Risk factors for thrombotic events in systemic lupus erythematosus patients with antiphospholipid antibodies: insights from morphometric measurements of carotid arteries.

Objective: To identify the correlation between thrombosis and atherosclerosis in systemic lupus erythematosus (SLE) patients with antiphospholipid antibodies (aPLs) (SLE/aPLs) through high-resolution magnetic resonance imaging (HR-MRI) of the carotid artery. Methods: A single-center, cross-sectional study was conducted. We collected consecutive patients with SLE/aPLs and healthy controls who underwent carotid HR-MRI examinations. The morphometric characteristics of the common carotid artery (CCA), internal carotid artery (ICA), external carotid artery (ECA), and carotid bulb (Sinus) were measured, and the differences in morphometric parameters between different groups were analyzed. Results: A total of 144 carotid arteries were analyzed. Compared with the control group, the wall area, wall thickness (WT and WTmax), and normalized wall index of CCA, ICA, ECA, and Sinus were increased in patients with SLE/aPLs, and the total vascular area (TVA) of CCA, ICA, and Sinus, and the bifurcation angle (BIFA) of ICA-ECA were also increased. A negative lupus anticoagulant (LAC) (with or without positive anticardiolipin antibody (aCL) or anti-ß2glycoprotein antibody (aß2GPI)) contributed to illustrating lower increased TVA and thickened vessel walls of CCA and ICA in SLE/aPLs patients without thrombotic events. Logistic regression analysis showed that WTmaxSinus and WTmaxGlobal were independent risk factors for thrombotic events in SLE/aPLs patients. The receiver operator characteristic curve showed that the cut-off value of WTmaxSinus was 2.855 mm, and WTmaxGlobal was 3.370 mm. Conclusion: HR-MRI ensures the complete and accurate measurement of carotid morphometric parameters. Compared with the control group, the carotid artery in patients with SLE/aPLs is mainly characterized by diffusely thickened vessel walls, and the patients with thrombotic events showed additional higher vascular area of CCA and ICA, and BIFA of ICA-ECA without significant change in lumen area. The carotid arteries of SLE/aPLs patients with thrombotic events exhibited significant vessel wall thickening in all segments except ECA compared to those without thrombotic events. LAC-negative and non-thrombotic events distinguish relatively early atherosclerosis in the carotid arteries in patients with SLE/aPLs. Patients with SLE/aPLs that possess circumscribed thickened carotid vessel walls (>3.370 mm), particularly thickened at the Sinus (>2.855 mm), may require management strategies for the risk of thrombotic events.

Decoding N400m Evoked Component: A Tutorial on Multivariate Pattern Analysis for OP-MEG Data.

Multivariate pattern analysis (MVPA) has played an extensive role in interpreting brain activity, which has been applied in studies with modalities such as functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG) and Electroencephalography (EEG). The advent of wearable MEG systems based on optically pumped magnetometers (OPMs), i.e., OP-MEG, has broadened the application of bio-magnetism in the realm of neuroscience. Nonetheless, it also raises challenges in temporal decoding analysis due to the unique attributes of OP-MEG itself. The efficacy of decoding performance utilizing multimodal fusion, such as MEG-EEG, also remains to be elucidated. In this regard, we investigated the impact of several factors, such as processing methods, models and modalities, on the decoding outcomes of OP-MEG. Our findings indicate that the number of averaged trials, dimensionality reduction (DR) methods, and the number of cross-validation folds significantly affect the decoding performance of OP-MEG data. Additionally, decoding results vary across modalities and fusion strategy. In contrast, decoder type, resampling frequency, and sliding window length exert marginal effects. Furthermore, we introduced mutual information (MI) to investigate how information loss due to OP-MEG data processing affect decoding accuracy. Our study offers insights for linear decoding research using OP-MEG and expand its application in the fields of cognitive neuroscience.

Source localization comparison and combination of OPM-MEG and fMRI to detect sensorimotor cortex responses.

BACKGROUND AND OBJECTIVES: The exploration of various neuroimaging techniques have become focal points within the field of neuroscience research. Magnetoencephalography based on optically pumped magnetometers (OPM-MEG) has shown significant potential to be the next generation of functional neuroimaging with the advantages of high signal intensity and flexible sensor arrangement. In this study, we constructed a 31-channel OPM-MEG system and performed a preliminary comparison of the temporal and spatial relationship between magnetic responses measured by OPM-MEG and blood-oxygen-level-dependent signals detected by functional magnetic resonance imaging (fMRI) during a grasping task. METHODS: For OPM-MEG, the ß-band (15-30 Hz) oscillatory activities can be reliably detected across multiple subjects and multiple session runs. To effectively localize the inhibitory oscillatory activities, a source power-spectrum ratio-based imaging method was proposed. This approach was compared with conventional source imaging methods, such as minimum norm-type and beamformer methods, and was applied in OPM-MEG source analysis. Subsequently, the spatial and temporal responses at the source-level between OPM-MEG and fMRI were analyzed. RESULTS: The effectiveness of the proposed method was confirmed through simulations compared to benchmark methods. Our demonstration revealed an average spatial separation of 10.57 ± 4.41 mm between the localization results of OPM-MEG and fMRI across four subjects. Furthermore, the fMRI-constrained OPM-MEG localization results indicated a more focused imaging extent. CONCLUSIONS: Taken together, the performance exhibited by OPM-MEG positions it as a potential instrument for functional surgery assessment.

Patients With Type 2 Diabetes Mellitus and Early Diabetic Kidney Disease Exhibit Lower Computed Tomography-measured Skeletal Muscle Attenuation Values: A Propensity Score-matched Study.

OBJECTIVE: To investigate the association between computed tomography-measured quality characteristics of skeletal muscle (SM) and early diagnosis of diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM). METHODS: This retrospective study included patients diagnosed with T2DM, with and without early DKD, between January 2019 and December 2021. To reduce potential bias, propensity score matching (PSM) was performed. The area and computed tomography attenuation values for SM and different abdominal adipose depots were measured. After PSM, logistic and multiple linear regression analyze were performed to analyse risk factors for early DKD. RESULTS: A total of 267 patients were enrolled (mean age, 61.67 years ± 10.87; 155 men) and divided into two groups: T2DM with early DKD (n = 133); and T2DM without DKD (n = 134). After PSM, 230 patients were matched (T2DM with early DKD [n = 115]; and T2DM without DKD [n = 115]), with no statistical differences in general characteristics between the two groups (P > .05). In multivariate logistic regression analysis, high-density lipoprotein cholesterol (odds ratio [OR] 0.14; 95% confidence interval [CI] 0.04-0.49; P = .002), uric acid (OR 1.01; 95% CI 1.00-1.01; P = .006), and SM attenuation value (OR 0.94; 95% CI 0.90-0.98; P = .003) were independent risk factors for early DKD. Multiple linear regression analysis revealed significant correlations between SM attenuation value and cystatin C (ß = -0.39, P = .004), urine albumin-to-creatinine ratio (ß = -0.26, P = .026), and estimated glomerular filtration rate (ß = 0.31 P = .009) after adjustment for confounders. CONCLUSION: Patients with T2DM and lower SM attenuation values may exhibit a higher risk for early DKD than those with higher values, which provides a potential imaging biomarker for early DKD diagnosis.

Prognostic significance and immune landscape of a cell cycle progression-related risk model in bladder cancer.

BACKGROUND: A greater emphasis has been placed on the part of cell cycle progression (CCP) in cancer in recent years. Nevertheless, the precise connection between CCP-related genes and bladder cancer (BCa) has remained elusive. This study endeavors to establish and validate a reliable risk model incorporating CCP-related factors, aiming to predict both the prognosis and immune landscape of BCa. METHODS: Clinical information and RNA sequencing data were collected from the GEO and TCGA databases. Univariate and multivariate Cox regression analyses were conducted to construct a risk model associated with CCP. The performance of the model was assessed using ROC and Kaplan-Meier survival analyses. Functional enrichment analysis was employed to investigate potential cellular functions and signaling pathways. The immune landscape was characterized using CIBERSORT algorithms. Integration of the risk model with various clinical variables led to the development of a nomogram. RESULTS: To build the risk model, three CCP-related genes (RAD54B, KPNA2, and TPM1) were carefully chosen. ROC and Kaplan-Meier survival analysis confirm that our model has good performance. About immunological infiltration, the high-risk group showed decreased levels of regulatory T cells and dendritic cells coupled with increased levels of activated CD4 + memory T cells, M2 macrophages, and neutrophils. Furthermore, the nomogram showed impressive predictive power for OS at 1, 3, and 5 years. CONCLUSION: This study provides new insights into the association between the CCP-related risk model and the prognosis of BCa, as well as its impact on the immune landscape.

Ultrasmall Fe3O4 nanoparticles self-assembly induced dual-mode T1/T2-weighted magnetic resonance imaging and enhanced tumor synergetic theranostics.

Individual theranostic agents with dual-mode MRI responses and therapeutic efficacy have attracted extensive interest due to the real-time monitor and high effective treatment, which endow the providential treatment and avoid the repeated medication with side effects. However, it is difficult to achieve the integrated strategy of MRI and therapeutic drug due to complicated synthesis route, low efficiency and potential biosafety issues. In this study, novel self-assembled ultrasmall Fe3O4 nanoclusters were developed for tumor-targeted dual-mode T1/T2-weighted magnetic resonance imaging (MRI) guided synergetic chemodynamic therapy (CDT) and chemotherapy. The self-assembled ultrasmall Fe3O4 nanoclusters synthesized by facilely modifying ultrasmall Fe3O4 nanoparticles with 2,3-dimercaptosuccinic acid (DMSA) molecule possess long-term stability and mass production ability. The proposed ultrasmall Fe3O4 nanoclusters shows excellent dual-mode T1 and T2 MRI capacities as well as favorable CDT ability due to the appropriate size effect and the abundant Fe ion on the surface of ultrasmall Fe3O4 nanoclusters. After conjugation with the tumor targeting ligand Arg-Gly-Asp (RGD) and chemotherapy drug doxorubicin (Dox), the functionalized Fe3O4 nanoclusters achieve enhanced tumor accumulation and retention effects and synergetic CDT and chemotherapy function, which serve as a powerful integrated theranostic platform for cancer treatment.

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 MPI, 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 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, andin vivocell tracking.

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.

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.

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.

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 H2O2 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.

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.

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.

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.