Computer-Aided Diagnosis for 3-D Power Doppler Breast Ultrasound

In recent studies, both tumor morphology and vascularity played an important role in differentiating breast tumors. In this article, a computer-aided diagnosis (CAD) system was proposed to quantify the tumor morphology of vascularity on three-dimensional (3-D) power Doppler breast ultrasound (PDUS) images. We segmented the tumor margin by the level set method and skeletonized vessels by the 3-D thinning algorithm from 3-D PDUS data to capture the B-mode and vascularity features. The B-mode features including texture, shape and ellipsoid fitting and the vascularity features containing volume, complexity, length, radius and tortuosity were used to differentiate breast tumors. In the experiment, 82 biopsy-verified lesions including 41 benign and 41 malignant lesions were used to test the performance of the proposed system. The proposed method performed well, achieving accuracy, sensitivity, specificity and Az values of 85.37% (70/82), 85.37% (35/41), 85.37% (35/41) and 0.9104, respectively.     

Computer-Aided Diagnosis Based on Quantitative Elastographic Features with Supersonic Shear Wave Imaging

Supersonic shear wave imaging (SSI) has recently been explored as a technique to evaluate tissue elasticity modulus and has become a valuable tool for tumor characterization. The purpose of this study was to develop a novel computer-aided diagnosis (CAD) system that can acquire quantitative elastographic information from color SSI elastography images automatically and objectively for the purpose of classifying benign and malignant breast tumors. Conventional ultrasonography (US) and SSI elastography images of 125 breast tumors (81 benign, 44 malignant), in 93 consecutive patients (mean age: 40 y, age range: 16–75 y), were obtained. After reconstruction of tissue elasticity data and automatic segmentation of each breast tumor, 10 quantitative elastographic features of the tumor and peri-tumoral areas, respectively (elasticity modulus mean, maximum and standard deviation, hardness degree and elasticity ratio), were computed and evaluated. A support vector machine (SVM) classifier was used for optimum classification via combination of these features. The B-mode Breast Imaging Reporting and Data System (BI-RADS) was used to compare gray-scale US and SSI elastography with respect to diagnostic performance. Histopathologic examination was used as the reference standard. Student's t-test, the Mann-Whitney U-test, the point biserial correlation coefficient and receiver operating characteristic curve analysis were performed for statistical analysis. As a result, the accuracy, sensitivity, specificity, positive predictive value and negative predictive value of benign/malignant classification were 95.2% (119/125), 90.9% (40/44), 97.5% (79/81), 95.2% (40/42) and 95.2% (79/83) for the CAD scheme, respectively, and 79.2% (99/125), 90.9% (40/44), 72.8% (59/81), 64.5% (40/62) and 93.7% (59/63) for BI-RADS assessment, respectively. The area under the receiver operating characteristic curve (A_z value) for the proposed CAD system using the combination of elastographic features was significantly higher than the A_z value for visual assessment by the radiologists using BI-RADS (0.97 vs. 0.91). The results indicate that SSI elastography could be used for computer-aided feature extraction, and the proposed CAD method could improve the diagnostic accuracy of classification of breast tumors to avoid unnecessary biopsy. Furthermore, elastographic features of the peri-tumoral area have the potential to provide critical information in differential diagnosis.     

Vascular Morphology and Tortuosity Analysis of Breast Tumor Inside and Outside Contour by 3-D Power Doppler Ultrasound

This study aimed to evaluate morphologic and tortuous features of vessels inside and outside the tumor region on three-dimensional power Doppler ultrasonography (PDUS) in 113 breast mass lesions, including 60 benign and 53 malignant tumors. Compared with benign lesions, malignant breast lesions had significantly larger values of vascular morphologic and tortuous features and larger tumor sizes. The receiver operating characteristic curve analysis and Student's t-test were used to estimate the performance of a proposed classification system using 13 vascular features and tumor size selected by the neural network. Accuracy, sensitivity, specificity, positive predictive value, negative predictive value and the A_Z value of the diagnosis performance based on 14 features were 89.38% (101/113), 84.91% (45/53), 93.33% (56/60), 91.84% (45/49), 87.50% (56/64) and 0.9188, respectively. The three-dimensional PDUS morphologic and tortuous characteristics of blood vessels inside and outside breast mass lesions can be effectively used to classify benign and malignant tumors.     

Computer-aided diagnosis for the classification of breast masses in automated whole breast ultrasound images

New automated whole breast ultrasound (ABUS) machines have recently been developed and the ultrasound (US) volume dataset of the whole breast can be acquired in a standard manner. The purpose of this study was to develop a novel computer-aided diagnosis system for classification of breast masses in ABUS images. One hundred forty-seven cases (76 benign and 71 malignant breast masses) were obtained by a commercially available ABUS system. Because the distance of neighboring slices in ABUS images is fixed and small, these continuous slices were used for reconstruction as three-dimensional (3-D) US images. The 3-D tumor contour was segmented using the level-set segmentation method. Then, the 3-D features, including the texture, shape and ellipsoid fitting were extracted based on the segmented 3-D tumor contour to classify benign and malignant tumors based on the logistic regression model. The Student’s t test, Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis were used for statistical analysis. From the Az values of ROC curves, the shape features (0.9138) are better than the texture features (0.8603) and the ellipsoid fitting features (0.8496) for classification. The difference was significant between shape and ellipsoid fitting features (p = 0.0382). However, combination of ellipsoid fitting features and shape features can achieve a best performance with accuracy of 85.0% (125/147), sensitivity of 84.5% (60/71), specificity of 85.5% (65/76) and the area under the ROC curve Az of 0.9466. The results showed that ABUS images could be used for computer-aided feature extraction and classification of breast tumors. (E-mail: rfchang@csie.ntu.edu.tw)     

基于跨模态注意力机制特征B型超声与弹性超声融合模块联合诊断乳腺良、恶性肿瘤

目的 设计跨模态注意力机制特征融合模块,观察其用于B型超声与弹性超声联合诊断乳腺良、恶性肿瘤的价值。方法 收集371例接受常规超声检查及超声弹性成像的女性乳腺肿瘤患者、共466处病灶;按3∶1∶1将466组病灶图像分为训练集(n=280)、验证集(n=93)及测试集(n=93)。采用卷积神经网络分支模型分别提取B型超声图像和弹性超声图像特征,之后以基于跨模态注意力机制的多模态特征融合网络进行特征融合,观察其诊断乳腺良、恶性肿瘤的价值。结果 改进后的DenseNet用于B型超声诊断乳腺良、恶性肿瘤的准确率为88.43%,敏感度为88.96%,特异度为87.31%,其效能略优于改进前。基于跨模态注意机制特征融合的B型超声与弹性超声联合诊断乳腺良、恶性肿瘤的准确率为94.23%,敏感度为95.11%,特异度为93.28%,效能优于决策加权融合模型、直接串联融合模型及单模态模型。结论 跨模态注意力机制特征融合模块可在一定程度上提高B型超声与弹性超声联合诊断乳腺良、恶性肿瘤的效能。     

Classification of Breast Tumors Using Elastographic and B-mode Features: Comparison of Automatic Selection of Representative Slice and Physician-Selected Slice of Images

Inter-observer variability and image quality are two key factors that can affect the diagnostic performance of elastography and B-mode ultrasound for breast tumor characterization. The purpose of this study is to use an image quantification method that automatically chooses a representative slice and then segments the tumor contour to evaluate the diagnostic features for tumor characterization. First, the representative slice is selected based on either the stiffness inside the tumor (the signal-to-noise ratio on the elastogram [SNR_e]) or the contrast between the tumor and the surrounding normal tissue (the contrast-to-noise ratio on the elastogram [CNR_e]). Next, the level set method is used to segment the tumor contour. Finally, the B-mode and elastographic features related to the segmented tumor are extracted for tumor characterization. The performance of the representative slice selected using the proposed methods is compared to that of the physician-selected slice in 151 biopsy-proven lesions (89 benign and 62 malignant). The diagnostic accuracies using elastographic features are 82.1% (124/151) for the slice with the maximum CNR_e value, 82.1% (124/151) for the slice with the maximum SNR_e value and 82.8% (125/151) for the physician-selected slice, whereas the diagnostic accuracies using B-mode features are 80.8% (122/151) for the slice with the maximum CNR_e value, 87.4% (132/151) for the slice with the maximum SNR_e value and 84.1% (127/151) for the physician-selected slice. When using both the B-mode and elastographic features to characterize the tumor, the accuracy of diagnosis is 86.1% (130/151) for the slice with the maximum CNR_e value, 90.1% (136/151) for the slice with the maximum SNR_e value and 89.4% (135/151) for the physician-selected slice. Our results show that the representative slice selected by SNR_e and CNR_e could be used to reduce the observer variability and to increase the diagnostic performance by the B-mode and elastographic features.     

Semi-Quantitative and Qualitative Assessment of Breast Ultrasound Elastography in Differentiating Between Malignant and Benign Lesions

The purpose of this study was to evaluate the diagnostic value of qualitative and semi-quantitative assessment of ultrasound elastography in differentiating between benign and malignant breast lesions. This prospective study was conducted in two tertiary medical centers. Consecutive B-mode ultrasound and real-time elastographic images were obtained for 67 malignant and 101 benign breast lesions in 168 women. Four experienced radiologists analyzed B-mode ultrasound alone and B-mode ultrasound combined with elastography independently. Conventional ultrasound findings were classified according to the American College of Radiology Breast Imaging Reporting and Data System classification. The elastographic assessment was based on qualitative and semi-quantitative parameters (i.e., strain pattern, width ratio, strain ratio). The sensitivity and specificity of combined elastography and conventional ultrasound were significantly higher than that of conventional ultrasound alone. The sensitivity, specificity, positive predictive value and negative predictive value was 97%, 61.4%, 62.5% and 96.8%, respectively, for conventional ultrasound and 100%, 93%, 99% and 90%, respectively, for combined technique. The semi-quantitative assessment with strain ratio and width ratio in elastography were the most useful parameters in differentiating between benign and malignant breast lesions. Cut-off point values for width ratio of more than 1.1 and strain ratio of more than 5.6 showed a high predictive value of malignancy with specificities of 84% and 76%, respectively (p < 0.001). This combined technique also had the best results in detecting carcinoma and could reduce the need of unnecessary biopsy for benign lesions with indeterminate or equivocal features.     

Comparison of Strain and Shear Wave Elastography for the Differentiation of Benign From Malignant Breast Lesions,Combined With B-mode Ultrasonography: Qualitative and Quantitative Assessments

Our aim was to compare the diagnostic performance of strain elastography (SE) and shear-wave elastography (SWE), combined with B-mode ultrasonography (US), in breast cancer. For 79 breast lesions that underwent SE and SWE, two radiologists reviewed five data sets (B-mode US, SWE, SE and two combined sets). Qualitative and quantitative elastographic data and Breast Imaging Reporting and Data System (BI-RADS) categories were recorded. The area under the receiver operating characteristic curve (AUC) was evaluated. No significant difference in the AUC between the two elastography methods was noted. After subjective assessment by reviewers, the AUC for the combined sets was improved (SWE, 0.987; SE, 0.982; B-mode US, 0.970; p < 0.05). When SE and SWE were added, 38% and 56% of benign BI-RADS category 4a lesions with a low suspicion of cancer were downgraded without false-negative results, respectively. SE and SWE performed similarly. Therefore, addition of SE or SWE improved the diagnostic performance of B-mode US, potentially reducing unnecessary biopsies.     

边界增强像素级多模态声像图融合方法评估良、恶性乳腺肿瘤

目的 观察以边界增强多模态乳腺声像图像素级特征融合方法评估良、恶性乳腺肿瘤性质的价值。方法 基于乳腺肿瘤B型声像图提取边界增强图像,于超声弹性复合声像图中提取纯弹性信息图像。对多模态乳腺肿瘤声像图进行像素级特征融合,形成边界特征增强的融合图像,再以卷积神经网络(CNN)进行分类;评估融合方法分类良、恶性乳腺肿瘤的性能,并与单模态方法、特征级融合方法、无边界增强像素级图像融合方法及其他CNN模型进行对比。结果 边界增强像素级特征融合方法有助于CNN提取乳腺肿瘤特征,分类良、恶性乳腺性能最佳,其分类准确率为85.71%,特异度为85.49%,敏感度为86.16%,模型稳定。结论 边界特征增强像素级多模态声像图融合方法可用于判断良、恶性乳腺肿瘤。     

Implementation of Elastography Score and Strain Ratio in Combination with B-Mode Ultrasound Avoids Unnecessary Biopsies of Breast Lesions

The aim of this study was to evaluate whether the combination of B-mode ultrasound, elastography score (ES) and strain ratio (SR) improves diagnostic performance with respect to breast lesions. One hundred thirty lesions were prospectively evaluated by B-mode ultrasound and strain elastography, followed by fine-needle aspiration cytology/biopsy in 117 woman who were scheduled for regular breast BUS. The median ES (4.5 vs. 2.9, p < 0.001) and SR (4.9 vs. 2.3, p < 0.001) were significantly higher for malignant than for benign lesions. A sensitivity of 90.5% and specificity of 93.2% for the ES (cutoff point = 3.8) and a sensitivity of 87.5% and specificity of 87.6% for the SR (cutoff point = 3.5) were obtained. Elastography combined with B-mode ultrasound improved the specificity, accuracy and positive predictive value. Receiver operating characteristic curves yielded a higher value for the combined technique for diagnosis of breast lesions. Routine use of such a diagnostic algorithm could reduce the number of unnecessary biopsies.     

Sonographic Elastography Improves the Sensitivity and Specificity of Axilla Sampling in Breast Cancer: A Prospective Study

We describe a study to determine whether elastography of axillary lymph nodes (LNs) combined with B-mode ultrasound (US) is capable of differentiating the benign from the metastatic state in patients with breast cancer. B-mode US, elastography and fine-needle aspiration of 90 axillary lymph nodes from 89 female patients with breast cancer are described in this report. Five elastographic patterns were observed as defined by the percentages of high elasticity according to pattern of distribution and degree of hardness of the target LNs. B-mode US and elastography scores were combined to give the final scores. Sensitivity and specificity were 80% and 88%, respectively, for B-mode US alone, 86% and 90% for elastography alone and 84% and 98% for the combined assessment to differentiate the benign from the malignant state. The combination of B-mode US and elastography is capable of identifying metastatic axillary LNs from benign enlargement in patients with breast cancer.     

超声造影和钼钯X线对乳腺肿物良恶性的诊断效能比较

目的 探讨乳腺CEUS和钼钯X线对乳腺肿物良恶性的诊断效能。方法 收集接受乳腺肿物切除的患者164例共176个病灶,患者于术前接受乳腺CEUS和钼钯X线检查,以病理结果为金标准,比较这两种方法鉴别乳腺肿物良恶性的诊断效能。结果 176个肿物中,良性63个,恶性113个。CEUS和钼钯X线鉴别乳腺肿物良恶性的ROC曲线下面积分别为0.91、0.71(Z=4.69,P<0.05)。CEUS和钼钯X线诊断乳腺恶性病变的准确率、敏感度、特异度分别为88.64%(156/176)、92.04%(104/113)、82.54%(52/63)和69.32%(122/176)、75.22%(85/113)、58.73%(37/63)。结论 CEUS鉴别乳腺肿物良恶性有较高的诊断效能。     

比较超声弹性成像与CDFI鉴别诊断良恶性乳腺病变

目的比较超声弹性成像与CDFI在鉴别诊断良恶性乳腺病变中的应用价值。方法随机选取155例乳腺病变术前患者行超声弹性成像及CDFI检查。CDFI依据Alder血流分级法、弹性成像采用5分法进行评估。以病理结果为金标准,比较两种技术诊断乳腺病变的敏感度、特异度及准确率。结果 155例共检出187个乳腺病灶,其中恶性57个、良性130个。CDFI与弹性评分诊断乳腺病变的敏感度、特异度及准确率分别为68.42%(39/57)、75.38%(98/130)、73.26%(137/187)及84.21%(48/57)、84.62%(110/130)、84.49%(158/187);弹性成像的敏感度及准确率均高于CDFI(P均<0.05),而二者特异度的差异无统计学意义(P>0.05)。CDFI和弹性成像诊断乳腺病变的ROC曲线下面积分别为0.789及0.892。结论超声弹性成像鉴别诊断乳腺良恶性病变的效能高于CDFI。     

超声对乳腺肿块微小钙化点的显示与临床意义

本义观察分析120例乳腺良、恶性肿物内微小钙化的超声显示状况；全部病例均获病理诊断。结果可见，乳腺恶性肿物内部微小钙化的检出率占68、5％（63／92），良性肿块占17．9％（5／28），两者比较具有显著的统计学差异（P＜0.01）；超声检出的内那微小钙化后方声影显示率为47％（32／68）；在不同病理类型的乳腺癌中以浸润性导管癌的微小钙化的检出率最高（91．3％）；恶性肿块内微小钙化多表现为簇状，而良性多表现为点状或斑点状；超声对乳腺肿块内微小钙化的显示承优于X线摄影。故作者认为，除声像图表现及彩色多普勒血流研究外，病灶内部微小钙化的检出也可成为乳腺癌超声诊断的重要指标，应予以重视。     

Quantification of Elastic Heterogeneity Using Contourlet-Based Texture Analysis in Shear-Wave Elastography for Breast Tumor Classification

Ultrasound shear-wave elastography (SWE) has become a valuable tool for diagnosis of breast tumors. The purpose of this study was to quantify the elastic heterogeneity of breast tumors in SWE by using contourlet-based texture features and evaluating their diagnostic performance for classification of benign and malignant breast tumors, with pathologic results as the gold standard. A total of 161 breast tumors in 125 women who underwent B-mode and SWE ultrasonography before biopsy were included. Five quantitative texture features in SWE images were extracted from the directional subbands after the contourlet transform, including the mean (T_mean), maximum (T_max), median (T_med), third quartile (T_qt), and standard deviation (T_sd) of the subbands. Diagnostic performance of the texture features and the classic features was compared using the area under the receiver operating characteristic curve (AUC) and the leave-one-out cross validation with Fisher classifier. The feature T_mean achieved the highest AUC (0.968) among all features and it yielded a sensitivity of 89.1%, a specificity of 94.3% and an accuracy of 92.5% for differentiation between benign and malignant tumors via the leave-one-out cross validation. Compared with the best classic feature, i.e., the maximum elasticity, T_mean improved the AUC, sensitivity, specificity and accuracy by 3.5%, 12.7%, 2.8% and 6.2%, respectively. The T_med, T_qt and T_sd were also superior to the classic features in terms of the AUC and accuracy. The results demonstrated that the contourlet-based texture features captured the tumor's elastic heterogeneity and improved diagnostic performance contrasted with the classic features.     

实时三维子宫输卵管超声造影评价输卵管通畅性

目的 探讨经阴道实时三维子宫输卵管超声造影（3D-HyCoSy）评价输卵管通畅性的应用价值。方法 回顾性分析98例不孕女性患者输卵管3D-HyCoSy表现,并与腹腔镜诊断结果进行对照分析。结果 动态观察指标输卵管全程显影、伞端有造影剂溢出、卵巢周围造影剂环状包绕组合诊断输卵管通畅与腹腔镜符合率为100%;观察指标输卵管无显影、伞端无造影剂溢出、卵巢周围未见造影剂包绕组合,输卵管全程显影、伞端无造影剂溢出、卵巢周围未见造影剂包绕组合,输卵管无显影、伞端无造影剂溢出、卵巢周围造影剂呈半环状或斑片状包绕组合,输卵管部分显影、伞端无造影剂溢出、卵巢周围造影剂呈半环状或斑片状包绕组合诊断输卵管阻塞与腹腔镜符合率均为100%。3D-HyCoSy诊断输卵管不通畅（包括输卵管通而不畅和阻塞）的准确率、敏感度、特异度、阳性预测值及阴性预测值分别为97.31%（181/186）、100%（113/113）、93.15%（68/73）、95.76%（113/118）及100%（68/68）。结论 3D-HyCoSy可有效评价输卵管通畅性,有助于治疗不孕症。     

An Innovative Ultrasound Strain Elastographic Method for the Differential Diagnosis of Breast Tumors

In this monocentric perspective study examining 64 lesions (19 benign and 45 malignant), we tested an innovative ultrasound elastographic method for diagnosing breast tumors. A new region of control and a new index designating the strain ratio (SR) were used. A traditional ultrasound elastographic method was used as the control. The results indicated that the SR ratios of the benign and malignant groups were 0.25 ± 0.22 and 1.10 ± 1.53, respectively, and this difference was statistically significant. The area under the receiver operating characteristic curve of the SR ratio method was 0.95, the cutoff point was 0.37 and the sensitivity and specificity were 95.56% and 94.74%, respectively. The two receiver operating characteristic curves of the traditional method and the SR of the proposed method were significantly different, at a p value of 0.02. Compared with that of the traditional method, the SR obtained with the new method had a larger area under the receiver operating characteristic curve, sensitivity, specificity and objectivity.     

Operator Evaluation of Ultrasound Fusion Imaging Usefulness in the Percutaneous Ablation of Hepatic Malignancies: A Prospective Study

The purpose of the work described here was to evaluate the use of ultrasound fusion imaging (US-FI) in percutaneous ablation of hepatic malignancies with respect to its global usefulness, improvement of conspicuity, confidence increment, needle route changes, and time savings. Thirty-four hepatic tumors (28 hepatocellular carcinomas, 6 metastases) were prospectively ablated in 16 consecutive patients (64 ± 11 y, 75% males), first simulating B-mode-only planning and then synchronizing computed tomography/magnetic resonance images with US and proceeding to needle insertion and tumor ablation under US-FI guidance. Surveys were administered to the operators to evaluate their perception of US-FI advantages. First treatment and local recurrence were also compared. US-FI was judged useful in 91% of cases and more useful when the tumor was not conspicuous in B-mode (p < 0.001), in particular for ablation of local recurrences (p = 0.021). Forty-one percent of tumors were undetectable at B-mode evaluation, and 93% of them were completely ablated under US-FI guidance. The confidence of the operator was highly increased by US-FI in 68% of cases. In 71% of cases, the radiologist saved time using US-FI, especially in treatment of local recurrences (p = 0.024). In conclusion, US-FI significantly increases the detection of target tumors, improves the confidence of the operator and is a time-saving method, especially in the treatment of local recurrences.     

Non-Rigid Image Registration Based Strain Estimator for Intravascular Ultrasound Elastography

Intravascular ultrasound elastography (IVUSe) could improve the diagnosis of cardiovascular disease by revealing vulnerable plaques through their mechanical tissue properties. To improve the performance of IVUSe, we developed and implemented a non-rigid image-registration method to visualize the radial and circumferential component of strain within vascular tissues. We evaluated the algorithm's performance with four initialization schemes using simulated and experimentally acquired ultrasound images. Applying the registration method to radio-frequency (RF) echo frames improved the accuracy of displacements compared to when B-mode images were employed. However, strain elastograms measured from RF echo frames produce erroneous results when both the zero-initialization method and the mesh-refinement scheme were employed. For most strain levels, the cross-correlation-initialization method produced the best performance. The simulation study predicted that elastograms obtained from vessels with average strains in the range of 3%–5% should have high elastographic signal-to-noise ratio (SNRe)–on the order of 4.5 and 7.5 for the radial and circumferential components of strain, respectively. The preliminary in vivo validation study (phantom and an atherosclerotic rabbit) demonstrated that the non-rigid registration method could produce useful radial and circumferential strain elastograms under realistic physiologic conditions. The results of this investigation were sufficiently encouraging to warrant a more comprehensive in vivo validation.     

B-Mode Ultrasound Combined with Color Doppler and Strain Elastography in the Diagnosis of Non-mass Breast Lesions: A Prospective Study

Non-mass breast lesions on ultrasound (US) are areas without an associated mass. The purpose of this study was to evaluate whether combining B-mode US with color Doppler US and strain elastography (SE) improves US differentiation between benign and malignant non-mass breast lesions and the decision for biopsy. In this prospective study, three different radiologists analyzed the US images of 77 non-mass lesions independently and recorded Breast Imaging Reporting and Data System (BI-RADS) categories for four data sets. The image characteristics and BI-RADS categories of the four data sets were analyzed by another radiologist. The final diagnosis was made on the basis of pathologic findings. Values for area under the receiver operating curve (AUC), sensitivity, specificity and accuracy were compared among the data sets. The AUC of B-mode US combined with both color Doppler US and SE was greater than that of B-mode US alone (0.666 vs. 0.828) (p = 0.011). The specificity of making the decision for biopsy increased from 6.5% to 38.7% when B-mode US was combined with color Doppler and SE, without a statistically significant change in sensitivity (p < 0.001). Combined use of color Doppler and SE could improve the diagnostic value of B-mode US in distinguishing benign from malignant non-mass breast lesions and the specificity of making the decision for biopsy of non-mass breast lesions.