Ultrasound localization microscopy (ULM) achieves subwavelength resolution by localizing individual microbubbles (MBs), yet current methods mainly emphasize positional accuracy while neglecting MB amplitude information, which carries valuable physiological significance. This study proposes a deep learning (DL)-based quantitative ULM framework, termed qULM-AGDL, to jointly recover MB position and amplitude. The network integrates a multi-scale fusion channel-spatial attention block within an end-to-end convolutional neural network (CNN) to enhance feature learning for both localization and quantification. Simulation and experimental results demonstrate that qULM-AGDL achieves high localization accuracy (8.98 +/- 4.83 & micro;m) and low amplitude error (33.89 +/- 16.75) even under overlapping or low-signal-to-noise ratio (SNR) conditions. Compared with the mSPCN-ULM and modified Gaussian fitting methods, qULM-AGDL provides significantly improved performance (p < 0.001) in terms of mean squared error (MSE) and structural similarity (SSIM). The proposed framework establishes a basis for quantitative ULM and holds potential for functional and clinical applications such as perfusion assessment and vascular monitoring.

Segment Anything Models (SAMs) generalize well for generic vision tasks; however, they underperform on ultrasound images. Moreover, they require extensive annotated data for training. To address their limitations in generalization, high training data demands, and computational resource usage on ultrasound segmentation, we propose UltraSAM, a foundational medical ultrasound segmentation model. The main contributions of the study are as follows: First, we propose a Fast Fourier Transform (FFT) Convolutional (FFTConv) branch to enhance local frequency-domain feature extraction from ultrasound images, enabling better learning of generalizable ultrasound features. Second, we introduce an FFT injector (FFTI) to promote feature interaction between spatial and frequency domains, reducing training data requirements and improving the generalization of SAM on ultrasound images. Finally, low-rank adapters (LoRAs) are used to fine-tune the encoder, preserving the general visual representation of SAM while enabling domain adaptation for ultrasound imaging with minimal computational cost. We demonstrated the effectiveness of UltraSAM across four downstream segmentation tasks on 10 publicly available datasets spanning eight different ultrasound modalities. UltraSAM outperformed nine task-specific methods and six SAM-based foundation methods with only hundreds of annotated training samples. Compared with existing SAM-based segmentation methods, UltraSAM achieved a 2.07%-59.45% improvement in the Dice score in zero-shot segmentation tasks. In summary, UltraSAM enhances ultrasound diagnostics and personalized therapeutics by reducing dependence on resource-intensive datasets, holding potential to be a foundational clinical decision-support system.

Background Multi-modal ultrasonic images method for grading prostate cancer is becoming an effective diagnostic technique. However, the subjective integration of images’ information leads to grade variability between pathologists, potentially impacting clinical decision-making. Herein, we developed a multimodal adaptive feature fusion network (MAFF-Net) as a computer-aided network system for prostate cancer classification and grading. Methods We proposed and designed two core modules in the MAFF-Net, which include the triple efficient feature extractor (TEFE) and the adaptive feature fusion module (AFFM). The proposed TEFE extracts multiple features from B-mode Ultrasound (BUS), Color Doppler Flow Imaging (CDFI), and Ultrasound Elastography (UE) images, which include tissue microstructure, hemodynamic changes, and mechanical elasticity characteristics. The AFFM adaptively assigns weights to features from different modalities via an attention mechanism. A multi-modal ultrasonic images prostate database was established for training and validating, which included 870 cross-sectional images collected from 182 patients. Results The proposed TEFE improves the robustness of lesion feature extraction than single/dual features. The AFFM improves the effectiveness of feature fusion. The proposed method was compared with existing single/multi-modal methods and showed better performance, with an average area under the receiver operating characteristic curve (AUC) of 0.8408 and 0.7913 for prostate cancer classification and Grade Group (GG). Conclusion MAFF-Net uncovers the potential features from BUS, CDFI, and UE images, and provides effective integration of images’ information for enhancing the diagnosis and grading of prostate cancer. The AI network-based multi-modal ultrasonic images aided diagnostic technique presents robustness and clinical applicability in digital pathology.

Accurate tissue classification of chronic prostatitis is critical for pathological research and clinical evaluation. Traditional diagnostic methods are either highly invasive or lack sufficient specificity, making non-invasive and precise quantitative characterization challenging. Photoacoustic imaging (PAI), which enables label-free and high-contrast detection of tissue biochemical properties, provides a novel approach for the non-invasive quantification of prostate inflammation. In this study, 11 inflamed and 9 normal rat prostate tissue sections were used to extract the power spectrum slope of photoacoustic signals at 36 wavelengths, and its efficacy for inflammation classification was evaluated. Results showed the most significant intergroup differences at 720 nm and $770 \mathrm{~nm}(P=0.005)$. The range from 650 to 890 nm contained numerous characteristic wavelengths that effectively distinguished the two tissue types, with negative slopes in the normal group and positive slopes in the inflamed group. This study clarifies the discriminative value of the power spectrum slope parameter, providing reliable quantitative evidence and technical support for the non-invasive detection of prostatitis, with important theoretical and practical significance.

The photoacoustic tomography system requires custom-built and extensive array elements to achieve high-sensitivity and high-contrast imaging in clinical practice. However, the manufacture of photoacoustic transducers with extensive elements is costly and limits their flexible clinical applications. In this work, we focused on the research of sparse reconstruction algorithms to alleviate the demand for manufacturing. A learnable physical deep learning model was proposed to achieve high-quality photoacoustic image reconstruction from signals collected by sparse and limited view photoacoustic array elements. Numerical simulation and in vivo experiments showed that the learnable physical deep learning model was able to achieve high-quality and artifact-free photoacoustic tomography reconstruction from sparse photoacoustic signals. Even with the extremely sparse in vivo photoacoustic signals (16 detectors in 155 degrees), considerable reconstruction results with a structural similarity of 0.728 ± 0. 029 and peak signal-to-noise ratio of 20.700 ± 2.404 dB could be achieved.

Coherent plane-wave compounding, while efficient for ultrafast ultrasound imaging, yields lower image quality due to unfocused waves. Delay multiply-and-sum (DMAS) beamformer is one of the representative coherence-based methods which can improve images quality, but suffers from poor speckle quality brought by oversuppression. Current DMAS-based methods involve trade-offs between contrast, resolution, and speckle preservation. To overcome this limitation, a new beamformer method combining the null subtraction imaging (NSI) and DMAS is investigated. The proposed method explores the DMAS on different beamformers which employs NSI and delay and sum (DAS) at receive and do multiply-and-sum on different beamformers across transmitting dimension, thereby simultaneously possessing the speckle quality of DAS and the high resolution of NSI. The effectiveness of the proposed method is evaluated through simulation, phantom, and in vivo datasets. From the experimental study, in comparison with NSI, the proposed method has improved contrast ratio by 10.02

Abstract This study aims to investigate the feasibility of using photoacoustic microscopy for the diagnosis of prostatitis. We induced inflammation in rats to establish a model of prostatitis using Freund’s Complete Adjuvant (FCA). Prostate tissues from both the model and control groups were extracted and processed into histological sections. We explored the photoacoustic microscopy imaging results of unstained sections, consistent with the histological detection results using HE staining. Inflammation was found to enhance the photoacoustic signals. Subsequently, we conducted photoacoustic microscopy imaging on all samples, and the detection results were nearly consistent with the diagnoses made by medical professionals. Finally, we quantified the collected photoacoustic signals to classify the severity of prostatitis.

Elastography is a promising diagnostic tool that measures the hardness of tissues, and it has been used in clinics for detecting lesion progress, such as benign and malignant tumors. However, due to the high cost of examination and limited availability of elastic ultrasound devices, elastography is not widely used in primary medical facilities in rural areas. To address this issue, a deep learning approach called the multiscale elastic image synthesis network (MEIS-Net) was proposed, which utilized the multiscale learning to synthesize elastic images from ultrasound data instead of traditional ultrasound elastography in virtue of elastic deformation. The method integrates multi-scale features of the prostate in an innovative way and enhances the elastic synthesis effect through a fusion module. The module obtains B-mode ultrasound and elastography feature maps, which are used to generate local and global elastic ultrasound images through their correspondence. Finally, the two-channel images are synthesized into output elastic images. To evaluate the approach, quantitative assessments and diagnostic tests were conducted, comparing the results of MEIS-Net with several deep learning-based methods. The experiments showed that MEIS-Net was effective in synthesizing elastic images from B-mode ultrasound data acquired from two different devices, with a structural similarity index of 0.74 ± 0.04. This outperformed other methods such as Pix2Pix (0.69 ± 0.09), CycleGAN (0.11 ± 0.27), and StarGANv2 (0.02 ± 0.01). Furthermore, the diagnostic tests demonstrated that the classification performance of the synthetic elastic image was comparable to that of real elastic images, with only a 3 % decrease in the area under the curve (AUC), indicating the clinical effectiveness of the proposed method.

Background Noninvasive evaluation of metabolic dysfunction-associated fatty liver disease (MAFLD) with multiparametric US is essential, but multicenter studies are lacking. Purpose To evaluate the ability of multiparametric US with attenuation imaging (ATI) and two-dimensional (2D) shear-wave elastography (SWE) for predicting metabolic dysfunction-associated steatohepatitis (MASH) in participants with MAFLD, regardless of hepatitis B virus infection status. Materials and Methods This prospective cross-sectional multicenter study of consecutive adults with MAFLD who underwent multiparametric US with ATI and 2D SWE, as well as liver biopsy, from September 2020 to June 2022 was conducted in 12 tertiary hospitals in China. Multivariable logistic regression was performed to assess risk factors associated with MASH. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate diagnostic performance in predicting MASH in training and validation groups (6:4 ratio of participants), and for a post hoc subgroup analysis of hepatitis B virus infection and diabetes. Results A total of 424 participants (median age, 47 years; IQR, 34-59 years; 244 male) were evaluated, including 332 participants (78%) with MASH and 92 (22%) without. Attenuation coefficient (AC) (odds ratio [OR], 3.32 [95% CI: 1.94, 5.71]; P < .001), alanine aminotransferase (ALT) level (OR, 4.42 [95% CI: 1.78, 10.94]; P = .001), and international normalized ratio (INR) (OR, 0.59 [95% CI: 0.37, 0.95]; P = .03) were independently associated with MASH. A combined model (AC, ALT, and INR) had AUCs of 0.85 (95% CI: 0.79, 0.91) and 0.77 (95% CI: 0.69, 0.85) for predicting MASH in the training and validation groups, respectively. AUC values for the subgroups with and without diabetes were 0.83 (95% CI: 0.72, 0.94) and 0.81 (95% CI: 0.75, 0.87) and for the subgroups with and without hepatitis B were 0.82 (95% CI: 0.74, 0.90) and 0.79 (95% CI: 0.71, 0.87), respectively. Conclusion A model combining AC, ALT level, and INR showed good discrimination ability for predicting MASH in participants with MAFLD. Clinical trial registration no. NCT04551716 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Reuter in this issue.

To compare the diagnostic accuracy of 3D contrast-enhanced ultrasound (CEUS)/MRI–CEUS fusion imaging with 2D-CEUS in assessing the response of hepatocellular carcinoma (HCC) to locoregional therapies in a multicenter prospective study. A consecutive series of patients with HCC scheduled for locoregional treatment were enrolled between April 2021 and March 2023. Patients were randomly divided into 3D-CEUS/MRI–CEUS fusion imaging group (3D/fusion group) or 2D-CEUS group (2D group). CEUS was performed 1 week before and 4–6 weeks after locoregional treatment. Contrast-enhanced MRI (CE-MRI) 4–6 weeks after treatment was set as the reference standard. CEUS images were evaluated for the presence or absence of viable tumors. Diagnostic performance criteria, including sensitivity, specificity, accuracy, and area under the curve (AUC), were determined for each modality. A total of 140 patients were included, 70 patients in the 2D group (mean age, 60.2 ± 10.4 years) and 70 patients in the 3D/fusion group (mean age, 59.8 ± 10.6 years). The sensitivity of the 3D/fusion group was 100.0

Objective To enhance the quality of low-resolution (LR) ultrasound images and mitigate artifacts and speckle noise, which can impede accurate medical diagnosis, a novel method called the dual frequency-domain guided adaptation model (DF-GAM) is proposed. The method aims to achieve high-quality image reconstruction across diverse domains, including different ultrasound machines, diseases and phantom images. Methods DF-GAM utilizes a dual-branch network architecture combined with frequency-domain self-adaptation and self-supervised edge regression. This approach enables cross-domain enhancement by focusing on the reconstruction of clear tissue structures and speckle patterns. The model is designed to adapt to various ultrasound imaging (USI) scenarios, ensuring its applicability in real-world clinical settings. Results Experimental evaluations of DF-GAM were conducted using five different datasets. The results demonstrated the method's effectiveness, with DF-GAM outperforming existing enhancement techniques. The average peak signal-to-noise ratio (PSNR) achieved was 34.62, and the structural similarity index (SSIM) was 0.91, indicating a significant improvement in image quality compared to other methods. Conclusion DF-GAM shows great potential in improving medical image diagnosis and interpretation. Its ability to enhance LR ultrasound images across various domains without the need for extensive training data makes it a valuable tool for clinical use. The high PSNR and SSIM scores validate the method's effectiveness, suggesting that DF-GAM could significantly contribute to the field of USI diagnostics.

Objective To analyze the echocardiographic images in children with corrected transposition of the great arteries（CTGA） before and after double switch operation, and to investigate the value of the echocardiography to the diagnosis of CTGA and the assessment of surgical outcome. Methods In 78 children receiving double switch operation, 42 were performed Senning + arterial switch operation, and 36 children were performed Senning + Rastelli procedure. Echocardiography was done before and one year after operation. The myocardial performance index was recorded and the degree of tricuspid regurgitation was evaluated. The postoperative complications were observed as residual shunt, pulmonary vein obstruction, vena cava obstruction, left ventricular outflow tract obstruction, right ventricular outflow tract obstruction, and heart dysfunction. Results In 78 patients, 72（92.31%） were diagnosed correctly, 6（7.69%） were misdiagnosed, in which 5 were misdiagnosed with double outlet right ventricle and 1 was misdiagnosed with complete transposition of the great arteries. The myocardial performance index of right ventricle and the rate of moderately severe tricuspid regurgitation decreased from 0.48±0.16 and 34.61% preoperatively to 0.36±0.11 and 15.38% postoperatively（t=7.321, P=0.037; Z=-2.880, P=0.004）. The postoperative complications included residual shunt in 12 patients（15.38%）, pulmonary vein obstruction in 7（8.97%）, vena cava obstruction in 3（3.85%）, left ventricular outflow tract obstruction in 7（8.97%）, right ventricular outflow tract obstruction in 14（17.95%）, and heart dysfunction in 12（15.38%）. Conclusions The double switch operation can improve the right ventricular function, and relieve the severity of tricuspid regurgitation. Echocardiography has a high value to the preoperative diagnosis of CTGA and the assessment of surgical outcome.

BACKGROUND: Molecular targeted contrast-enhanced ultrasound (CEUS) imaging is a potential imaging strategy to improve the diagnostic accuracy of conventional ultrasound (US) imaging. US contrast agents are usually micrometer-sized and non-target gas bubbles while nano-sized and targeted agents containing phase-shift materials absorb more attractions for their size and the liquid core and excellent molecular imaging effect. METHODS: PLGA12k-mPEG2k-NH2, DSPE-mPEG2k and perfluorohexan (PFH) were used to construct a new targeted ultrasound contrast agent with CUB domain-containing protein 1 (CDCP1) receptor for the detection and diagnosis of prostate cancer. The potential of tumor-targeted nanoparticles (CDCP1-targeted perfluorohexan-loaded phase-transitional nanoparticles, anti-CDCP1 NPs) as contrast agents for ultrasound (US) imaging was assessed in vitro. Moreover, studies on the cytotoxicity and the targeting ability of anti-CDCP1 NPs assisted by US were carried out. RESULTS: The results showed that anti-CDCP1 NPs had low cytotoxicity, and with the increasing of polymer concentration in anti-CDCP1 NPs, the CEUS imaging of agent gradually enhanced, and enhanced imaging associated with the length of observing time. Furthermore, it was testified that anti-CDCP1 assisted the agent to target cells expressing CDCP1, which demonstrated the active targeting of anti-CDCP1 NPs in vitro. CONCLUSION: All in all, the feasibility of using targeted anti-CDCP1 NPs to enhance ultrasound imaging has been demonstrated in vitro, which laid a solid foundation for molecular US imaging in vivo, and anti-CDCP1 NPs might have a great clinical application prospect.

目的 评价超声心动图对胎儿Ebstein畸形(EA)的诊断价值,建立新的诊断标准,探索EA的预后评估指标.方法 选取产前超声诊断为EA胎儿37例为病例组,孕周与病例组相匹配的正常胎儿37例为对照组,对比分析二者间的超声测量指标;宫内死亡、新生儿死亡及术后死亡者16例为死亡组,进行外科手术及随访者21例为存活组,比较二者间心胸比(CTR)、GOSE评分、动脉导管分流方向、肺动脉发育情况.结果 与对照组比较,病例组胎儿CTR增大(0.49±0.28)vs(0.28±0.05),P＜0.01;二尖瓣前瓣根部与三尖瓣隔瓣根部距离(MTD)明显增大(7.80±2.39)mm vs(3.80±1.20)mm,P＜0.01;肺动脉与主动脉内径之比(PA/AO)明显减低(0.94±0.23)vs(1.12±0.17),P＜0.01.应用 MTD＞4 mm诊断EA,正确28例(75.68％),漏诊9例(24.32％);用二尖瓣前瓣根部距心尖的距离与三尖瓣隔瓣根部距心尖距离比(MTR)＞1.5诊断EA,正确35例(94.59％),漏诊2例(5.41％);二者间差异有统计学意义(P＜0.05).与存活组比较,死亡组CTR＞0.5、G0SE评分＞1.0、动脉导管逆向血流、PA/AO＜1的例数明显增多,差异有统计学意义(P＜0.01).结论 根据MTR＞1.5,胎儿超声可比较准确地诊断EA.CTR＞0.5、G0SE评分＞1.0、动脉导管逆向血流、PA/AO＜1是死亡高危因素.

目的 探讨先天性血管环(vascular ring,VR)的彩色多普勒超声心动图对VR的诊断价值.方法 回顾性分析247例经心外科手术诊断为VR患者的超声心动图检查资料.结果 本组双主动脉弓127例、右位主动脉弓伴迷走左锁骨下动脉及左侧动脉导管未闭/韧带88例、肺动脉吊带29例、左位主动脉弓伴迷走右锁骨下动脉及右侧动脉导管未闭/韧带2例、无名动脉压迫1例.本组单纯性血管环68例,合并其他心血管系统畸形179例,常见的合并畸形有室间隔缺损88例、房间隔缺损/卵圆孔未闭64例、左侧上腔静脉残存29例、动脉导管未闭21例、肺动脉瓣狭窄15例、法洛四联症14例、Kommerell憩室12例.247例患者中,超声诊断符合201例(81.38％);漏误诊46例(18.62％),其中41例漏诊,5例误诊.结论 多普勒超声心动图技术可以比较准确地诊断VR,但容易漏、误诊,必要时需行心脏CTA检查明确诊断.

Objective:To explore the diagnostic value of transrectal ultrasound(TRUS)/multiparametric magnetic resonance imaging(mpMRI) fusion targeted biopsy(FTB) for clinically significant prostate cancer(PCa) detection by using both biopsy histopathology and radical prostatectomy histopathology as reference standards.Methods:A total of 303 consecutive patients with suspicious lesions detected by mpMBI and underwent prostate biopsy at Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine between November 2017 to January 2020 were retrospectively analyzed. All the suspicious lesions were sampled by TRUS/mpMRI FTB in addition with standard 12-core systematic biopsy(SB). The clinically significant PCa detection rates by TRUS/mpMRI FTB and SB were compared by using both biopsy histopathology and radical prostatectomy histopathology as reference standards.Results:The diagnosis of PCa was histologically confirmed in 189 of 303 patients, including 178 patients with clinically significant PCa and 11 patients with clinically insignificant PCa. With biopsy histopathology as reference standard, the clinically significant PCa detection rate of TRUS/mpMRI FTB was statistically higher than SB (57.1% vs 45.9%, P<0.001). Among 189 patients with biopsy proven PCa, 80 patients underwent radical prostatectomy, and the radical prostatectomy histopathology confirmed 79 patients with clinically significant PCa.With radical prostatectomy as reference standard, the clinically significant PCa detection rate of TRUS/mpMRI FTB was statistically higher than SB (91.1% vs 74.7%, P<0.001). Conclusions:Compared with SB, MRI/US FTB can offer more accurate sampling of suspicious lesions on mpMRI, and consequently improve the clinically significant PCa detection rate.

NanomedicineVol. 16, No. 23 EditorialUltrasound-based nanomedicine for molecular imaging of prostate cancer: from diagnostics to theranosticsYunkai Zhu‡, Ying Sun‡, Yourong Duan & Yaqing ChenYunkai Zhu‡ https://orcid.org/0000-0002-5457-7994Department of Ultrasound in Medicine, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, 1665 Kongjiang Road, Shanghai, 200092, PR China, Ying Sun‡State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200032, PR China, Yourong Duan *Author for correspondence: E-mail Address: yrduan@shsci.orghttps://orcid.org/0000-0002-3781-7845State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200032, PR China & Yaqing Chen **Author for correspondence: E-mail Address: chenyaqing@xinhuamed.com.cnhttps://orcid.org/0000-0001-5132-0096Department of Ultrasound in Medicine, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, 1665 Kongjiang Road, Shanghai, 200092, PR ChinaPublished Online:14 Sep 2021https://doi.org/10.2217/nnm-2021-0170AboutSectionsView ArticleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInReddit View articleKeywords: drug deliverynanobubblenanodropletnanomedicineprostate cancertheranosticsultrasoundReferences1. 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Theranostics 8(6), 1665–1677 (2018).Crossref, Medline, CAS, Google ScholarFiguresReferencesRelatedDetails Vol. 16, No. 23 Follow us on social media for the latest updates Metrics Downloaded 81 times History Received 30 April 2021 Accepted 11 August 2021 Published online 14 September 2021 Published in print October 2021 Information© 2021 Future Medicine LtdKeywordsdrug deliverynanobubblenanodropletnanomedicineprostate cancertheranosticsultrasoundFinancial & competing interests disclosureThis project was supported by grants from the National Natural Science Foundation of China (no. 81671708, 81271595, 81572999, 81771839 and 81773272), Shanghai Shen Kang Hospital Development Center Research Project (no. 16CR3092B) and the Fund of Shanghai Jiaotong University (no. YG2014ZD04). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.No writing assistance was utilized in the production of this manuscript.PDF download

Objective:To review the imaging characteristics and evaluate the diagnostic value of Doppler echocardiography for congenitally malposition of septum primum(MSP).Methods:Images of Doppler echocardiography were retrospectirely rewiewed and compared with CTA, operative and necropsy findings in 8 cases with MSP in Xinhua Hospital, and Shanghai Children′s Medical Center, Shanghai Jiaotong University School of Medicine from January 2009 to October 2019.Results:MSP was characterized by the absent of superior limbic band of septum secundum and different degrees of the leftward deviation of septum primum, and the pulmonary veins which connected with the posterior wall of the anatomical left atrium incorporated intothe right atrium. The associated malformations included totally anomalous (7 cases) and partially anomalous (1 case) pulmonary venous drainage directly to the right atrium. Six of 8 cases were diagnosed correctly. In the remaining 2 cases, 1 case misdiagnosed as single atrium, and the other case misdiagnosed as cor triatriatum.Conclusions:MSP could be diagnosed accurately by Doppler echocardiography. This malformation should be distinguished from single atrium and cor triatriatum.

As an adjunct to mammography, ultrasound can improve the detection of breast cancer in women with dense breasts. We aimed to evaluate the diagnostic performance of automated breast ultrasound system (ABUS) and handheld ultrasound (HHUS) in Chinese women with dense breasts, both in combination with mammography and separately. This is a cross-sectional multicenter clinical research study. Nine hundred and thirty-seven women with dense breasts underwent ABUS, HHUS, and mammography at one of five tertiary-care hospitals. The diagnostic performance of ABUS and HHUS was evaluated in combination with mammography, or separately in women with mammography-negative dense breasts. The agreement between ABUS and HHUS in breast cancer detection was also assessed. The sensitivity of the combination of ABUS or HHUS with mammography was 99.1% (219/221), and the specificities were 86.9% (622/716) and 84.9% (608/716), respectively. The area under the curve was 0.93 for ABUS combined with mammography and 0.92 for that of HHUS combined with mammography. Statistically significant agreement between ABUS and HHUS in breast cancer detection was observed (percent agreement = 0.94, κ = 0.85). The incremental cancer detection rate in mammography-negative dense breasts was 42.8 per 1000 ultrasound examinations. Both ABUS and HHUS as adjuncts to mammography can significantly improve the breast cancer detection rate in women with dense breasts, and there is a strong correlation between them. Given the high prevalence of dense breasts and the multiple advantages of ABUS over HHUS, such as less operator dependence and reproducibility, ABUS showed great potential for use in breast cancer early detection, especially in resource-limited areas.