Blinding and blurring the multi-object tracker with adversarial perturbations.

Adversarial attack reveals a potential imperfection in deep models that they are susceptible to being tricked by imperceptible perturbations added to images. Recent deep multi-object trackers combine the functionalities of detection and association, rendering attacks on either the detector or the association component an effective means of deception. Existing attacks focus on increasing the frequency of ID switching, which greatly damages tracking stability, but is not enough to make the tracker completely ineffective. To fully explore the potential of adversarial attacks, we propose Blind-Blur Attack (BBA), a novel attack method based on spatio-temporal motion information to fool multi-object trackers. Specifically, a simple but efficient perturbation generator is trained with the blind-blur loss, simultaneously making the target invisible to the tracker and letting the background be regarded as moving targets. We take TraDeS as our main research tracker, and verify our attack method on other excellent algorithms (i.e., CenterTrack, FairMOT, and ByteTrack) on MOT-Challenge benchmark datasets (i.e., MOT16, MOT17, and MOT20). BBA attack reduced the MOTA of TraDeS and ByteTrack from 69.1 and 80.3 to -238.1 and -357.0, respectively, indicating that it is an efficient method with a high degrees of transferability.

Tuning Thermal Conductivity of Hybrid Perovskites through Halide Alloying.

Tuning the thermal transport properties of hybrid halide perovskites is critical for their applications in optoelectronics, thermoelectrics, and photovoltaics. Here, an effective strategy is demonstrated to modulate the thermal transport property of hybrid perovskites by halide alloying. A highly tunable thermal conductivity of mixed-halide hybrid perovskites is achieved due to halide-alloying and structural distortion. The experimental measurements show that the room temperature thermal conductivity of MAPb(BrxI1- x)3 (x = 0─1) can be largely modulated from 0.27 ± 0.07 W m-1 K-1 (x = 0.5) to 0.47 ± 0.09 W m-1 K-1 (x = 1). Molecular dynamics simulations further demonstrate that the thermal conductivity reduction of hybrid halide perovskites results from the suppression of the mean free paths of the low-frequency acoustic and optical phonons. It is found that halide alloying and the induced structural distortion can largely increase the scatterings of optical and acoustic phonons, respectively. The confined diffusion of MA+ cations in the octahedra cage is found to act as an additional thermal transport channel in hybrid perovskites and can contribute around 10-20% of the total thermal conductivity. The findings provide a strategy for tailoring the thermal transport in hybrid halide perovskites, which may largely benefit their related applications.

Prediction of Subsurface Microcrack Damage Depth Based on Surface Roughness in Diamond Wire Sawing of Monocrystalline Silicon.

In diamond wire saw cutting monocrystalline silicon (mono-Si), the material brittleness removal can cause microcrack damage in the subsurface of the as-sawn silicon wafer, which has a significant impact on the mechanical properties and subsequent processing steps of the wafers. In order to quickly and non-destructively obtain the subsurface microcrack damage depth (SSD) of as-sawn silicon wafers, this paper conducted research on the SSD prediction model for diamond wire saw cutting of mono-Si, and established the relationship between the SSD and the as-sawn surface roughness value (SR) by comprehensively considering the effect of tangential force and the influence of the elastic stress field and residual stress field below the abrasive on the propagation of median cracks. Furthermore, the theoretical relationship model between SR and SSD has been improved by adding a coefficient considering the influence of material ductile regime removal on SR values based on experiments sawing mono-Si along the (111) crystal plane, making the theoretical prediction value of SSD more accurate. The research results indicate that a decrease in wire speed and an increase in feed speed result in an increase in SR and SSD in silicon wafers. There is a non-linear increasing relationship between silicon wafer SSD and SR, with SSD = 21.179 Ra4/3. The larger the SR, the deeper the SSD, and the smaller the relative error of SSD between the theoretical predicted and experimental measurements. The research results provide a theoretical and experimental basis for predicting silicon wafer SSD in diamond wire sawing and optimizing the process.

A scientometric analysis of immunotherapies for gliomas: Focus on GBM.

Gliomas are the most prevalent primary malignant brain tumors worldwide, with glioblastoma (GBM) being the most common and aggressive type. The standard therapy for GBM has remained unchanged for nearly two decades, with no significant improvement in survival outcomes. Despite several barriers such as the tumor microenvironment (TME) and blood-brain barrier, immunotherapies bring new hope for the treatment of GBM. To better understand the development and progress of immunotherapies in GBM, we made this scientometric analysis of this field. A total of 3753 documents were obtained from the Web of Science Core Collection, with publication years ranging from 1999 to 2022. The Web of Science platform, CiteSpace, and VOS viewer were used to conduct the scientometric analysis. The results of scientometric analysis showed that this field has recently become a popular topic of interest. The United States had the most publications among 89 countries or regions. Keyword analysis indicated significant areas in the field of immunotherapies for GBM, especially TME, immune checkpoint blockades (ICBs), chimeric antigen receptor T (CAR-T) cells, vaccines, and oncolytic viruses (OVs). Overall, we hope that this scientometric analysis can provide insights for researchers and promote the development of this field.

High-performance presurgical differentiation of glioblastoma and metastasis by means of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics.

OBJECTIVES: To evaluate the performance of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics in distinguishing between glioblastoma (Gb) and solitary brain metastasis (SBM). MATERIALS AND METHODS: In this retrospective study, NODDI images were curated from 109 patients with Gb (n = 57) or SBM (n = 52). Automatically segmented multiple volumes of interest (VOIs) encompassed the main tumor regions, including necrosis, solid tumor, and peritumoral edema. Radiomics features were extracted for each main tumor region, using three NODDI parameter maps. Radiomics models were developed based on these three NODDI parameter maps and their amalgamation to differentiate between Gb and SBM. Additionally, radiomics models were constructed based on morphological magnetic resonance imaging (MRI) and diffusion imaging (diffusion-weighted imaging [DWI]; diffusion tensor imaging [DTI]) for performance comparison. RESULTS: The validation dataset results revealed that the performance of a single NODDI parameter map model was inferior to that of the combined NODDI model. In the necrotic regions, the combined NODDI radiomics model exhibited less than ideal discriminative capabilities (area under the receiver operating characteristic curve [AUC] = 0.701). For peritumoral edema regions, the combined NODDI radiomics model achieved a moderate level of discrimination (AUC = 0.820). Within the solid tumor regions, the combined NODDI radiomics model demonstrated superior performance (AUC = 0.904), surpassing the models of other VOIs. The comparison results demonstrated that the NODDI model was better than the DWI and DTI models, while those of the morphological MRI and NODDI models were similar. CONCLUSION: The NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM. CLINICAL RELEVANCE STATEMENT: The NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM, and radiomics features can be incorporated into the multidimensional phenotypic features that describe tumor heterogeneity. KEY POINTS: • The neurite orientation dispersion and density imaging (NODDI) radiomics model showed promising performance for preoperative discrimination between glioblastoma and solitary brain metastasis. • Compared with other tumor volumes of interest, the NODDI radiomics model based on solid tumor regions performed best in distinguishing the two types of tumors. • The performance of the single-parameter NODDI model was inferior to that of the combined-parameter NODDI model.

Integrated model of ozone mass transfer and oxidation kinetic: Construction, solving and analysis.

Ozone-based advanced oxidation process (O3-AOPs) is rapidly evolving, but the surge of emerging pollutants brings new challenges for ozone oxidation research. Herein, we proposed a state-of-the-art model for simultaneously analyzing both ozone mass transfer and oxidation kinetics during ozone oxidation of emerging organic contaminants. The numerical solution and graphical representations of the integrated model were utilized to analyze the dynamics of ozone and pollutant concentration. An in-depth analysis of the integrated model revealed that the reaction rate constants in this present study were higher than previously reported apparent reaction rate constants, and catalysts were not always necessary. Finally, we developed an installable mobile application (APP) that allowed the simulation of the dynamic process for ozone oxidizing organic pollutants in the laboratory, which offered theoretical support for the selection of experimental conditions. The results of model simulation not only provide scientific explanations for counter-intuitive experimental phenomena, but also optimized experimental conditions to enhance ozone utilization.

Bottom-up design of carbon allotropes with tunable properties.

Carbon materials have received great attention owing to their numerous allotropes and rich properties. Structure design and property tuning of carbon materials is one of the tremendous challenges in the field of materials science research. Here we carried out a bottom-up approach and identified a series of carbon allotropes with similar structures but distinct properties. The structures designed in this work had comparable energy stability to those previously predicted using the top-down global structure search method. Theoretical property calculations demonstrated that the three carbon structures with pure sp3 hybridization possessed semiconductive and superhard properties, while the carbon structure with sp2 + sp3 hybridization exhibited metallic features. Also, they differed significantly in the anisotropy of the mechanical properties. These carbon structures had some match to the unidentified phases in the detonation soot and could hopefully be synthesized by thermal "degassing" of high-pressure Na-C products. Our results propose a strategy to regulate properties through structural tuning, thus paving a way for the design and synthesis of materials with desirable properties.

Flexible and Robust Core-Shell PANI/PVDF@PANI Nanofiber Membrane for High-Performance Electromagnetic Interference Shielding.

Developing high-performance electromagnetic interference (EMI) shielding materials that are lightweight and flexible and have excellent mechanical properties is an ideal choice for modern integrated electronic devices and microwave protection. Herein, we report the preparation of core-shell polyaniline (PANI)-based nanofiber membranes for EMI shielding through seed polymerization. Electrospinning a PANI solution leads to homogeneously dispersed PANI on the nanofiber surface, with abundant attachment sites for aniline through electrostatic adsorption and hydrogen bonding interaction, allowing PANI to grow on the nanofiber surfaces. This stable core-shell heterostructure provides more interfaces for reflecting and absorbing microwaves. The PANI/PVDF@PANI membranes achieved a shielding efficiency (SE) of 44.7 dB at a thickness of only 1.2 mm, exhibiting an exceptionally high specific EMI shielding effectiveness (SE/t) of 372.5 dB cm-1. Furthermore, the composite membrane exhibits outstanding mechanical stability, durability, air permeability, and moisture permeability, also making it suitable for applications such as EM shielding clothing.

Structural transition and migration of incoherent twin boundary in diamond.

Grain boundaries (GBs), with their diversity in both structure and structural transitions, play an essential role in tailoring the properties of polycrystalline materials1-5. As a unique GB subset, {112} incoherent twin boundaries (ITBs) are ubiquitous in nanotwinned, face-centred cubic materials6-9. Although multiple ITB configurations and transitions have been reported7,10, their transition mechanisms and impacts on mechanical properties remain largely unexplored, especially in regard to covalent materials. Here we report atomic observations of six ITB configurations and structural transitions in diamond at room temperature, showing a dislocation-mediated mechanism different from metallic systems11,12. The dominant ITBs are asymmetric and less mobile, contributing strongly to continuous hardening in nanotwinned diamond13. The potential driving forces of ITB activities are discussed. Our findings shed new light on GB behaviour in diamond and covalent materials, pointing to a new strategy for development of high-performance, nanotwinned materials.

Polarization properties and Umov effect of human hair.

This study delves into the polarization properties of various hair colors using several techniques, including polarization ray tracing, full Stokes, and Mueller matrix imaging. Our analysis involved studying hair in both indoor and outdoor settings under varying lighting conditions. Our results demonstrate a strong correlation between hair color and the degree of linear polarization. Specifically, light-colored hair, such as white and blond, exhibits high albedo and low DoLP. In contrast, dark hair, like black and brown hair, has low albedo and high DoLP. Our research also revealed that a single hair strand displays high diattenuation near specular reflections but high depolarization in areas with diffuse reflections. Additionally, we investigated the wavelength dependency of the polarization properties by comparing the Mueller matrix under illumination at 450 nm and 589 nm. Our investigation demonstrates the impact of hair shade and color on polarization properties and the Umov effect.

A Color-Changing Biomimetic Material Closely Resembling the Spectral Characteristics of Vegetation Foliage.

Multispectral/hyperspectral technologies can easily detect man-made objects in vegetation by subtle spectral differences between the object and vegetation, and powerful reconnaissance increases the demand for camouflage materials closely resembling vegetation spectra. However, previous biomimetic materials have only presented static colors that cannot change color, and camouflage in multiple bands is difficult to achieve. To address this challenge, inspiration is drawn from the color change of foliage, and a color-change model is proposed with active and static pigments embedded in a matrix medium. The color of a composite material is dominated by the colored active pigment, which conceals the color of the static pigments and the color is revealed when the active pigment fades. A color-changing biomimetic material (CCBM) is developed with a solution casting method by adopting microcapsuled thermochromic pigments and chrome titanate yellow pigments as fillers in a base film with polyvinyl alcohol and lithium chloride. A Kubelka-Munk four-flux model is constructed to optimize the component proportions of the CCBM. The material has a reversible color change, closely resembles the foliage spectrum in UV-vis-NIR ranges, and imitates the thermal behavior of natural foliage in the mid-infrared regime. These results provide a novel approach to multispectral and hyperspectral camouflage.

Radiomics Analysis of Diffusion Kurtosis Imaging: Distinguishing Between Glioblastoma and Single Brain Metastasis.

RATIONALE AND OBJECTIVES: This study aimed to assess the value of diffusion kurtosis imaging (DKI)-based radiomics models in differentiating glioblastoma (GB) from single brain metastasis (SBM) and compare their diagnostic performance with that of routine magnetic resonance imaging (MRI) models. MATERIALS AND METHODS: A total of 110 patients who underwent DKI and were pathologically diagnosed with GB (n = 58) or SBM (n = 52) were enrolled in this study. Radiomics features were extracted from the manually delineated region of interest of the lesion. A training set for model development was constructed from the images of 88 random patients, and 22 patients were reserved for independent validation. Seven single-DKI-parametric models and a multi-DKI-parametric model were constructed using six classifiers, whereas four single-routine-sequence models (based on T2 weighted imaging, apparent diffusion coefficient, T2-dark-fluid, and contrast-enhanced T1 magnetization prepared rapid gradient echo) and a multisequence routine MRI model were constructed for comparison. Receiver operating characteristic curve analysis was conducted to assess the diagnostic performance. The areas under the curve (AUCs) of different models were compared using the DeLong test. RESULTS: The AUCs of the single-DKI-parametric models ranged from 0.800 to 0.933 (mean kurtosis [MK] model). The multi-DKI-parametric model had a slightly higher AUC (0.958) than the MK model; however, the difference was not statistically significant (P = 0.688). In comparison, the AUCs of the routine MRI models ranged from 0.633 to 0.733 (multisequence routine MRI model). The AUC of the multi-DKI-parametric model was significantly higher than that of the multisequence routine MRI model (P = 0.042). CONCLUSION: The multi-DKI-parametric radiomics model exhibited better performance than that of the single-DKI-parametric models and routine MRI models in distinguishing GB from SBM.

Unsupervised Joint Domain Adaptation for Decoding Brain Cognitive States From tfMRI Images.

Recent advances in large model and neuroscience have enabled exploration of the mechanism of brain activity by using neuroimaging data. Brain decoding is one of the most promising researches to further understand the human cognitive function. However, current methods excessively depends on high-quality labeled data, which brings enormous expense of collection and annotation of neural images by experts. Besides, the performance of cross-individual decoding suffers from inconsistency in data distribution caused by individual variation and different collection equipments. To address mentioned above issues, a Join Domain Adapative Decoding (JDAD) framework is proposed for unsupervised decoding specific brain cognitive state related to behavioral task. Based on the volumetric feature extraction from task-based functional Magnetic Resonance Imaging (tfMRI) data, a novel objective loss function is designed by the combination of joint distribution regularizer, which aims to restrict the distance of both the conditional and marginal probability distribution of labeled and unlabeled samples. Experimental results on the public Human Connectome Project (HCP) S1200 dataset show that JDAD achieves superior performance than other prevalent methods, especially for fine-grained task with 11.5%-21.6% improvements of decoding accuracy. The learned 3D features are visualized by Grad-CAM to build a combination with brain functional regions, which provides a novel path to learn the function of brain cortex regions related to specific cognitive task in group level.

AMSF: attention-based multi-view slice fusion for early diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is an irreversible neurodegenerative disease with a high prevalence in the elderly population over 65 years of age. Intervention in the early stages of AD is of great significance to alleviate the symptoms. Recent advances in deep learning have shown extreme advantages in computer-aided diagnosis of AD. However, most studies only focus on extracting features from slices in specific directions or whole brain images, ignoring the complementarity between features from different angles. To overcome the above problem, attention-based multi-view slice fusion (AMSF) is proposed for accurate early diagnosis of AD. It adopts the fusion of three-dimensional (3D) global features with multi-view 2D slice features by using an attention mechanism to guide the fusion of slice features for each view, to generate a comprehensive representation of the MRI images for classification. The experiments on the public dataset demonstrate that AMSF achieves 94.3% accuracy with 1.6-7.1% higher than other previous promising methods. It indicates that the better solution for AD early diagnosis depends not only on the large scale of the dataset but also on the organic combination of feature construction strategy and deep neural networks.

From heart failure and kidney dysfunction to cardiorenal syndrome: TMAO may be a bridge.

The study of trimethylamine oxide (TMAO), a metabolite of gut microbiota, and heart failure and chronic kidney disease has made preliminary achievements and been summarized by many researchers, but its research in the field of cardiorenal syndrome is just beginning. TMAO is derived from the trimethylamine (TMA) that is produced by the gut microbiota after consumption of carnitine and choline and is then transformed by flavin-containing monooxygenase (FMO) in the liver. Numerous research results have shown that TMAO not only participates in the pathophysiological progression of heart and renal diseases but also significantly affects outcomes in chronic heart failure (CHF) and chronic kidney disease (CKD), besides influencing the general health of populations. Elevated circulating TMAO levels are associated with adverse cardiovascular events such as HF, myocardial infarction, and stroke, patients with CKD have a poor prognosis as well. However, no study has confirmed an association between TMAO and cardiorenal syndrome (CRS). As a syndrome in which heart and kidney diseases intersect, CRS is often overlooked by clinicians. Here, we summarize the research on TMAO in HF and kidney disease and review the existing biomarkers of CRS. At the same time, we introduced the relationship between exercise and gut microbiota, and appropriately explored the possible mechanisms by which exercise affects gut microbiota. Finally, we discuss whether TMAO can serve as a biomarker of CRS, with the aim of providing new strategies for the detection, prognostic, and treatment evaluation of CRS.

Quantitative Simulation of Enzymatic Breakdown of Alcohol in Human Metabolism.

The breakdown of ethanol, the active chemical in alcohol, is tightly regulated by the body, yet alcohol intoxication occurs in thousands of Americans annually. Many factors contribute to the concentration of ethanol in the bloodstream and the tolerance an individual has, including body size, previous drinking experience, and liver functionality. We propose a model that estimates both the blood alcohol concentration and the concentration of acetaldehyde (the toxic intermediate during catabolism) in the liver over time to quantify organ damage for an average person. From the current literature, we derived ordinary differential equations that govern the absorption of ethanol in the body and extended it with the metabolic enzyme mechanisms. We also altered the parameters of our system in order to show the effects of Asian flush, which impairs the body's processing of acetaldehyde. We demonstrated the accumulation of acetaldehyde in Asian flush patients was about 660 times higher compared to those without the disease.Clinical relevance-With further improvements and personalization, our model would be able to quantitatively describe the effects of alcohol consumption without having volunteers go through repetitive trials with extensive exposure to alcohol. Liver damage can also be estimated with the acetaldehyde buildup predicted by the model.

Radiomic texture analysis based on neurite orientation dispersion and density imaging to differentiate glioblastoma from solitary brain metastasis.

BACKGROUND: We created discriminative models of different regions of interest (ROIs) using radiomic texture features of neurite orientation dispersion and density imaging (NODDI) and evaluated the feasibility of each model in differentiating glioblastoma multiforme (GBM) from solitary brain metastasis (SBM). METHODS: We conducted a retrospective study of 204 patients with GBM (n = 146) or SBM (n = 58). Radiomic texture features were extracted from five ROIs based on three metric maps (intracellular volume fraction, orientation dispersion index, and isotropic volume fraction of NODDI), including necrosis, solid tumors, peritumoral edema, tumor bulk volume (TBV), and abnormal bulk volume. Four feature selection methods and eight classifiers were used for the radiomic texture feature selection and model construction. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the models. Routine magnetic resonance imaging (MRI) radiomic texture feature models generated in the same manner were used for the horizontal comparison. RESULTS: NODDI-radiomic texture analysis based on TBV subregions exhibited the highest accuracy (although nonsignificant) in differentiating GBM from SBM, with area under the ROC curve (AUC) values of 0.918 and 0.882 in the training and test datasets, respectively, compared to necrosis (AUCtraining:0.845, AUCtest:0.714), solid tumor (AUCtraining:0.852, AUCtest:0.821), peritumoral edema (AUCtraining:0.817, AUCtest:0.762), and ABV (AUCtraining:0.834, AUCtest:0.779). The performance of the five ROI radiomic texture models in routine MRI was inferior to that of the NODDI-radiomic texture model. CONCLUSION: Preoperative NODDI-radiomic texture analysis based on TBV subregions shows great potential for distinguishing GBM from SBM.

Study on Grinding-Affected Layer of Outer-Ring Inner Raceway of Tapered Roller Bearing.

In the grinding of bearing raceways, the coupling effect between grinding force and heat in the contact area between the grinding wheel and the workpiece causes changes in the material structure and mechanical properties of the raceway surface layer, which can lead to the formation of a grinding-affected layer. The grinding-affected layer has a significant impact on the service performance and fatigue life of bearings. In order to improve the ground surface quality of the outer-ring inner raceway of tapered roller bearings and optimize the processing parameters, this paper presents a study on the grinding-affected layer. A finite element simulation model for grinding the outer-ring inner raceway of the tapered roller bearing was established. The grinding temperature field was simulated to predict the affected-layer thickness during raceway grinding. The correctness of the model was verified through grinding experiments using the current industrial process parameters of bearing raceway grinding. The research results indicate that the highest grinding temperature of the outer-ring inner raceway of the tapered roller bearing is located near the center of the grinding arc area on the thin end edge. As the workpiece speed and grinding depth decrease, the highest grinding temperature decreases, and the dark layer thickness of the grinding-affected layer decreases or even does not occur. The research results can provide theoretical guidance and experimental reference for grinding the raceway of tapered roller bearings.

Hardness and electronic properties of Si-C-N structures.

Three novel hexagonal Si-C-N structures, namely SiC3N3, SiC7N6, and SiC13N14, were constructed on the basis of the α-Si3N4 crystal structure. The stability of the three structures is demonstrated by analyzing their elastic constants and phonon dispersion spectra and by calculating their formation energies. The calculated band structures and partial densities of states suggest that the SiC3N3 and SiC7N6 structures possess hole conductivity. The electron orbital analyses indicate that the SiC3N3 and SiC7N6 crystals possess three-dimensional and one-dimensional conductivity, respectively. SiC13N14 is a semiconductor with a wide bandgap of 4.39 eV. Based on two different hardness models and indentation shear stress calculations, the Vickers hardness values of SiC3N3, SiC7N6, and SiC13N14 are estimated to be 28.04/28.45/16.18 GPa, 31.17/34.19/20.24 GPa, and 40.60/41.59/36.40 GPa. This result indicates that SiC3N3 and SiC7N6 are conductive hard materials while SiC13N14 is a quasi superhard material.

The outstanding diagnostic value of DKI in multimodal magnetic resonance imaging for benign and malignant breast tumors: A diagnostic accuracy study.

To explore the value of applying different magnetic resonance imaging MRI sequences in the differential diagnosis of benign and malignant breast tumors. Routine breast magnetic resonance scans (T1-weighted image, T1WI; T2-weighted image, T2WI), dynamically enhanced scans, diffusion-weighted Imaging, and diffusion kurtosis imaging (DKI) scans were performed on 63 female patients with breast-occupying lesions. The benign and malignant lesions were confirmed by biopsy, excision-histopathology reports. There are 70 lesions, of which 46 are benign and 24 are malignant. Analyze the primary conditions, such as the shape, size, and boundary of the lesion, and determine the apparent diffusion coefficient (ADC), mean kurtosis (MK), and mean diffusion (MD) values. The receiver operating characteristic curve was used to evaluate the value and difference in differentiating benign and malignant lesions. In this study, the results of the 2 testers both showed that the MK of malignant lesions was significantly higher than that of benign lesions (P < .001), and the MD of benign lesions was higher than that of malignant lesions (P < .05). The ADC of benign lesions was higher than that of malignant lesions (P < .05). For MK, the area under the curve of the 2 testers was 0.855/0.869, respectively. When the best cutoff value of MK for tester 1 was 0.515, the sensitivity and specificity of MK for diagnosing malignant tumors were 83.3%/87.0%, respectively. For the 2 testers MD, and ADC, the area under the curve was < 0.5, and the diagnostic value was low. The MK value obtained by DKI has a specific value in the differential diagnosis of benign and malignant breast lesions. DKI is helpful in the identification of benign and malignant breast tumors. The diagnostic value is outstanding, and its importance to the changes in the microstructure of the organization needs to be further explored.