SPFS: SNR peak-based frequency selection method to alleviate resolution degradation in MPI real-time imaging.

OBJECTIVE: The primary objective of this study is to address the reconstruction time challenge in Magnetic Particle Imaging (MPI) by introducing a novel approach named SNR-Peak-Based Frequency Selection (SPFS). The focus is on improving spatial resolution without compromising reconstruction speed, thereby enhancing the clinical potential of MPI for real-time imaging. APPROACH: To overcome the trade-off between reconstruction time and spatial resolution in MPI, the researchers propose SPFS as an innovative frequency selection method. Unlike conventional SNR-based selection, SPFS prioritizes frequencies with Signal-to-Noise Ratio (SNR) peaks that capture crucial system matrix information. This adaptability to varying quantities of selected frequencies enhances versatility in the reconstruction process. The study compares the spatial resolution of MPI reconstruction using both SNR-based and SPFS frequency selection methods, utilizing simulated and real device data. MAIN RESULTS: The research findings demonstrate that the SPFS approach substantially improves image resolution in Magnetic Particle Imaging, especially when dealing with a limited number of frequency components. By focusing on SNR peaks associated with critical system matrix information, SPFS mitigates the spatial resolution degradation observed in conventional SNR-based selection methods. The study validates the effectiveness of SPFS through the assessment of MPI reconstruction spatial resolution using both simulated and real device data, highlighting its potential to address a critical limitation in the field. SIGNIFICANCE: The introduction of SNR-Peak-Based Frequency Selection (SPFS) represents a significant breakthrough in MPI technology. The method not only accelerates reconstruction time but also enhances spatial resolution, thus expanding the clinical potential of MPI for various applications. The improved real-time imaging capabilities of MPI, facilitated by SPFS, hold promise for advancements in drug delivery, plaque assessment, tumor treatment, cerebral perfusion evaluation, immunotherapy guidance, and in vivo cell tracking.

Design Principles and Mechanistic Understandings of Non-Noble-Metal Bifunctional Electrocatalysts for Zinc-Air Batteries.

Zinc-air batteries (ZABs) are promising energy storage systems because of high theoretical energy density, safety, low cost, and abundance of zinc. However, the slow multi-step reaction of oxygen and heavy reliance on noble-metal catalysts hinder the practical applications of ZABs. Therefore, feasible and advanced non-noble-metal electrocatalysts for air cathodes need to be identified to promote the oxygen catalytic reaction. In this review, we initially introduced the advancement of ZABs in the past two decades and provided an overview of key developments in this field. Then, we discussed the working mechanism and the design of bifunctional electrocatalysts from the perspective of morphology design, crystal structure tuning, interface strategy, and atomic engineering. We also included theoretical studies, machine learning, and advanced characterization technologies to provide a comprehensive understanding of the structure-performance relationship of electrocatalysts and the reaction pathways of the oxygen redox reactions. Finally, we discussed the challenges and prospects related to designing advanced non-noble-metal bifunctional electrocatalysts for ZABs.

pH-responsive iron-loaded carbonaceous nanoparticles for chemodynamic therapy based on the Fenton reaction.

The Fenton reaction-based chemodynamic therapy is a form of cancer therapy, and its efficacy can be significantly improved by promoting catalytic reactions involving iron ions. A system with high catalytic capacity and low biological toxicity that effectively inhibits tumor progression is required for optimal treatment. In this study, iron-loaded carbonaceous nanoparticles (CNPs@Fe) with Fenton catalytic activity were fabricated and applied for the chemodynamic therapy of cancer. The carbonaceous nanoparticles derived from glucose via a caramelization reaction demonstrated high biocompatibility. Besides, aromatic structures in the carbonaceous nanoparticles helped accelerate electron transfer to enhance the catalytic decomposition of H2O2, resulting in the formation of highly reactive hydroxyl radicals (˙OH). At pH 6.0 (representing weak acidity in the tumor microenvironment), the Fenton catalytic activity of CNPs@Fe in the decomposition of H2O2 was 15.3 times higher than that of Fe2+ and 28.3 times higher than that of Fe3O4via a chromogenic reaction. The reasons for the enhancement were revealed by analyzing the chemical composition of carbonaceous nanoparticles using high-resolution mass spectra. The developed Fenton agent also demonstrated significant therapeutic effectiveness and minimal side effects in in vitro and in vivo anticancer studies. This work proposes a novel approach to promote the generation of reactive oxygen species (ROS) for the chemodynamic therapy of cancer.

Diffusion-weighted imaging-based radiomics model using automatic machine learning to differentiate cerebral cystic metastases from brain abscesses.

OBJECTIVES: To develop a radiomics model based on diffusion-weighted imaging (DWI) utilizing automated machine learning method to differentiate cerebral cystic metastases from brain abscesses. MATERIALS AND METHODS: A total of 186 patients with cerebral cystic metastases (n = 98) and brain abscesses (n = 88) from two clinical institutions were retrospectively included. The datasets (129 from institution A) were randomly portioned into separate 75% training and 25% internal testing sets. Radiomics features were extracted from DWI images using two subregions of the lesion (cystic core and solid wall). A thorough image preprocessing method was applied to DWI images to ensure the robustness of radiomics features before feature extraction. Then the Tree-based Pipeline Optimization Tool (TPOT) was utilized to search for the best optimized machine learning pipeline, using a fivefold cross-validation in the training set. The external test set (57 from institution B) was used to evaluate the model's performance. RESULTS: Seven distinct TPOT models were optimized to distinguish between cerebral cystic metastases and abscesses either based on different features combination or using wavelet transform. The optimal model demonstrated an AUC of 1.00, an accuracy of 0.97, sensitivity of 1.00, and specificity of 0.93 in the internal test set, based on the combination of cystic core and solid wall radiomics signature using wavelet transform. In the external test set, this model reached 1.00 AUC, 0.96 accuracy, 1.00 sensitivity, and 0.93 specificity. CONCLUSION: The DWI-based radiomics model established by TPOT exhibits a promising predictive capacity in distinguishing cerebral cystic metastases from abscesses.

Study of radiomics based on dual-energy CT for nuclear grading and T-staging in renal clear cell carcinoma.

INTRODUCTION: Clear cell renal cell carcinoma (ccRCC) is the most lethal subtype of renal cell carcinoma with a high invasive potential. Radiomics has attracted much attention in predicting the preoperative T-staging and nuclear grade of ccRCC. OBJECTIVE: The objective was to evaluate the efficacy of dual-energy computed tomography (DECT) radiomics in predicting ccRCC grade and T-stage while optimizing the models. METHODS: 200 ccRCC patients underwent preoperative DECT scanning and were randomized into training and validation cohorts. Radiomics models based on 70 KeV, 100 KeV, 150 KeV, iodine-based material decomposition images (IMDI), virtual noncontrasted images (VNC), mixed energy images (MEI) and MEI + IMDI were established for grading and T-staging. Receiver operating characteristic analysis and decision curve analysis (DCA) were performed. The area under the curve (AUC) values were compared using Delong test. RESULTS: For grading, the AUC values of these models ranged from 0.64 to 0.97 during training and from 0.54 to 0.72 during validation. In the validation cohort, the performance of MEI + IMDI model was optimal, with an AUC of 0.72, sensitivity of 0.71, and specificity of 0.70. The AUC value for the 70 KeV model was higher than those for the 100 KeV, 150 KeV, and MEI models. For T-staging, these models achieved AUC values of 0.83 to 1.00 in training and 0.59 to 0.82 in validation. The validation cohort demonstrated AUCs of 0.82 and 0.70, sensitivities of 0.71 and 0.71, and specificities of 0.80 and 0.60 for the MEI + IMDI and IMDI models, respectively. In terms of grading and T-staging, the MEI + IMDI model had the highest AUC in validation, with IMDI coming in second. There were statistically significant differences between the MEI + IMDI model and the 70 KeV, 100 KeV, 150 KeV, MEI, and VNC models in terms of grading (P < .05) and staging (P ≤ .001). DCA showed that both MEI + IDMI and IDMI models outperformed other models in predicting grade and stage of ccRCC. CONCLUSIONS: DECT radiomics models were helpful in grading and T-staging of ccRCC. The combined model of MEI + IMDI achieved favorable results.

CT­based radiomics analysis of consolidation characteristics in differentiating pulmonary disease of non­tuberculous mycobacterium from pulmonary tuberculosis.

Global incidence rate of non-tuberculous mycobacteria (NTM) pulmonary disease has been increasing rapidly. In some countries and regions, its incidence rate is higher than that of tuberculosis. It is easily confused with tuberculosis. The topic of this study is to identify two diseases using CT radioomics. The aim in the present study was to investigate the value of CT-based radiomics to analyze consolidation features in differentiation of non-tuberculous mycobacteria (NTM) from pulmonary tuberculosis (TB). A total of 156 patients (75 with NTM pulmonary disease and 81 with TB) exhibiting consolidation characteristics in Shandong Public Health Clinical Center were retrospectively analyzed. Subsequently, 305 regions of interest of CT consolidation were outlined. Using a random number generated via a computer, 70 and 30% of consolidations were allocated to the training and the validation cohort, respectively. By means of variance threshold, when investigating the effective radiomics features, SelectKBest and the least absolute shrinkage and selection operator regression method were employed for feature selection and combined to calculate the radiomics score. K-nearest neighbor (KNN), support vector machine (SVM) and logistic regression (LR) were used to analyze effective radiomics features. A total of 18 patients with NTM pulmonary disease and 18 with TB possessing consolidation characteristics in Jinan Infectious Disease Hospital were collected for external validation of the model. A total of three methods was used in the selection of 52 optimal features. For KNN, the area under the curve (AUC; sensitivity, specificity) for the training and validation cohorts were 0.98 (0.93, 0.94) and 0.90 (0.88, 083), respectively; for SVM, AUC was 0.99 (0.96, 0.96) and 0.92 (0.86, 0.85) and for LR, AUC was 0.99 (0.97, 0.97) and 0.89 (0.88, 0.85). In the external validation cohort, AUC values of models were all >0.84 and LR classifier exhibited the most significant precision, recall and F1 score (0.87, 0.94 and 0.88, respectively). LR classifier possessed the best performance in differentiating diseases. Therefore, CT-based radiomics analysis of consolidation features may distinguish NTM pulmonary disease from TB.

A stepwise strategy integrating dynamic stress CT myocardial perfusion and deep learning-based FFRCT in the work-up of stable coronary artery disease.

OBJECTIVES: To validate a novel stepwise strategy in which computed tomography-derived fractional flow reserve (FFRCT) is restricted to intermediate stenosis on coronary computed tomography angiography (CCTA) and computed tomography myocardial perfusion imaging (CT-MPI) was reserved for vessels with gray zone FFRCT values. MATERIALS AND METHODS: This retrospective study included 87 consecutive patients (age, 58 ± 10 years; 70% male) who underwent CCTA, dynamic CT-MPI, interventional coronary angiography (ICA), and fractional flow reserve (FFR) for suspected or known coronary artery disease. FFRCT was computed using a deep learning-based platform. Three stepwise strategies (CCTA + FFRCT + CT-MPI, CCTA + FFRCT, CCTA + CT-MPI) were constructed and their diagnostic performance was evaluated using ICA/FFR as the reference standard. The proportions of vessels requiring further ICA/FFR measurement based on different strategies were noted. Furthermore, the net reclassification index (NRI) was calculated to ascertain the superior model. RESULTS: The CCTA + FFRCT + CT-MPI strategy yielded the lowest proportion of vessels requiring additional ICA/FFR measurement when compared to the CCTA + FFRCT and CCTA + CT-MPI strategies (12%, 22%, and 24%). The CCTA + FFRCT + CT-MPI strategy exhibited the highest accuracy for ruling-out (91%, 84%, and 85%) and ruling-in (90%, 85%, and 85%) functionally significant lesions. All strategies exhibited comparable sensitivity for ruling-out functionally significant lesions and specificity for ruling-in functionally significant lesions (p > 0.05). The NRI indicated that the CCTA + FFRCT + CT-MPI strategy outperformed the CCTA + FFRCT strategy (NRI = 0.238, p < 0.001) and the CCTA + CT-MPI strategy (NRI = 0.233%, p < 0.001). CONCLUSIONS: The CCTA + FFRCT + CT-MPI stepwise strategy was superior to the CCTA + FFRCT strategy and CCTA+ CT-MPI strategy by minimizing unnecessary invasive diagnostic catheterization without compromising the agreement rate with ICA/FFR. CLINICAL RELEVANCE STATEMENT: Our novel stepwise strategy facilitates greater confidence and accuracy when clinicians need to decide on interventional coronary angiography referral or deferral, reducing the burden of invasive investigations on patients. KEY POINTS: • A stepwise CCTA + FFRCT + CT-MPI strategy holds promise as a viable method to reduce the need for invasive diagnostic catheterization, while maintaining a high level of agreement with ICA/FFR. • The CCTA + FFRCT + CT-MPI strategy performed better than the CCTA + FFRCT and CCTA + CT-MPI strategies. • A stepwise CCTA + FFRCT + CT-MPI strategy allows to minimize unnecessary invasive diagnostic catheterization and helps clinicians to referral or deferral for ICA/FFR with more confidence.

Combination of time domain-system matrix and x-space methods to reconstruct magnetic particle images with isotropic resolution.

Objective. Imaging of superparamagnetic iron oxide nanoparticles based on their non-linear response to alternating magnetic fields shows promise for imaging cells and vasculature in healthy and diseased tissue. Such imaging can be achieved through x-space reconstruction typically along a unidirectional Cartesian trajectory, which rapidly convolutes the particle distribution with a 'anisotropic blurring' point spread function (PSF), leading to images with anisotropic resolution.Approach. Here we propose combining the time domine-system matrix and x-space reconstruction methods into a forward model, where the output of the forward model is the PSF-blurred x-space reconstructed image. We then treat the blur as an inverse problem solved by Kaczmarz iteration.Main results. After we have proposed the method optimization, the normal resolution of simulation and device images has been increased from 3.5 mm and 5.25 mm to 1.5 mm and 3.25 mm, which has reached the level in the tangential resolution. Quantitative indicators of image quality such as PSNR and SSIM have also been greatly improved.Significance. Simulation and imaging of real phantoms indicate that our approach provides better isotropic resolution and image quality than the x-space method alone or other methods for removing PSF blur. Using our proposed method to optimize the image quality of x-space reconstructed images using unidirectional Cartesian trajectories, it will promote the clinical application of MPI in the future.

Mnox Nanoenzyme Armed CAR-NK Cells Enhance Solid Tumor Immunotherapy by Alleviating the Immunosuppressive Microenvironment.

Adoptively transferred cells usually suffer from exhaustion, limited expansion, and poor infiltration, partially attributing to the complicated immunosuppressive microenvironment of solid tumors. Therefore, it is necessary to explore more effective strategies to improve the poor tumor microenvironment (TME) to efficaciously deliver and support extrinsic effector cells in vivo. Herein, an intelligent biodegradable hollow manganese dioxide nanoparticle (MnOX ) that possesses peroxidase activity to catalyze excess H2 O2 in the TME to produce oxygen and relieve the hypoxia of solid tumors is developed. MnOX nanoenzymes modified with CD56 antibody could specifically bind CAR-NK (chimeric antigen receptor modified natural killer) cells. It is demonstrated that CAR-NK cells incorporated with MnOX nanoenzymes effectively infiltrate into tumor tissues with an improved TME, which results in superior antitumor activity in solid tumor-bearing mice. The antibody connection between MnOX nanoenzymes and CAR-NK endows the lowest efficient dosage of MnOX . This study features a smart synergistic immunotherapy approach for solid tumors using MnOX nanoenzyme-armed CAR-NK cells, which would provide a valuable tool for immunocyte therapy in solid tumors.

Abdominal adipose tissue and type 2 diabetic kidney disease: adipose radiology assessment, impact, and mechanisms.

Diabetic kidney disease (DKD) is a significant healthcare burden worldwide that substantially increases the risk of kidney failure and cardiovascular events. To reduce the prevalence of DKD, extensive research is being conducted to determine the risk factors and consequently implement early interventions. Patients with type 2 diabetes mellitus (T2DM) are more likely to be obese. Abdominal adiposity is associated with a greater risk of kidney damage than general obesity. Abdominal adipose tissue can be divided into different fat depots according to the location and function, including visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), perirenal adipose tissue (PAT), and renal sinus adipose tissue (RSAT), which can be accurately measured by radiology techniques, such as computed tomography (CT) and magnetic resonance imaging (MRI). Abdominal fat depots may affect the development of DKD through different mechanisms, and radiologic abdominal adipose characteristics may serve as imaging indicators of DKD risk. This review will first describe the CT/MRI-based assessment of abdominal adipose depots and subsequently describe the current studies on abdominal adipose tissue and DKD development, as well as the underlying mechanisms in patients of T2DM with DKD.

Value of CT-based radiomics in evaluating the response of bone metastases to systemic drug therapy in breast cancer patients.

BACKGROUND: This study aimed to investigate the value of nonenhanced computed tomography (CT)-based radiomics in determining disease progression in breast cancer patients with bone marrow metastases and to develop a model for assessing treatment efficacy. METHODS: A total of 134 breast cancer patients with bone metastases were enrolled from three hospitals. Nonenhanced CT was performed after two cycles of drug treatment. The images were categorized into an invalid and a valid group according to disease progression status. The largest osteolytic lesions' maximum cross-sections in the CT images were selected as regions of interest (ROIs) for feature extraction. Variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were used to reduce feature dimensionality. K-nearest neighbor algorithm (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), logistic regression (LR), and decision tree (DT) algorithms were trained to establish radiomics models. Receiver operating characteristic (ROC) curves were generated to evaluate the diagnostic performance of the models. RESULTS: The KNN classifier demonstrated the best performance compared to the random grouping method. In the validation group, the area under the ROC curve (AUC) was 0.810. In the cross-validation method, the RF classifier showed the best performance with an AUC of 0.84. CONCLUSION: Nonenhanced CT-based radiomics provides a promising method for evaluating the efficacy of systemic drug therapy in breast cancer patients with osteolytic bone metastases.

Perivascular fat attenuation index value and plaque volume increased in non-target lesions of coronary arteries after stenting.

BACKGROUND: Progression of non-target lesions (NTLs) after stenting has been reported and is associated with the triggering of an inflammatory response. The perivascular fat attenuation index (FAI) may be used as a novel imaging biomarker for the direct quantification of coronary inflammation. OBJECTIVES: To investigate whether FAI values can help identify changes in inflammation status in patients undergoing stent implantation, especially in NTLs. METHODS: Patients who underwent pre- and post-stenting coronary computed tomography angiography (CCTA) examination between January 2015 and February 2021 were consecutively enrolled. The pre- and post-stenting FAIs of the full coronary arteries were compared in both the non- and stent-implanted coronary arteries. Moreover, local FAI values were measured and compared between the NTLs and target lesions in the stent implantations. We also compared changes in plaque type and volume in NTLs before and after stenting. RESULTS: A total of 89 patients (mean age 61 years; male 59) were enrolled. The perivascular FAI values in the full coronary arteries decreased after stenting in both the non- and stent-implanted coronary arteries, similar to those in the target lesions. Conversely, the perivascular FAI values in the NTLs increased after stenting (p < 0.05). In addition, the plaque volumes significantly increased in the NTLs after stenting, regardless of whether they were non-calcified, mixed, or calcified (p < 0.05). CONCLUSION: Perivascular FAI values and plaque volumes increased in the NTLs after stenting. Perivascular FAI can be a promising imaging biomarker for monitoring coronary inflammation after stenting and facilitate long-term monitoring in clinical settings. CLINICAL RELEVANCE STATEMENT: Perivascular fat attenuation index, a non-invasive imaging biomarker, may help identify coronary arteries with high inflammation in non-target lesions and facilitate long-term monitoring, potentially providing an opportunity for more targeted treatment. KEY POINTS: • Perivascular fat attenuation index (FAI) values and plaque volumes increased in the non-target lesions (NTLs) after stenting, suggesting potential focal inflammation progression after stenting. However, stenting along with anti-inflammatory treatment ameliorated inflammation in the full coronary arteries. • Perivascular FAI, a non-invasive imaging biomarker, may help identify coronary arteries with high inflammation in NTLs and facilitate long-term monitoring, potentially providing an opportunity for more targeted treatment.

Co-registration of OPM-MCG signals with CT using optical scanning.

Magnetocardiography (MCG) can be used to noninvasively measure the electrophysiological activity of myocardial cells. The high spatial resolution of magnetic source localization can precisely determine the location of cardiomyopathy, which is of great significance for the diagnosis and treatment of cardiovascular disease. To perform magnetic source localization, MCG data must be co-registered with anatomical images. We propose a co-registration method that can be applied to OPM-MCG systems. In this method, the sensor array and the trunk of the subject are scanned using structured light-scanning technology, and the scan results are registered with the reconstructed structure using computed tomography (CT). This can increase the number of effective cloud points acquired and reduce the interference from respiratory motion. The scanning bed of the OPM-MCG system was modified to be consistent with the CT device, ensuring that the state of the body remains consistent between the cardiac magnetometry measurements and CT scans.

Magnetocardiography-based coronary artery disease severity assessment and localization using spatiotemporal features.

Objective.This study aimed to develop an automatic and accurate method for severity assessment and localization of coronary artery disease (CAD) based on an optically pumped magnetometer magnetocardiography (MCG) system.Approach.We proposed spatiotemporal features based on the MCG one-dimensional signals, including amplitude, correlation, local binary pattern, and shape features. To estimate the severity of CAD, we classified the stenosis as absence or mild, moderate, or severe cases and extracted a subset of features suitable for assessment. To localize CAD, we classified CAD groups according to the location of the stenosis, including the left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA), and separately extracted a subset of features suitable for determining the three CAD locations.Main results.For CAD severity assessment, a support vector machine (SVM) achieved the best result, with an accuracy of 75.1%, precision of 73.9%, sensitivity of 67.0%, specificity of 88.8%, F1-score of 69.8%, and area under the curve of 0.876. The highest accuracy and corresponding model for determining locations LAD, LCX, and RCA were 94.3% for the SVM, 84.4% for a discriminant analysis model, and 84.9% for the discriminant analysis model.Significance. The developed method enables the implementation of an automated system for severity assessment and localization of CAD. The amplitude and correlation features were key factors for severity assessment and localization. The proposed machine learning method can provide clinicians with an automatic and accurate diagnostic tool for interpreting MCG data related to CAD, possibly promoting clinical acceptance.

Semi-supervised medical image segmentation using cross-style consistency with shape-aware and local context constraints.

Despite the remarkable progress in semi-supervised medical image segmentation methods based on deep learning, their application to real-life clinical scenarios still faces considerable challenges. For example, insufficient labeled data often makes it difficult for networks to capture the complexity and variability of the anatomical regions to be segmented. To address these problems, we design a new semi-supervised segmentation framework that aspires to produce anatomically plausible predictions. Our framework comprises two parallel networks: shape-agnostic and shape-aware networks. These networks learn from each other, enabling effective utilization of unlabeled data. Our shape-aware network implicitly introduces shape guidance to capture shape fine-grained information. Meanwhile, shape-agnostic networks employ uncertainty estimation to further obtain reliable pseudo-labels for the counterpart. We also employ a cross-style consistency strategy to enhance the network's utilization of unlabeled data. It enriches the dataset to prevent overfitting and further eases the coupling of the two networks that learn from each other. Our proposed architecture also incorporates a novel loss term that facilitates the learning of the local context of segmentation by the network, thereby enhancing the overall accuracy of prediction. Experiments on three different datasets of medical images show that our method outperforms many excellent semi-supervised segmentation methods and outperforms them in perceiving shape. The code can be seen at https://github.com/igip-liu/SLC-Net.

Analysis of minimal levator ani hiatus area based on MRI in female adults without pelvic floor dysfunction at different age groups.

PURPOSE: To investigate the association of minimal levator ani hiatus area with age in female adults without pelvic floor dysfunction. METHODS: 532 female subjects aged 18 ~ 90 years without pelvic floor dysfunction, divided into four groups (Group A, 18 ~ 29 years old; Group B, 30 ~ 39 years old; Group C, 40 ~ 49 years old; Group D, ≥ 50 years old) based on age, underwent traditional pelvic two-dimensional (2D) T2-weighted imaging (T2WI) axial to the body (AxB) for measuring the minimal levator ani hiatus area. 39 female volunteers were re-recruited to undergo both traditional pelvic 2D T2WI AxB and three-dimensional (3D) T2WI. An axial plane parallel to the direction of the puborectalis muscle (AxPRM) was acquired based on 3D T2WI. The difference of levator ani hiatus area measured on AxB and AxPRM images in 39 female volunteers was compared by one-sample t test, to verify if minimal levator ani hiatus area can be acquired on the traditional pelvic 2D T2WI AxB images. Spearman analysis evaluated the association of minimal levator ani hiatus area with age and the rank-sum test analyzed the area differences among four age groups. RESULTS: Female age was positively correlated with minimal levator ani hiatus area (r = 0.23; p < 0.001). The minimal levator ani hiatus areas of 532 subjects were: 15.17 ± 1.77 cm2 in Group A, 15.52 ± 2.21 cm2 in Group B, 16.03 ± 2.16 cm2 in Group C, and 16.40 ± 2.10 cm2 in Group D. ANOVA showed significant statistical differences among four age groups (F = 7.519, p < 0.0001). Significant differences in minimal levator ani hiatus areas were found between Group A and Group C (p = 0.0491), Group A and Group D (p = 0.0007), and Group B and Group D (p < 0.001). There was no statistical difference in minimal levator ani hiatus areas measured on AxB and AxPRM images in 39 female volunteers (p = 0.1000). There were no statistical difference in minimal levator ani hiatus areas between nulliparous and multiparous group for each age group (all p > 0.05). CONCLUSIONS: Based on a large sample, this study summarized the minimum levator ani hiatus area of female adults without pelvic floor dysfunction in different age groups. We found significant differences among different age groups. In addition, a positive correlation was found between age and the minimum levator ani hiatus area. These findings can provide reference criteria for diagnosing pelvic organ prolapse in female adults of different age groups.

Application of DKI and IVIM imaging in evaluating histologic grades and clinical stages of clear cell renal cell carcinoma.

Purpose: To evaluate the value of quantitative parameters derived from diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) in differentiating histologic grades and clinical stages of clear cell renal cell carcinoma (ccRCC). Materials and methods: A total of 65 patients who were surgically and pathologically diagnosed as ccRCC were recruited in this study. In addition to routine renal magnetic resonance imaging examination, all patients underwent preoperative IVIM and DKI. The corresponding diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), mean diffusivity (MD), kurtosis anisotropy (KA), and mean kurtosis (MK) values were obtained. Independent-samples t-test or Mann-Whitney U test was used for comparing the differences in IVIM and DKI parameters among different histologic grades and clinical stages. The diagnostic efficacy of IVIM and DKI parameters was evaluated using the receiver operating characteristic (ROC) curve. Spearman's correlation analysis was used to separately analyze the correlation of each parameter with histologic grades and stages of ccRCC. Results: The D and MD values were significantly higher in low-grade ccRCC than high-grade ccRCC (all p < 0.001) and in low-stage than high-stage ccRCC (all p < 0.05), and the f value of high-stage ccRCC was lower than that of low-stage ccRCC (p = 0.007). The KA and MK values were significantly higher in low-grade than high-grade ccRCC (p = 0.000 and 0.000, respectively) and in low-stage than high-stage ccRCC (p = 0.000 and 0.000, respectively). The area under the curve (AUC) values of D, D*, f, MD, KA, MK, DKI, and IVIM+DKI values were 0.825, 0.598, 0.626, 0.792, 0.750, 0.754, 0.803, and 0.857, respectively, in grading ccRCC and 0.837, 0.719, 0.710, 0.787, 0.796, 0.784, 0.864, 0.823, and 0.916, respectively, in staging ccRCC. The AUC of IVIM was 0.913 in staging ccRCC. The D, D*, and MD values were negatively correlated with the histologic grades and clinical stages (all p < 0.05), and the KA and MK values showed a positive correlation with histologic grades and clinical stages (all p < 0.05). The f value was also negatively correlated with the ccRCC clinical stage (p = 0.008). Conclusion: Both the IVIM and DKI values can be used preoperatively to predict the degree of histologic grades and stages in ccRCC, and the D and MD values have better diagnostic performance in the grading and staging. Also, further slightly enhanced diagnostic efficacy was observed in the model with combined IVIM and DKI parameters.

Assessment of functional and structural brain abnormalities with resting-state functional MRI in patients with vestibular neuronitis.

BACKGROUND: Vestibular neuritis (VN) is a disorder manifesting as acute, isolated, spontaneous vertigo. There are few comprehensive studies on the changes in related functional and structural brain regions. PURPOSE: To evaluate alterations in spontaneous neural activity, functional connectivity (FC), and gray matter volume (GMV) in patients with VN. MATERIAL AND METHODS: A total of 24 patients with VN and 22 age- and sex-matched healthy controls underwent resting-state functional magnetic resonance imaging (rs-fMRI) and three-dimensional T1-weighted anatomical imaging. We calculated the amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) to discern local brain abnormalities. The most abnormal brain region was selected as the region of interest (ROI) for FC analysis based on ALFF and ReHo values after Bonferroni correction. Voxel-based morphometry (VBM) was used to assess differences in GMV. RESULTS: Patients with VN, compared to healthy controls, showed increased ALFF (P < 0.001), ReHo values (P = 0.002, <0.001), and DC (P = 0.013) in the left lingual gyrus and right postcentral gyrus. FC analysis demonstrated enhanced connectivity between the left lingual gyrus and the left superior frontal gyrus, and decreased connectivity with the right insula gyrus, right and left supramarginal gyrus (P = 0.012, 0.004, <0.001, 0.014). In addition, GMV was reduced in the bilateral caudate (P = 0.022, 0.014). CONCLUSIONS: Patients with VN exhibit abnormal spontaneous neural activity and changes in ALFF, ReHo, DC, GMV, and FC. Understanding these functional and structural brain abnormalities may elucidate the underlying mechanisms of VN.

Evaluation of the relations between reproduction-related pituitary and ovarian hormones and abdominal fat area-related variables determined with computed tomography in overweight or obese women who have undergone bariatric surgery: a cross-sectional study.

Background: An understanding of the associations between midregion fat depots and systemic hormone levels will be crucial for developing health-promotion messages aimed at overweight or obese women. However, related research in this area is rare. The present study was performed to identify and quantify fat-related reproduction pituitary and ovarian hormones in overweight or obese women. Methods: A total of 250 eligible overweight or obese women scheduled to undergo laparoscopic sleeve gastrectomy (LSG) from a single center were retrospectively included in this study. Computed tomography (CT) images at the level of the umbilicus were selected, and abdominal fat areas were measured and calculated. The reproduction-related pituitary and ovarian hormones were also measured. The correlations among the parameters were examined using Spearman correlation test. Multiple linear regression analysis was performed after log and ß-transformation of the hormone levels and fat area-related variables. Results: Positive correlations were detected for prolactin (PRL) with total fat area (TFA) [ß=0.045; P=0.029; 95% confidence interval (CI): 0.004-0.085] and subcutaneous fat area (SFA) (ß=0.066; P=0.023; 95% CI: 0.009-0.123), whereas estradiol showed a negative correlation with visceral fat area (VFA) (ß=-0.056, P=0.005; 95% CI: -0.096 to -0.017) and relative VFA (rVFA) (ß=-0.068; P=0.001; 95% CI: -0.109 to -0.027) and a positive correlation with SFA (ß=0.036; P=0.042; 95% CI: 0.001-0.071). Progesterone (PROG) was negatively correlated with both VFA (ß=-0.037; P=0.002; 95% CI: -0.061 to -0.013) and rVFA (ß=-0.039; P=0.002; 95% CI: -0.063 to -0.014). The final results revealed that TFA was increased by 3.1% and SFA was increased by 4.7% with a doubling of PRL concentration; VFA was reduced by 2.5% and rVFA was reduced by 2.6% with a doubling of PROG concentration; and VFA was reduced by 3.8%, rVFA was reduced by 4.6%, and SFA was increased by 2.5% with a doubling of estradiol concentration. Conclusions: There exist certain associations between some reproduction-related pituitary and ovarian hormones and fat areas. Our findings provide new insights into the associations between midregion fat depots and systemic hormone levels in overweight or obese women.