EfficientNetB1 Deep Learning Model for Microscopic Lung Cancer Lesion Detection and Classification Using Histopathological Images

Cancer poses a significant threat due to its aggressive nature,potential for widespread metastasis,and inherent heterogeneity,which often leads to resistance to chemotherapy.Lung cancer ranks among the most prevalent forms of cancer worldwide,affecting individuals of all genders.Timely and accurate lung cancer detection is critical for improving cancer patients’treatment outcomes and survival rates.Screening examinations for lung cancer detection,however,frequently fall short of detecting small polyps and cancers.To address these limitations,computer-aided techniques for lung cancer detection prove to be invaluable resources for both healthcare practitioners and patients alike.This research implements an enhanced EfficientNetB1 deep learning model for accurate detection and classification using histopathological images.The proposed technique accurately classifies the histopathological images into three distinct classes:(1)no cancer(benign),(2)adenocarcinomas,and(3)squamous cell carcinomas.We evaluated the performance of the proposed technique using the histopathological(LC25000)lung dataset.The preprocessing steps,such as image resizing and augmentation,are followed by loading a pretrained model and applying transfer learning.The dataset is then split into training and validation sets,with fine-tuning and retraining performed on the training dataset.The model’s performance is evaluated on the validation dataset,and the results of lung cancer detection and classification into three classes are obtained.The study’s findings show that an enhanced model achieves exceptional classification accuracy of 99.8%.

Pulmonary Edema and Pleural Effusion Detection Using Efficient Net-V1-B4 Architecture and AdamW Optimizer from Chest X-Rays Images

This paper presents a novelmulticlass systemdesigned to detect pleural effusion and pulmonary edema on chest Xray images,addressing the critical need for early detection in healthcare.A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14,PadChest,and CheXpert databases,with 10,287,6022,and 12,000 samples representing Pleural Effusion,Pulmonary Edema,and Normal cases,respectively.Consequently,the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization(CLAHE)method to boost the local contrast of the X-ray samples,then resizing the images to 380×380 dimensions,followed by using the data augmentation technique.The classification task employs a deep learning model based on the EfficientNet-V1-B4 architecture and is trained using the AdamW optimizer.The proposed multiclass system achieved an accuracy(ACC)of 98.3%,recall of 98.3%,precision of 98.7%,and F1-score of 98.7%.Moreover,the robustness of the model was revealed by the Receiver Operating Characteristic(ROC)analysis,which demonstrated an Area Under the Curve(AUC)of 1.00 for edema and normal cases and 0.99 for effusion.The experimental results demonstrate the superiority of the proposedmulti-class system,which has the potential to assist clinicians in timely and accurate diagnosis,leading to improved patient outcomes.Notably,ablation-CAM visualization at the last convolutional layer portrayed further enhanced diagnostic capabilities with heat maps on X-ray images,which will aid clinicians in interpreting and localizing abnormalities more effectively.

Power of SAR Imagery and Machine Learning in Monitoring Ulva prolifera:A Case Study of Sentinel-1 and Random Forest

Automatically detecting Ulva prolifera(U.prolifera)in rainy and cloudy weather using remote sensing imagery has been a long-standing problem.Here,we address this challenge by combining high-resolution Synthetic Aperture Radar(SAR)imagery with the machine learning,and detect the U.prolifera of the South Yellow Sea of China(SYS)in 2021.The findings indicate that the Random Forest model can accurately and robustly detect U.prolifera,even in the presence of complex ocean backgrounds and speckle noise.Visual inspection confirmed that the method successfully identified the majority of pixels containing U.prolifera without misidentifying noise pixels or seawater pixels as U.prolifera.Additionally,the method demonstrated consistent performance across different im-ages,with an average Area Under Curve(AUC)of 0.930(+0.028).The analysis yielded an overall accuracy of over 96%,with an average Kappa coefficient of 0.941(+0.038).Compared to the traditional thresholding method,Random Forest model has a lower estimation error of 14.81%.Practical application indicates that this method can be used in the detection of unprecedented U.prolifera in 2021 to derive continuous spatiotemporal changes.This study provides a potential new method to detect U.prolifera and enhances our under-standing of macroalgal outbreaks in the marine environment.

HY-1C Coastal Zone Imager observations of the suspended sediment content distribution details in the sea area near Hong Kong-Zhuhai-Macao Bridge in China

The impacts of the Hong Kong-Zhuhai-Macao Bridge(HKZMB)on suspended sediment content(SSC)were analysed in the Zhujiang River Estuary based on data from HY-1C,which was launched in September 2018 in China,carrying Coastal Zone Imager(CZI)and Chinese Ocean Color and Temperature Scanner on it.A new SSC inversion model was established based on the relationship between in-situ SSC and the remote sensing reflectance in red and near-infrared bands of CZI image.HY-1C satellite data obtained from October to December 2019 were applied to retrieve SSC in the Zhujiang River Estuary.The results show that SSC around the HKZMB is ranging from 20 mg/L to 95 mg/L.SSC change obviously on two sides of the bridge.During flooding and ebbing period,SSC increases obviously downstream of the bridge.SSC difference between upstream and downstream is ranging from 5 mg/L to 20 mg/L.Currents flowing across the HKZMB,the change trend of SSC in most places upstream and downstream is almost the same that SSC downstream of the bridge is higher than SSC upstream.The tidal currents interact with bridge piers,inducing vortexes downstream,leading the sediment to re-suspend downstream of the bridge piers.Other factors,including seafloor topography and wind,can also contribute to the distribution of SSC in the Zhujiang River Estuary.

MRI T_2 star mapping、T_1 images与3D DESS融合图在隐匿性膝关节软骨损伤中的应用

目的探讨T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨损伤中的诊断价值。方法对26例关节软骨损伤患者行T_2 star mapping、T_1 images和3D DESS扫描,并将T_1 images、T_2 star mapping与3D DESS图像融合,评价患者股骨、胫骨、髌骨关节软骨损伤程度并与关节镜结果对比,计算融合伪彩图诊断软骨损伤的特异性、敏感性及与关节镜诊断结果一致性。结果 T_1 images-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为92.8%、93.0%、0.769,T_2 star mapping-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为91.4%、94.2%、0.787。结论 T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨早期损伤评价上优于关节镜。

Performances of conventional fusion methods evaluated for inland water body observation using GF-1 image

Satellite remote sensing of inland water body requires a high spatial resolution and a multiband narrow spectral resolution, which makes the fusion between panchromatic(PAN) and multi-spectral(MS) images particularly important. Taking the Daquekou section of the Qiantang River as an observation target, four conventional fusion methods widely accepted in satellite image processing, including pan sharpening(PS), principal component analysis(PCA), Gram-Schmidt(GS), and wavelet fusion(WF), are utilized to fuse MS and PAN images of GF-1.The results of subjective and objective evaluation methods application indicate that GS performs the best,followed by the PCA, the WF and the PS in the order of descending. The existence of a large area of the water body is a dominant factor impacting the fusion performance. Meanwhile, the ability of retaining spatial and spectral informations is an important factor affecting the fusion performance of different fusion methods. The fundamental difference of reflectivity information acquisition between water and land is the reason for the failure of conventional fusion methods for land observation such as the PS to be used in the presence of the large water body. It is suggested that the adoption of the conventional fusion methods in the observing water body as the main target should be taken with caution. The performances of the fusion methods need re-assessment when the large-scale water body is present in the remote sensing image or when the research aims for the water body observation.

A lightweight symmetric image encryption cryptosystem in wavelet domain based on an improved sine map

In the era of big data,the number of images transmitted over the public channel increases exponentially.As a result,it is crucial to devise the efficient and highly secure encryption method to safeguard the sensitive image.In this paper,an improved sine map(ISM)possessing a larger chaotic region,more complex chaotic behavior and greater unpredictability is proposed and extensively tested.Drawing upon the strengths of ISM,we introduce a lightweight symmetric image encryption cryptosystem in wavelet domain(WDLIC).The WDLIC employs selective encryption to strike a satisfactory balance between security and speed.Initially,only the low-frequency-low-frequency component is chosen to encrypt utilizing classic permutation and diffusion.Then leveraging the statistical properties in wavelet domain,Gaussianization operation which opens the minds of encrypting image information in wavelet domain is first proposed and employed to all sub-bands.Simulations and theoretical analysis demonstrate the high speed and the remarkable effectiveness of WDLIC.

The brightness reversal of submarine sand waves in “HJ-1A/B” CCD sun glitter images

The brightness reversal of submarine sand waves appearing in the small satellite constellation for environ- ment and disaster monitoring and forecasting （＂HJ- 1A/B＂） CCD sun glitter images can affect the observation and depth inversion of sand wave topography. The simulations of the normalized sun glitter radiance on the submarine sand waves confirm that the reversal would happen at a specific sensor viewing angle, defined as the critical angle. The difference between the calculated critical angle position and the reversal position in the image is about 1＇, which is excellent in agreement. Both the simulation and actual image show that sand wave crests would be indistinct at the reversal position, which may cause problems when using these sun glitter images to analyze spatial characteristics and migration of sand waves. When using the sun glitter image to obtain the depth inversion, one should take the advantage of image properties of sand waves and choose the location in between the reversal position and the brightest position. It is also necessary to pay attention to the brightness reversal when using ＂HI-1A/B＂ CCD images to analyze other oceanic features, such as internal waves, oil slicks, eddies, and ship wakes.

Population Spatial Distribution Based on Luojia 1-01 Nighttime Light Image:A Case Study of Beijing

With the continuous development of urbanization in China,the country’s growing population brings great challenges to urban development.By mastering the refined population spatial distribution in administrative units,the quantity and agglomeration of population distribution can be estimated and visualized.It will provide a basis for a more rational urban planning.This paper takes Beijing as the research area and uses a new Luojia1-01 nighttime light image with high resolution,land use type data,Points of Interest(POI)data,and other data to construct the population spatial index system,establishing the index weight based on the principal component analysis.The comprehensive weight value of population distribution in the study area was then used to calculate the street population distribution of Beijing in 2018.Then the population spatial distribution was visualize using GIS technology.After accuracy assessments by comparing the result with the WorldPop data,the accuracy has reached 0.74.The proposed method was validated as a qualified method to generate population spatial maps.By contrast of local areas,Luojia 1-01 data is more suitable for population distribution estimation than the NPP/VIIRS(Net Primary Productivity/Visible infrared Imaging Radiometer)nighttime light data.More geospatial big data and mathematical models can be combined to create more accurate population maps in the future.

MRI联合血清SDF-1、NDRG4诊断卵巢癌的应用价值

目的 分析磁共振成像(MRI)联合血清基质细胞衍生因子-1(SDF-1)、N-myc下游调节因子4(NDRG4)诊断卵巢癌的应用价值。方法 选取2021年11月至2023年11月本院收治的96例经病理学确诊的卵巢癌患者即为卵巢癌组,同期收治确诊为卵巢良性肿瘤患者96例为对照组。采用酶联免疫吸附试验(ELISA)法检测血清中SDF-1、NDRG4水平;血清SDF-1、NDRG4对卵巢癌的诊断价值绘制ROC曲线。采用四格表检测MRI联合血清SDF-1、NDRG4对卵巢癌的诊断价值。结果 与对照组相比,卵巢癌组患者血清中SDF-1水平显著升高, NDRG4水平显著降低(P<0.05)。卵巢癌患者血清SDF-1、NDRG4水平与TNM分期、琳巴结转移、分化程度、 CA125相关(P<0.05)。血清SDF-1、NDRG4联合诊断卵巢癌发生的AUC显著高于单独诊断的AUC值(Z_(SDF-1~SDF-1+NDRG4)=2.084,P=0.037;Z_(NDRG4~SDF-1+NDRG4)=2.570,P=0.010)。MRI联合血清SDF-1、NDRG4检测诊断卵巢癌的敏感性低于单一指标,特异性高于单一指标。结论 卵巢癌患者血清中SDF-1高表达、NDRG4低表达, MRI联合血清SDF-1、NDRG4检测能够提高对卵巢癌的诊断效能。

China Receives Images From SuperView-1

Launched on December 28,2016,the Super View-1 satellite has operated for over 70 days at an altitude of 530km.The initial results of in-orbit commissioning show that the images from the Super View-1 satellite are clear with radiation resolution reaching 11 bits pixel.The geospatial positioning accuracy without ground control pointing is5-8 m,the elevation relative accuracy is1 m.The maximum single scene can be60 km×70 km,enabling some 900。

Image potential states mediated STM imaging of cobalt phthalocyanine on NaCl/Cu(100)

The adsorption and electronic properties of isolated cobalt phthalocyanine （CoPc） molecule on an ultrathin layer of NaC1 have been investigated. High-resolution STM images give a detailed picture of the lowest unoccupied molecular orbital （LUMO） of an isolated CoPc. It is shown that the NaC1 ultrathin layer efficiently decouples the interaction of the molecules from the underneath metal substrate, which makes it an ideal substrate for studying the properties of single molecules. Moreover, strong dependence of the appearance of the molecules on the sample bias in the region of relatively high bias （〉 3.1 V） is ascribed to the image potential states （IPSs） of NaCI/Cu（100）, which may provide us with a possible method to fabricate quantum storage devices.

A Cross-Plane Color Image Encryption Algorithm Based on 1D-SLM

With the rapid development of 5G technology,it has become fast and easy for people to transmit information on the Internet.Digital images can express information more intuitively,so transmitting information through images has excellent applications.This paper uses a new chaotic system called 1D-Sin-Logistic-Map(1D-SLM).1D-SLM has two control parameters,which can provide larger parameter space,and the parameter space in the chaotic state is continuous.Through Lyapunov exponent analysis(LE),bifurcation diagrams analysis,spectral entropy analysis(SE),and 0-1 test,it is verified that 1D-SLM has complex dynamic behavior and is very suitable for cryptography.Compared with other 1D chaotic systems,the 1D-SLM has a larger Lyapunov exponent(LE)and spectral entropy(SE).For color image encryption algorithms,only relying on chaotic mapping is not enough to ensure security.So combined with 1D-SLM,we design a color image encryption algorithm,which is implemented by plane expansion,which reduces the correlation between the three channels of color images.The experimental results show that the proposed cross-plane color image encryption algorithm is safe and resistant to common attack methods.

Extraction of Planting Information of Winter Wheat in a Province Based on GF-1/WFV Images

In order to explore the adaptability of domestic high-resolution GF-1 satellite images in the extraction of planting information of crops especially in a province, based on the 16-meter remote sensing images of a multi-spectral wide-spectrum camera （WFV） carried by the GF-1 satellite as well as land use type and field survey data of Shandong Province, the planting area and distribution regions of winter wheat in Shandong Province （the main producing area of winter wheat in China） in 2016 were extracted by decision tree classification method and supervised classification- maximum likelihood classification method, and the accuracy of the classification results was verified based on ground survey data and data published by the statistics bureau. The results showed that the method of taking the GF-1/WFV images as the main source of data, introducing multi-source information into the decision tree and supervised classification models, and then calculating the planting area of winter wheat in the province was feasible. The total accuracy of remote sensing interpretation of winter wheat in Shandong Province in 2016 reached 92.1 %, and Kappa coefficient was 0.806. The planting area of winter wheat extracted based on the remote sensing images in the province was slightly smaller than the area pro-vided by the statistics department, and the extraction accuracy of the area was 93.0%. Research indicates that GF-1/WFV images have great po-tential for development and application in remote sensing monitoring of planting information of crops in a province.

Diffusion-weighted imaging and variable flip angle T1 mapping: a supplement for image-guided biopsy in follow-up analysis of liver fibrosis

Purpose To evaluate the performance of diffusion-weighted imaging(DWI) and variable flip angle(VFA) T1 mapping as a supplement to image-guided biopsy in follow-up analysis of liver fibrosis. Materials and Methods This prospective study was approved by the institution's committee on human research, and written informed consent was provided from the enrolled patients. We investigated five MRI parameters of DWI and VFA T1 mapping, collected from 11 patients who underwent serial ultrasound image-guided biopsy with follow-up MRI within 1.5 years after treatment for liver fibrosis/cirrhosis. For each patient, four consecutive MRI examinations were conducted, including baseline MRI before treatment and three follow-up MRI examinations after treatment at each 0.5-year interval. ADC values at four b values and T1 relaxation times were correlated to pathology-confirmed liver fibrosis stages, which were subsequently divided into two groups, stages F2–3 and F4. The receiver operating characteristic(ROC) analysis and repeated measurement analysis of variance were used for statistical analysis. Results Among these ADC parameters, ADC value(b = 500 s/mm^2) was the most consistent in differentiating between stage F2–3 and F4 liver fibrosis. Repeated measurement analysis showed that the intra-group and inter-group differences were 0.447 and 0.024, respectively. T1 relaxation time could not consistently differentiate between the F2–3 and F4 groups; however, it was repeatable, and the intra-group and inter-group differences were 0.410 and 0.042, respectively. Conclusion MRI-ADC value at a b value of 500 s/mm^2 can be a promising biomarker for differentiating stages F2–3 and F4 liver fibrosis. A combination of this biomarker with repeatable T1 relaxation time may function as a non-invasive tool for follow-up liver fibrosis in patients who reject repeated image-guided biopsy.

Assessment the Optimal Effect of Time of Repetition: Extrinsic Pulse Parameter on Gd-DTPA Enhanced, Spin-Echo T1-Weighted MR Images under Low Magnetic Field Strength

The contrast agent concentration, the time of repetition (TR) and magnetic field strength are significant parameters that influence for the accurate signal intensity (SI) in quantitative Magnetic Resonance Imaging (MRI). Therefore, this study was conducted to investigate and refine the dependence and the optimal effect of Time of Repetition (TR) on the relationship between signal intensity and Gd-DTPA (Gadolinium-diethylene-triaminepenta-acetic acid) concentration, after applying two-dimensional (2D) Spin Echo (SE) pulse sequence under low-field MRI. In addition to that, the optimal concentration of Gd-DTPA at given sequence parameters at low-field MRI was also evaluated. A water-filled phantom was constructed for a range of Gd-DTPA concentrations (0 - 6 mmol/L) and the mean signal intensities (SIs) were assessed in the defined region of interest on T1-weighted images with different TR values (40 - 2000 ms). The generated signal-concentration curves for Gd-DTPA revealed that increasing TR was associated with the increase of the overall SIs and the maximum relationship between SI to concentration. Moreover, the required Gd-DTPA concentration to produce the maximum SI was associated to decrease with the increase of TR. In addition to this, the application of beyond 100 ms TR values in this study with relatively higher concentrations (beyond 1 - 2 mmol/L) has resulted predominantly non-linear patterns in the signal-concentration curves and it appears the saturation or decay of the SIs due to T2 effect. From these results, it can be suggested that the selection of relatively lower Gd-DTPA concentration ( mmol/L) with less than 800 ms (<800 ms) TR values can produce a better linear relationship between the concertation and SIs in T1-weighted SE low field contrast-enhanced MRI. Furthermore, this study also outlined the significance and necessity of the optimization of TR in SE sequence in low field MRI prior to a particular examination.

磁共振成像联合血清IFITM1、LRG1检测对子宫肌瘤的临床诊断价值分析

目的 探究磁共振成像(MRI)联合血清干扰素诱导跨膜蛋白1(IFITM1)、富含亮氨酸α-2糖蛋白1(LRG1)检测对子宫肌瘤的临床诊断价值。方法选取2019年6月至2023年6月于本院就诊的疑似子宫肌瘤患者98例,根据病理活检结果或其他诊断方式将患者分为阳性组和阴性组;患者均进行M RI检查;酶联免疫吸附法检测血清IFITM1、LRG17水平;绘制ROC曲线分析血清IFITM1、LRG1对子宫肌瘤的临界诊断点;四格表分析M RI联合血清IFITM1、 LRG1对子宫肌瘤的诊断价值。结果根据病理结果和影像学等诊断确诊阳性63例,阴性35例。阳性组血清IFITM1、LRG1水平显著高于阴性组(P<0.05)。根据ROC曲线得知,血清IFITM1诊断子宫肌瘤的AUC为0.889,血清LRG1诊断子宫肌瘤的AUC为0.873,二者联合诊断子宫肌瘤的AUC为0.946。MRI在子宫肌瘤诊断中准确度为89.80%,灵敏度为93.65%,特异度为82.86%;IFITM1在子宫肌瘤诊断中准确度为82.65%,灵敏度为88.89%,特异度为71.43%;LR61在子宫肌瘤诊断中准确度为82.65%,灵敏度为87.30%,特异度为74.29%;三者联合检测在子宫肌瘤诊断中准确度为9 2.86%,灵敏度为95.24%,特异度为88.57%。结论IFITM1、LRG1在子宫肌瘤患者血清中显著升高,MRI联合血清IFITM1、LR61检测能提高对子宫肌瘤的临床诊断价值。

前列腺癌小鼠模型的多b值弥散加权成像与水通道蛋白1表达的相关性研究

目的:探究前列腺癌小鼠模型中弥散加权成像(DWI)定量参数与水通道蛋白1(AQP1)的相关性。方法:将25只雄性C57BL/6J小鼠随机分为实验组(n=20)与对照组(n=5),实验组建立原位前列腺癌模型。造模后第9、12、15、18 d随机选取实验组小鼠5只,行磁共振成像(MRI)扫描后取材肿瘤进行HE、AQP1免疫组化及免疫印迹。对照组小鼠重复扫描。MRI扫描序列包括T_(1)WI、T_(2)WI、T_(2)WI SPIR、常规b值DWI(b=0、500、1 000、1 500、2 000 s/mm^(2))和多高b值DWI(b=2 000、2 500、3 000、3 500、4 000 s/mm^(2))。比较各时间表观弥散系数(ADC)、指数表观弥散系数(EADC)及AQP1表达量,分析其相关性。结果:实验组小鼠常规b值ADC值随肿瘤进展逐渐降低,EADC值逐渐升高(P<0.05);多高b值ADC与EADC值各时间点间差异无统计学意义(P>0.05);AQP1表达逐渐增加,免疫组化阳性面积比与常规b值ADC值呈负相关(P<0.05),与常规b值EADC值呈正相关(P<0.05),与多高b值ADC、EADC值无显著相关性(P>0.05)。结论:前列腺癌小鼠模型常规b值ADC、EADC值与AQP1存在相关性,有望成为无创监测前列腺癌AQP1表达的影像生物标志物。

双清一号(珞珈三号01星)多模式成像样例数据集

针对当前多数高分辨遥感卫星面向用户服务存在获取数据种类单一问题,该文公开了双清一号(珞珈三号01星)多模式成像样例数据集,涵盖了面阵推扫、面阵推帧和视频凝视等多种成像模式,包含城市、水体、山区、机场等不同目标区域的典型数据样本。该数据集由信号数据解码、Bayer插值、相对辐射校正、几何定位、视频稳像和3维重建等步骤处理构建;同时,对在轨定标、兴趣区产品快速生产、高清视频几何稳像和多角度3维重建等关键算法做了深入探讨和研究。最后,对样本数据集从图像标准产品、凝视视频产品和实景3维产品等3个方面进行了可视化展示和定量化精度评价。

T_(1)-mapping鉴别腮腺常见肿瘤的初探

目的:探讨T_(1)-mapping和分段读出DWI(RESOLVE-DWI)在腮腺常见肿瘤鉴别诊断中的价值。方法:回顾性分析2018年6月-2022年8月经病理证实的169例腮腺常见肿瘤患者的T_(1)-mapping及分段读出DWI资料。其中,多形性腺瘤85例,Warthin瘤32例,恶性肿瘤52例。测量并比较三组肿瘤的T_(1)及ADC值,运用受试者工作特征(ROC)曲线评价T_(1)、ADC及二者联合(T_(1)+ADC)对腮腺肿瘤的鉴别诊断价值。采用Delong检验比较各参数的AUC的差异。结果:三组肿瘤的T_(1)值和ADC值的差异均有统计学意义(P<0.001);组间两两比较,差异均有统计学意义(P<0.001);多形性腺瘤的T_(1)值及ADC值最高;恶性肿瘤次之,Warthin瘤最低。T_(1)和ADC鉴别多形性腺瘤与Warthin瘤、多形性腺瘤与恶性肿瘤、Warthin瘤与恶性肿瘤的AUC分别为0.914和0.997、0.664和0.869、0.835和0.695,Delong检验显示2个参数AUC的差异均无统计学意义(P>0.05)。T_(1)+ADC鉴别多形性腺瘤与Warthin瘤、多形性腺瘤与恶性肿瘤的AUC均显著高于T_(1)(1.000 vs.0.914、0.873 vs.0.664,P均<0.001);T_(1)+ADC鉴别Warthin瘤与恶性肿瘤的AUC显著高于T_(1)(0.895 vs.0.835,P<0.001)和ADC(0.895 vs.0.695,P<0.001)。结论:T_(1)-mapping和RESOLVE-DWI有助于鉴别诊断腮腺常见肿瘤,二者联合诊断可提高鉴别诊断效能。