantation recipients: a longitudinal study based on 3D convolutional neuronal network model.

OBJECTIVES: To evaluate the dynamic evolution process of overall brain health in liver transplantation (LT) recipients, we employed a deep learning-based neuroanatomic biomarker to measure longitudinal changes of brain structural patterns before and 1, 3, and 6 months after surgery. METHODS: Because of the ability to capture patterns across all voxels from a brain scan, the brain age prediction method was adopted. We constructed a 3D-CNN model through T1-weighted MRI of 3609 healthy individuals from 8 public datasets and further applied it to a local dataset of 60 LT recipients and 134 controls. The predicted age difference (PAD) was calculated to estimate brain changes before and after LT, and the network occlusion sensitivity analysis was used to determine the importance of each network in age prediction. RESULTS: The PAD of patients with cirrhosis increased markedly at baseline (+ 5.74 years) and continued to increase within one month after LT (+ 9.18 years). After that, the brain age began to decrease gradually, but it was still higher than the chronological age. The PAD values of the OHE subgroup were higher than those of the no-OHE, and the discrepancy was more obvious at 1-month post-LT. High-level cognition-related networks were more important in predicting the brain age of patients with cirrhosis at baseline, while the importance of primary sensory networks increased temporarily within 6-month post-LT. CONCLUSIONS: The brain structural patterns of LT recipients showed inverted U-shaped dynamic change in the early stage after transplantation, and the change in primary sensory networks may be the main contributor. KEY POINTS: • The recipients' brain structural pattern showed an inverted U-shaped dynamic change after LT. • The patients' brain aging aggravated within 1 month after surgery, and the subset of patients with a history of OHE was particularly affected. • The change of primary sensory networks is the main contributor to the change in brain structural patterns.

Electromagnetic interference elimination via active sensing and deep learning prediction for radiofrequency shielding-free MRI.

At present, MRI scans are typically performed inside fully enclosed radiofrequency (RF) shielding rooms, posing stringent installation requirements and causing patient discomfort. We aim to eliminate electromagnetic interference (EMI) for MRI with no or incomplete RF shielding. In this study, a method of active sensing and deep learning EMI prediction is presented to model, predict, and remove EMI signal components from acquired MRI signals. Specifically, during each MRI scan, separate EMI-sensing coils placed in various locations are utilized to simultaneously sample external and internal EMI signals within two windows (for both conventional MRI signal acquisition and EMI characterization acquisition). A convolution neural network model is trained using the EMI characterization data to relate EMI signals detected by EMI-sensing coils to EMI signals in the MRI receive coil. This model is then used to retrospectively predict and remove EMI signal components detected by the MRI receive coil during the MRI signal acquisition window. This strategy was implemented on a low-cost ultralow-field 0.055 T permanent magnet MRI scanner without RF shielding. It produced final image signal-to-noise ratios that were comparable with those obtained using a fully enclosed RF shielding cage, and outperformed existing analytical EMI elimination methods (i.e., spectral domain transfer function and external dynamic interference estimation and removal [EDITER] methods). A preliminary experiment also demonstrated its applicability on a 1.5 T superconducting magnet MRI scanner with incomplete RF shielding. Altogether, the results demonstrated that the proposed method was highly effective in predicting and removing various EMI signals from both external environments and internal scanner electronics at both 0.055 T (2.3 MHz) and 1.5 T (63.9 MHz). The proposed strategy enables shielding-free MRI. The concept is relatively simple and is potentially applicable to other RF signal detection scenarios in the presence of external and/or internal EMI.

Calibrationless reconstruction of uniformly-undersampled multi-channel MR data with deep learning estimated ESPIRiT maps.

PURPOSE: To develop a truly calibrationless reconstruction method that derives An Eigenvalue Approach to Autocalibrating Parallel MRI (ESPIRiT) maps from uniformly-undersampled multi-channel MR data by deep learning. METHODS: ESPIRiT, one commonly used parallel imaging reconstruction technique, forms the images from undersampled MR k-space data using ESPIRiT maps that effectively represents coil sensitivity information. Accurate ESPIRiT map estimation requires quality coil sensitivity calibration or autocalibration data. We present a U-Net based deep learning model to estimate the multi-channel ESPIRiT maps directly from uniformly-undersampled multi-channel multi-slice MR data. The model is trained using fully-sampled multi-slice axial brain datasets from the same MR receiving coil system. To utilize subject-coil geometric parameters available for each dataset, the training imposes a hybrid loss on ESPIRiT maps at the original locations as well as their corresponding locations within the standard reference multi-slice axial stack. The performance of the approach was evaluated using publicly available T1-weighed brain and cardiac data. RESULTS: The proposed model robustly predicted multi-channel ESPIRiT maps from uniformly-undersampled k-space data. They were highly comparable to the reference ESPIRiT maps directly computed from 24 consecutive central k-space lines. Further, they led to excellent ESPIRiT reconstruction performance even at high acceleration, exhibiting a similar level of errors and artifacts to that by using reference ESPIRiT maps. CONCLUSION: A new deep learning approach is developed to estimate ESPIRiT maps directly from uniformly-undersampled MR data. It presents a general strategy for calibrationless parallel imaging reconstruction through learning from the coil and protocol-specific data.

Fast and Calibrationless Low-Rank Parallel Imaging Reconstruction Through Unrolled Deep Learning Estimation of Multi-Channel Spatial Support Maps.

Low-rank technique has emerged as a powerful calibrationless alternative for parallel magnetic resonance (MR) imaging. Calibrationless low-rank reconstruction, such as low-rank modeling of local k-space neighborhoods (LORAKS), implicitly exploits both coil sensitivity modulations and the finite spatial support constraint of MR images through an iterative low-rank matrix recovery process. Although powerful, this slow iteration process is computationally demanding and reconstruction requires empirical rank optimization, hampering its robust applications for high-resolution volume imaging. This paper proposes a fast and calibrationless low-rank reconstruction of undersampled multi-slice MR brain data, based on the finite spatial support constraint reformulation with a direct deep learning estimation of spatial support maps. The iteration process of low-rank reconstruction is unrolled into a complex-valued network by training on fully-sampled multi-slice axial brain datasets acquired from the same MR coil system. To utilize coil-subject geometric parameters available for datasets, the model minimizes a hybrid loss on two sets of spatial support maps, corresponding to brain data at the original slice locations as actually acquired and nearby locations within the standard reference coordinate. This deep learning framework was integrated with LORAKS reconstruction and was evaluated with publically available gradient-echo T1-weighted brain datasets. It directly produced high-quality multi-channel spatial support maps from undersampled data, enabling rapid reconstruction without iteration. Moreover, it led to effective reductions of artifacts and noise amplification at high acceleration. In summary, our proposed deep learning framework offers a new strategy to advance the existing calibrationless low-rank reconstruction, rendering it computationally efficient, simple, and robust in practice.

Integrated untargeted metabolome, full-length sequencing, and transcriptome analyses reveal insights into the fruit quality at different harvest times of Chaenomeles speciosa.

Chaenomeles speciosa fruit is a homologous medicine and food plant with a long history of multiple uses. It could be harvested near maturity and last for a long time. However, the optimal harvest strategy of Chaenomeles speciosa for various uses is currently unavailable. Here, untargeted metabolome at different harvest times during maturation was investigated for the first time, and 896 metabolites, including sugars, organic acids, amino acids, and phenylpropanoids, were identified. Optimal harvesting methods were proposed for different purposes. During the early maturation stages (before 105 days after full bloom), Ch. speciosa fruit could be harvested as Chinesemedicine. Whereas as snacks and food, Ch. speciosa fruit might be harvested at late maturity (after 120 days after full bloom). In addition, the overall network was revealed by integrating full-length Iso-seq and transcriptomics (RNA-seq) to investigate the association between quality-associated metabolites and Chaenomeles speciosa fruit gene expression during maturation. A few putative genes were captured via screening, dissecting and correlation analysis with the quality-associated metabolites (including d-glucose, catechin, gallocatechin, and succinic acid). Overall, in addition to providing a harvesting strategy for food and medicine, we also investigated the metabolism and gene expression pattern of Chaenomeles speciosa fruit during maturation. This comprehensive data and analyses laid the foundation for further investigating potential regulatory mechanisms during harvest and provided a new possibility for its development and utilization.

Longitudinal changes in pelvic organ support among primiparas with postpartum pelvic organ prolapse: A follow-up magnetic resonance imaging study.

OBJECTIVE: To quantify morphological changes of pelvic floor in primiparas with postpartum pelvic organ prolapse (POP) during the early postpartum period. METHODS: A total of 309 primiparas underwent pelvic floor magnetic resonance imaging (MRI) at 6 weeks postpartum. Those primiparas diagnosed with postpartum POP by MRI criterion were followed up at 3 and 6 months postpartum. Normal primiparas were enrolled in the control group. The puborectal hiatus line, muscular pelvic floor relaxation line, levator hiatus area, iliococcygeus angle, levator plate angle, uterus-pubococcygeal line, and bladder-pubococcygeal line were assessed on MRI. Longitudinal changes in pelvic floor measurements between the two groups were compared by repeated-measures analysis of variance. RESULTS: Compared with the control group, enlarged puborectal hiatus line, levator hiatus area, and RICA and decreased uterus-pubococcygeal line were observed at rest in the POP group (all P < 0.05). All of the pelvic floor measurements were significantly different in the POP group compared with the control group at the maximum Valsalva maneuver (all P < 0.05). All of the pelvic floor measurements showed no significant change over time in both the POP and control groups (all P > 0.05). CONCLUSIONS: Postpartum POP accompanied by poor pelvic floor support will persist in the early postpartum period.