Ultrasonic Imaging最新文献

{"title":"Automated Analysis of Ultrasound Images to Measure Muscle-Tendon Junction Excursions by Using the Multiple Feature Point Tracking Algorithm.","authors":"Taku Miyazawa, Keisuke Kubota, Hiroki Hanawa, Keisuke Hirata, Tatsuya Endo, Tsutomu Fujino, Katsuya Onitsuka, Moeka Yokoyama, Naohiko Kanemura","doi":"10.1177/01617346251340322","DOIUrl":"https://doi.org/10.1177/01617346251340322","url":null,"abstract":"<p><p>Ultrasound imaging is used to measure the muscle-tendon junction (MTJ) to investigate the mechanical properties of the tendon and the interaction of the muscle-tendon unit in vivo. Although the MTJ can be observed clearly in the resting state, accurate tracking of the MTJ is difficult during muscle contractions due to changes in its morphology. We devised a novel method using an algorithm that extracts and tracks multiple feature points in ultrasound images to automatically measure the MTJ that moves during muscle contraction. Instead of using a single reference image, multiple feature points are used to improve the tracking performance during the deformation of the MTJ. Subsequently, we experimentally evaluated the usefulness of this method. Tests were conducted on 20 healthy participants performing isometric maximal contractions, and ultrasound echo images of the medial gastrocnemius and Achilles tendon junctions were recorded. MTJ excursion was calculated using the developed multiple feature point algorithm and two conventional methods-multi-updating template-matching and modified Lucas-Kanade (LK)-based on automatic and manual analyses. The root mean square error (RMSE) was used to compare the results. The intraclass correlation coefficient (ICC) was used to evaluate the repeatability among examiners. RMSE was 1.57 ± 0.62 for the proposed algorithm and 2.18 ± 0.89 and 1.84 ± 1.13 for the conventional methods. The Bland-Altman plot showed that the proposed method exhibited a lower 95% confidence interval than the two conventional methods. Thus, the proposed algorithm had the smallest error. Furthermore, the ICC values were 0.96, 0.40, and 0.86 for the proposed algorithm, multi-updating template-matching, and the modified LK method, respectively. When tracking an MTJ excursion that flexibly changes its shape, the use of multiple feature points provides robust results and achieves tracking that approximates the manual analysis results.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"1617346251340322"},"PeriodicalIF":2.5,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of Ultrasonography-Based Fractal Parameters in Distinguishing Hepatocellular Carcinoma From Intrahepatic Cholangiocarcinoma.","authors":"Huihui Zhou, Lin Sang, Yuanyuan Sun, Xue Gong, Jun Zhang, Lina Liu, Junci Wei, Weijie Jiao, Ming Yu","doi":"10.1177/01617346251330257","DOIUrl":"https://doi.org/10.1177/01617346251330257","url":null,"abstract":"<p><p>To evaluate the ability of fractal parameters based on ultrasonography to distinguish between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) in patients. This retrospective study was performed to assess the performance of certain ultrasound-based parameters, including fractal dimension (FD), lacunarity (LAC), and FD combined with LAC (FL), in distinguishing HCC from ICC with liver biopsy as the gold standard, information was obtained from 204 eligible patients. Receiver operating characteristic (ROC) curve analysis was conducted, to assess the performance of these parameters in distinguishing between HCC and ICC in patients with or without CLD. The following parameters were significantly different between with and without CLD: the levels of alpha-fetoprotein, abnormal prothrombin, alanine aminotransferase, aspartic acid, total bilirubin and indirect bilirubin (<i>p</i> < .05). The AUC of FL in differentiating HCC from ICC was 0.983 and 0.854 in patients without or with CLD, which were significantly better than that of FD (non-CLD, 0.902 AUC; CLD, 0.647 AUC, <i>p</i> < .05) and LAC (non-CLD, 0.895 AUC; CLD, 0.843 AUC, <i>p</i> < .05). FL can better distinguish between HCC and ICC in patients with or without CLD than FD and LAC. FL may serve as a promising preoperative alternative for distinguishing between the two diseases.Clinical trials:Brief title: Exploration of noninvasive differential diagnosis of benign and malignant liver tumors.URL: https://register.clinicaltrials.govClinicalTrials.gov ID: NCT06524557.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"1617346251330257"},"PeriodicalIF":2.5,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144056587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Based on Ultrasound Images Differentiates Parotid Gland Pleomorphic Adenomas and Warthin Tumors.","authors":"Yajuan Li, Mingchi Zou, Xiaogang Zhou, Xia Long, Xue Liu, Yanfeng Yao","doi":"10.1177/01617346251319410","DOIUrl":"https://doi.org/10.1177/01617346251319410","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Exploring the clinical significance of employing deep learning methodologies on ultrasound images for the development of an automated model to accurately identify pleomorphic adenomas and Warthin tumors in salivary glands. A retrospective study was conducted on 91 patients who underwent ultrasonography examinations between January 2016 and December 2023 and were subsequently diagnosed with pleomorphic adenoma or Warthin's tumor based on postoperative pathological findings. A total of 526 ultrasonography images were collected for analysis. Convolutional neural network (CNN) models, including ResNet18, MobileNetV3Small, and InceptionV3, were trained and validated using these images for the differentiation of pleomorphic adenoma and Warthin's tumor. Performance evaluation metrics such as receiver operating characteristic (ROC) curves, area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value were utilized. Two ultrasound physicians, with varying levels of expertise, conducted independent evaluations of the ultrasound images. Subsequently, a comparative analysis was performed between the diagnostic outcomes of the ultrasound physicians and the results obtained from the best-performing model. Inter-rater agreement between routine ultrasonography interpretation by the two expert ultrasonographers and the automatic identification diagnosis of the best model in relation to pathological results was assessed using kappa tests. The deep learning models achieved favorable performance in differentiating pleomorphic adenoma from Warthin's tumor. The ResNet18, MobileNetV3Small, and InceptionV3 models exhibited diagnostic accuracies of 82.4% (AUC: 0.932), 87.0% (AUC: 0.946), and 77.8% (AUC: 0.811), respectively. Among these models, MobileNetV3Small demonstrated the highest performance. The experienced ultrasonographer achieved a diagnostic accuracy of 73.5%, with sensitivity, specificity, positive predictive value, and negative predictive value of 73.7%, 73.3%, 77.8%, and 68.8%, respectively. The less-experienced ultrasonographer achieved a diagnostic accuracy of 69.0%, with sensitivity, specificity, positive predictive value, and negative predictive value of 66.7%, 71.4%, 71.4%, and 66.7%, respectively. The kappa test revealed strong consistency between the best-performing deep learning model and postoperative pathological diagnoses (kappa value: .778, &lt;i&gt;p&lt;/i&gt;-value &lt; .001). In contrast, the less-experienced ultrasonographer demonstrated poor consistency in image interpretations (kappa value: .380, &lt;i&gt;p&lt;/i&gt;-value &lt; .05). The diagnostic accuracy of the best deep learning model was significantly higher than that of the ultrasonographers, and the experienced ultrasonographer exhibited higher diagnostic accuracy than the less-experienced one. This study demonstrates the promising performance of a deep learning-based method utilizing ultrasonography images for the differentiation of pleomorphic adenoma and ","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"1617346251319410"},"PeriodicalIF":2.5,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image Features and Diagnostic Value of Contrast-Enhanced Ultrasound for Ductal Carcinoma In Situ of the Breast: Preliminary Findings.","authors":"Weiwei Li, Yingyan Zhao, Xiaochun Fei, Ying Wu, Weiwei Zhan, Wei Zhou, Shujun Xia, Yanyan Song, Jianqiao Zhou","doi":"10.1177/01617346241292032","DOIUrl":"10.1177/01617346241292032","url":null,"abstract":"<p><p>To explore the image features and the diagnostic value of contrast-enhanced ultrasound (CEUS) for ductal carcinoma in situ (DCIS) of the breast. A total of 96 female patients with a solitary and histologically proven DCIS were analyzed retrospectively, and 100 female cases of invasive ductal carcinoma (IDC) lesions were used as the control group. The Breast Imaging Reporting and Data System (BI-RADS) category of breast lesions was assessed according to conventional ultrasound features. The DCIS lesions were classified into mass type and non-mass type. The CEUS characteristics of these breast lesions were retrospectively analyzed qualitatively and quantitatively. The final gold standard was biopsy or surgery with histo-pathological examination. Comparing the ultrasound images of DCIS with that of IDC, there were significant differences in echo pattern, calcification morphology, and calcification distribution (<i>p</i> < .05 for all). There was a significant difference between DCIS and IDC in enhancement intensity, perfusion defects, peripheral high enhancement, intratumoral vessels, and arrival time (AT) (<i>p</i> < .05 for all). In the logistic multivariate regression analysis, two indicators linked with DCIS were recognized: perfusion defects (<i>p</i> = .002) and peripheral high enhancement (<i>p</i> < .001). In forecasting DCIS, the logistic regression equation resulted in an AUC of 0.689, a specificity of 0.720, and a sensitivity of 0.563. CEUS showed differences in enhancement characteristics between DCIS and IDC, with perfusion defects and peripheral high enhancement being associated with DCIS.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"59-67"},"PeriodicalIF":2.5,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142591965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical Feasibility of 3-D Acoustic Radiation Force Impulse (ARFI) Imaging for Targeted Prostate Biopsy Guidance.","authors":"Derek Y Chan, Spencer R Moavenzadeh, Wren E Wightman, Mark L Palmeri, Thomas J Polascik, Kathryn R Nightingale","doi":"10.1177/01617346241311901","DOIUrl":"10.1177/01617346241311901","url":null,"abstract":"<p><p>We have developed a 3-D acoustic radiation force impulse (ARFI) prostate imaging system to identify regions suspicious for cancer and guide a targeted prostate biopsy in a single visit. The system uses a side-fire transrectal probe and an automated rotation stage to acquire ARFI and B-mode image volumes, combined with 3-D visualization and targeting software to enable biopsy target identification and guide a transperineal (TP) biopsy. The system was tested in the first clinical trial of its kind, with subjects serially undergoing ARFI-guided targeted TP biopsy, multiparametric magnetic resonance imaging (mpMRI)-ultrasound fusion TP biopsy, and systematic sampling TP biopsy. The findings indicate that the ARFI system is feasible for guiding a targeted biopsy. For lower-grade cancer (grade groups [GG] 1-2), ARFI underperformed mpMRI and systematic sampling, detecting cancer in 54% of GG 2 subjects. However, ARFI performance improved with increasing cancer grade; for higher-grade cancer (GG 3-5), ARFI performed comparably to the other biopsy approaches, and detected cancer in all GG 4 and 5 subjects. The findings also suggest the benefit of using 2-D ARFI imaging to confirm target location during live B-mode imaging, which could improve existing ultrasonic fusion biopsy workflows. This study is registered with ClinicalTrials.gov as NCT04607135.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"79-92"},"PeriodicalIF":2.5,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11781964/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142933273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Predictive Value of a Nomogram Based on Ultrasound Radiomics, Clinical Factors, and Enhanced Ultrasound Features for Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma.","authors":"Lei Gao, Xiuli Wen, Guanghui Yue, Hui Wang, Ziqing Lu, Beibei Wu, Zhihong Liu, Yuming Wu, Dongmei Lin, Shijian Yi, Wei Jiang, Yi Hao","doi":"10.1177/01617346251313982","DOIUrl":"10.1177/01617346251313982","url":null,"abstract":"<p><p>This study aims to establish and validate an ultrasound radiomics nomogram for preoperative prediction of central lymph node metastasis in papillary thyroid microcarcinoma (PTMC) before operation. A retrospective analysis conducted on ultrasonic images and clinical features derived from 288 PTMC patients, who were divided into training cohorts (<i>n</i> = 201) and validating cohorts (<i>n</i> = 87) in a ratio of 7:3 base on the principle of random allocation. Radiomics features were extracted from the PTMC patients after ultrasonic examination, followed by dimension reduction and characteristic selection to construct the radiomics score (Radscore) using LASSO regression analysis. Subsequently, the models, ultrasound features plus clinical features (US-Clin), radiomics score model, and combined model of clinical features plus ultrasound features and Radscore (Combined-model) were built through multi-factor logistic regression analysis. After that, the nomograms were developed for visualization and presentation of these models. The discriminative power, calibration and clinical utility of the nomogram models were evaluated in the training and validating cohorts. The Radscore model comprised 12 carefully selected features. The independent risk factors for conventional ultrasound features and clinical features of PTMC in predicting CLNM included age <45 years, tumor envelope invasion, male gender and presence of microcalcifications, while the enhanced ultrasound features risk factor was extrathyroidal expansion. The combined model showed good performance in predicting PTMC CLNM, with AUCs of 0.921 and 0.889 in the training and validating cohorts, respectively. And DCA based on the prediction model showed good clinical utility. The nomogram developed based on preoperative clinical data, ultrasound features, and Radscore of PTMC patients can more accurately predict central lymph node metastasis (CLNM) in PTMC patients. However, it needs to be validated for clinical applicability in multicenter studies with larger sample sizes and combined with genomic mutation analyses of the tumors.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"93-103"},"PeriodicalIF":2.5,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11783986/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on a Strategy to Improve the Image Quality and Imaging Depth for Novel Synthetic Aperture Schemes: An Experimental Investigation.","authors":"Aadavan Seetharaman, Arun K Thittai","doi":"10.1177/01617346241307637","DOIUrl":"10.1177/01617346241307637","url":null,"abstract":"<p><p>Imaging depth remains a restriction for Synthetic Aperture (SA) approaches, even though SA techniques have been shown to overcome some of the drawbacks of Conventional Focused Beamforming (CFB) technique. This limitation is attributed to lesser energy per transmit in SA techniques compared to that of CFB technique. Therefore, in this paper, a systematic investigation is done to evaluate the improvement in imaging depth and image quality of B-mode ultrasound images in the case of SA technique using PZT transducer by boosting the input voltage to the transducer, while measuring the acoustic exposure parameters recommended in international standards. Note-while acoustic exposure parameters like Mechanical and Thermal index (MI and TI, respectively) are well-studied and reported for CFB techniques, the same is not the case with novel SA techniques. For analysis, the image quality metrics of Contrast Ratio (CR), Contrast to Noise Ratio (CNR) and generalized Contrast to Noise Ratio (gCNR) are employed for the SA techniques of Synthetic Transmit Aperture (STA) and Diverging Beam Synthetic Aperture Technique (DB-SAT). The results show that, when the input voltage is increased from 45 to 70 V in PZT transducer, the following improvements in image quality parameters were observed: CR by 15.6% and 21.25%, CNR by 21.39% and 36.23% and gCNR by 14.1% and 21.5% for STA and DB-SAT techniques, respectively. Results also suggest that the considerable imaging depth improvement is achieved for the incremental input voltage till 60 V, with the highest of 7.5% depth improvement at 60 V, above which the tradeoff between the gain in imaging depth and acoustic safety index becomes critical to consider. Recently, transducers having lead magnesium nitrate lead titanate (PMN-PT) piezoelectric single crystals have shown to produce ultrasound images with higher image quality at greater depths, irrespective of the transmit schemes. The same concept of relatively higher output transmitting energy by cost expensive PMN-PT single crystal transducers for the same applied voltage, compared to the traditional lead zirconate titanate (PZT) transducers, explains the enhanced imaging depth. This study is probably the first one that reports the comparison between PMN-PT and PZT transducer, not only in terms of imaging depth but also reporting them in the context of acoustic exposure safety limits in terms of MI and TI. The findings of this study may help system designers to minimize the trade-off between cost and image quality.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"68-78"},"PeriodicalIF":2.5,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142933275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a Polymer Ultrasound Contrast Agent Incorporating Nested Carbon Nanodots.","authors":"Matthew A Shirley, Valeria Arango-Aliaga, Ankit Patel, Brian E Oeffinger, John Eisenbrey, Margaret A Wheatley","doi":"10.1177/01617346241279112","DOIUrl":"10.1177/01617346241279112","url":null,"abstract":"<p><p>Polymer microbubbles have garnered broad interest as potential theranostic agents. However, the capabilities of polymer MBs can be greatly enhanced, particularly regarding the imaging performance and functional versatility of the platform. This study investigates integrating fluorescent carbon nanodots within polylactic acid (PLA) microbubbles. First, the formulations are characterized by their size, microbubble counts, zeta potential, and resonance frequency. Then, the fluorescence capabilities, nanoparticle loading, and acoustic capabilities are examined. Unmodified (U-), carboxylated (C-), and aminated graphene quantum dots (A-GQDs) were separately suspended and synthesized at a 2% w/w ratio with PLA in the organic phase of the water/oil/water double emulsion process. The new microbubbles were characterized using an AccuSizer, Zetasizer, scanning electron microscopy, fluorescence microscopy and fluorimetry, a custom-built acoustic setup, and clinical ultrasound. The GQD microbubbles were sized between 1.4 and 1.9 µm (U = 1.90, C = 1.44, A = 1.72, Unloaded = 2.02 µm). The U-GQD microbubble exhibited a higher bubble concentration/mg PLA (<i>p</i> < .05) and the A-GQD microbubbles exhibited the greatest shift in zeta potential. Electron microscopy revealed smooth surfaces and a spherical shape, showing that the nanoparticle addition was not deleterious. The A-GQD microbubbles were specifically detectable using DAPI-filtering with fluorescence microscopy and had the highest TRITC-filtered fluorescence. The C-GQD microbubbles had the highest loading efficiency at 59.4% (<i>p</i> < .05), and the lowest max acoustic enhancement at 5 MHz (U = 19.8, C = 17.6, A = 18.9, Unloaded = 18.5 dB; <i>p</i> < .05). Additionally, all microbubbles were visible and susceptible to inertial cavitation utilizing clinical ultrasound. The A-GQDs showed promise toward improving the theranostic capabilities of the microbubble platform. They have imbued the most advantageous fluorescence capability and slightly improved backscatter enhancement while retaining all the necessary capabilities of an ultrasound contrast agent. Future studies will investigate the coloading potential of A-GQDs and drug within microbubbles.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"45-56"},"PeriodicalIF":2.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In vivo Multi-perspective 3D + t Ultrasound Imaging and Motion Estimation of Abdominal Aortic Aneurysms.","authors":"Esther J Maas, Kim M Donkers, Hein de Hoop, Arjet H M Nievergeld, Mirunalini Thirugnanasambandam, Marc R H M van Sambeek, Richard G P Lopata","doi":"10.1177/01617346241285168","DOIUrl":"10.1177/01617346241285168","url":null,"abstract":"<p><p>Time-resolved three-dimensional ultrasound (3D + t US) is a promising imaging modality for monitoring abdominal aortic aneurysms (AAAs), providing their 3D geometry and motion. The lateral contrast of US is poor, a well-documented drawback which multi-perspective (MP) imaging could resolve. This study aims to show the feasibility of in vivo multi-perspective 3D + t ultrasound imaging of AAAs for improving the image contrast and displacement accuracy. To achieve this, single-perspective (SP) aortic ultrasound images from three different angles were spatiotemporally registered and fused, and the displacements were compounded. The fused MP had a significantly higher wall-lumen contrast than the SP images, for both patients and volunteers (<i>P</i> < .001). MP radial displacements patterns are smoother than SP patterns in 67% of volunteers and 92% of patients. The MP images from three angles have a decreased tracking error (<i>P</i> < .001 for all participants), and an improved SNR_e compared to two out of three SP images (<i>P</i> < .05). This study has shown the added value of MP 3D + t US, improving both image contrast and displacement accuracy in AAA imaging. This is a step toward using multiple or large transducers in the clinic to capture the 3D geometry and strain more accurately, for patient-specific characterization of AAAs.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"3-13"},"PeriodicalIF":2.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11660510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142394685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ring-Enhancement on CEUS: Is it Useful in the Differential Diagnosis of Solid Thyroid Nodules?","authors":"Tingting Li, Lijuan Mao, Xi Wang, Cuixian Li, Caihong Dong, Wenqing Wu, Hantao Wang, Qing Lu","doi":"10.1177/01617346241291511","DOIUrl":"10.1177/01617346241291511","url":null,"abstract":"<p><p>To investigate the efficiency of contrast-enhanced ultrasound (CEUS) features, particularly ring-enhancement patterns, in the differential diagnosis of thyroid nodules. 302 nodules with CEUS ring-enhancement were retrospectively enrolled, including 135 benign and 167 malignant ones. The ring-enhancement patterns were classified into regular and irregular hyper- or hypo-ring enhancement. Comparative analyses of ultrasound (US) and CEUS features between benign and malignant nodules were performed. The diagnostic performances of the ring-enhancement patterns and Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) were compared in nodules with different sizes. Irregular hypo-ring enhancement was much more common in malignancies than that in benign ones, and it was an independent predictor for thyroid malignant nodules. With irregular hypo-ring enhancement as the diagnostic criteria for malignant nodules, the specificity was higher than that of C-TIRADS (85.2% vs. 75.6%, <i>p</i> = .037) while the AUC was comparable (0.845 vs. 0.803, <i>p</i> = .136) in all nodules. When the nodule size was taken into account, the specificity and AUC were both significantly higher than those of C-TIRADS (92.8% vs. 81.1%, <i>p</i> = .021; 0.907 vs. 0.823, <i>p</i> = .026) in nodules ≥10 mm, which can decrease the unnecessary FNA rate. Irregular hypo-ring enhancement was a valuable CEUS feature for the differential diagnosis of thyroid nodules, especially in nodules ≥10 mm.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"37-44"},"PeriodicalIF":2.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142478963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}