Measuring proximity to standard planes during fetal brain ultrasound scanning
Wellcome/EPSRC center for Interventional and Surgical Sciences (WEISS)（Wellcome/EPSRC介入与外科科学中心（WEISS））；University College London (UCL)（伦敦大学学院（UCL））；Dept of Computer Science, UCL（伦敦大学学院计算机科学系）；Dept of Obstetrics and Gynecology, University Hospital of Rennes（雷恩大学医院妇产科）；Univ Rennes, INSERM, LTSI - UMR 1099（雷恩大学、法国国家健康与医学研究院、图像与信号处理实验室 - UMR 1099）；Fetal Medicine Unit, Elizabeth Garrett Anderson Wing, UCLH NHS Foundation Trust（伦敦大学学院医院NHS基金会信托伊丽莎白·加勒特·安德森翼胎儿医学科）；Elizabeth Garrett Anderson Institute for Women’s Health, UCL（伦敦大学学院伊丽莎白·加勒特·安德森妇女健康研究所）；Dept of Obstetrics and Gynecology, UCLH（伦敦大学学院医院妇产科）
Chiara Di Vece, Antonio Cirigliano, Meala Le Lous, Raffaele Napolitano, Anna L. David, Donald Peebles, Pierre Jannin, Francisco Vasconcelos, Danail Stoyanov
该研究提出一种半监督分割模型结合6D平面姿态回归的流水线，在NVIDIA Clara AGX边缘设备上实现实时接近度检测，可辅助胎儿脑部超声扫描的导航支持。
摘要：
本文提出了一种旨在使超声（US）平面姿态估计更贴近临床应用的流水线，论证了为胎儿脑部标准平面（SPs）导航提供连续、实时接近度反馈的可行性。我们提出了一种半监督分割模型，该模型使用来自3D超声体积（非SPs）的标注SPs和未标注切片，在SPs上达到0.93的平均交并比（mIoU），在任意非SPs上达到0.86的mIoU。该模型包含分类机制，用于识别和过滤掉不含胎儿脑部的帧，并为包含胎儿脑部的帧生成掩码，增强了平面姿态回归在临床场景中的相关性。结合6D平面姿态回归，我们的流水线提供了无传感器、连续的SPs接近度检测，具有实时距离指标，而非二元平面识别。此外，我们通过在NVIDIA Clara AGX边缘设备上部署该系统来验证其可移植性，达到39 Hz的实时推理速度，超过了标准临床采集率。与之前在精选体积切片上验证的方法不同，我们回顾性地评估了来自17位不同专业水平的超声医师的真实胎儿扫描视频上的流水线：操作者在接近接近度信号的局部最小值时冻结，而非精确在该点冻结，这与临床冻结时机行为一致，而仅靠接近度无法预测专家的SP质量评分。该方法补充了现有的胎儿超声技术，是产前扫描中基于图像的导航支持的一步。
英文摘要：
This paper presents a pipeline designed to bring ultrasound (US) plane pose estimation closer to clinical use, demonstrating the feasibility of continuous, real-time proximity feedback for navigation to the standard planes (SPs) in the fetal brain. We propose a semi-supervised segmentation model that uses labeled SPs and unlabeled slices from 3D US volumes (non-SPs), achieving 0.93 mean Intersection over Union (mIoU) on SPs and 0.86 mIoU on arbitrary non-SPs. The model incorporates a classification mechanism to identify and filter out frames lacking the fetal brain, and to generate masks for those containing it, enhancing the relevance of plane pose regression in clinical settings. Combined with 6D plane pose regression, our pipeline provides sensorless, continuous proximity detection to SPs with real-time distance metrics rather than binary plane recognition. Furthermore, we validate its translational viability by deploying the system on an NVIDIA Clara AGX edge device, achieving a real-time inference speed of 39 Hz, which exceeds standard clinical acquisition rates. Unlike prior methods validated on curated volume slices, we evaluate the pipeline retrospectively on real fetal scan videos from 17 sonographers of varying expertise: operators freeze near, rather than exactly at, the local minima of the proximity signal, consistent with clinical freeze-timing behavior, whereas proximity alone does not predict expert SP quality scores. The approach complements existing fetal US technologies and is a step toward image-based navigation support in prenatal scanning.
FetalCLIP: A Visual-Language Foundation Model for Fetal Ultrasound Image Analysis
Department of Computer Vision（计算机视觉系）；Mohamed bin Zayed University of Artificial Intelligence（马尔代夫比兹人工智能大学）；Department of Machine Learning（机器学习系）；Corniche Hospital, Abu Dhabi Health Services Company (SEHA)（阿布扎赫尔医院，阿布扎赫健康服务公司（SEHA））
FUSEP: A Multi-Center Benchmark for Diverse Tasks in Early Pregnancy Fetal Ultrasound Screening
Hunan University（湖南大学）；The Hong Kong University of Science and Technology（香港科技大学）；Anhui University（安徽大学）；Shenzhen Maternity and Child Healthcare Hospital（深圳市妇幼保健院）；Sichuan Provincial Maternity and Child Health Care Hospital（四川省妇幼保健院）；National University of Singapore（新加坡国立大学）；Yunnan Maternal and Child Health Hospital（云南省妇幼保健院）；Zhejiang University（浙江大学）
Sano Centre for Computational Medicine（萨诺计算医学中心）；Warsaw University of Technology（华沙理工大学）；Fetai Health Ltd.（Fetai健康有限公司）；Medical University of Warsaw（华沙医科大学）；Tooploox（Tooploox公司）
In this paper, we propose an end-to-end multi-task neural network called FetalNet with an attention mechanism and stacked module for spatio-temporal fetal ultrasound scan video analysis. Fetal biometric measurement is a standard examination during pregnancy used for the fetus growth monitoring and estimation of gestational age and fetal weight. The main goal in fetal ultrasound scan video analysis is to find proper standard planes to measure the fetal head, abdomen and femur. Due to natural high speckle noise and shadows in ultrasound data, medical expertise and sonographic experience are requ…
SonoCLIP: Mask-Guided Region-Aware Vision-Language Pretraining for Fetal Ultrasound Analysis
School of Computer Science, Wuhan University, Wuhan, China（武汉大学计算机学院，武汉，中国）；Institute of Artificial Intelligence, Wuhan University, Wuhan, China（武汉大学人工智能研究院，武汉，中国）；Department of Ultrasound, Renmin Hospital of Wuhan University, Wuhan, China（武汉大学仁民医院超声科，武汉，中国）；National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China（武汉大学多媒体软件国家工程研究中心，武汉，中国）；Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China（湖北省多媒体与网络通信工程重点实验室，武汉大学，武汉，中国）
Explainable fetal ultrasound quality assessment with progressive concept bottleneck models
Technical University of Denmark（丹麦技术大学）；Slagelse Hospital（斯莱格瑟医院）；CAMES；Copenhagen University Hospital Rigshospitalet（哥本哈根大学医院丽格医院）；University of Copenhagen（哥本哈根大学）
Semi-supervised learning methods have achieved excellent performance on standard benchmark datasets using very few labelled images. Anatomy classification in fetal 2D ultrasound is an ideal problem setting to test whether these results translate to non-ideal data. Our results indicate that inclusion of a challenging background class can be detrimental and that semi-supervised learning mostly benefits classes that are already distinct, sometimes at the expense of more similar classes.
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
To assess fetal health during pregnancy, doctors use the gestational age (GA) calculation based on the Crown Rump Length (CRL) measurement in order to check for fetal size and growth trajectory. However, GA estimation based on CRL, requires proper positioning of calipers on the fetal crown and rump view, which is not always an easy plane to find, especially for an inexperienced sonographer. Finding a slightly oblique view from the true CRL view could lead to a different CRL value and therefore incorrect estimation of GA. This study presents an AI-based method for a quality assessment of the CR…
Determining Fetal Orientations From Blind Sweep Ultrasound Video
Technical University of Denmark（丹麦技术大学）；University of Copenhagen（哥本哈根大学）；Slagelse Hospital（斯劳厄尔瑟医院）；CAMES Rigshospitalet（CAMES 哥本哈根大学医院）；Pioneer Centre for AI（人工智能先锋中心）
HDC: Hierarchical Distillation for Multi-level Noisy Consistency in Semi-Supervised Fetal Ultrasound Segmentation
University of Information Technology（信息科技大学）；Ho Chi Minh University of Technology（胡志明市技术大学）；University of Science（科学大学）；Vietnam National University（越南国家大学）；Boston University（波士顿大学）；Taipei Medical University（台北医学大学）；Posts and Telecommunications Institute of Technology（邮电技术学院）；New Mexico State University（新墨西哥州立大学）
This paper addresses the task of detecting and localising fetal anatomical regions in 2D ultrasound images, where only image-level labels are present at training, i.e. without any localisation or segmentation information. We examine the use of convolutional neural network architectures coupled with soft proposal layers. The resulting network simultaneously performs anatomical region detection (classification) and localisation tasks. We generate a proposal map describing the attention of the network for a particular class. The network is trained on 85,500 2D fetal Ultrasound images and their as…
Multi-Center Study on Deep Learning-Assisted Detection and Classification of Fetal Central Nervous System Anomalies Using Ultrasound Imaging
School of Mathematics and Statistics, Xiamen University of Technology（厦门理工学院数学与统计学院）；Department of Obstetrics, Xiamen University Affiliated First Hospital（厦门大学附属第一医院妇产科）
Google Health（谷歌健康）；University of Zambia School of Medicine（赞比亚大学医学院）；University of North Carolina School of Medicine（北卡罗来纳大学医学院）；UNC Global Projects – Zambia, LLC（北卡罗来纳大学全球项目赞比亚有限责任公司）
Despite considerable progress in maternal healthcare, maternal and perinatal deaths remain high in low-to-middle income countries. Fetal ultrasound is an important component of antenatal care, but shortage of adequately trained healthcare workers has limited its adoption. We developed and validated an artificial intelligence (AI) system that uses novice-acquired "blind sweep" ultrasound videos to estimate gestational age (GA) and fetal malpresentation. We further addressed obstacles that may be encountered in low-resourced settings. Using a simplified sweep protocol with real-time AI feedback …
Learning to learn skill assessment for fetal ultrasound scanning
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford（生物医学工程研究所，工程科学系，牛津大学）；Department of Medical Physics and Biomedical Engineering, University College London（医学物理与生物医学工程系，伦敦大学学院）；Nuffield Department of Women’s & Reproductive Health, University of Oxford（妇女与生殖健康系，牛津大学）；Rabin-Beilinson Medical Center, Tel-Aviv University Faculty of Medicine（拉宾-贝利松医疗中心，特拉维夫大学医学院）
Objective. This work investigates the use of deep convolutional neural networks (CNN) to automatically perform measurements of fetal body parts, including head circumference, biparietal diameter, abdominal circumference and femur length, and to estimate gestational age and fetal weight using fetal ultrasound videos. Approach. We developed a novel multi-task CNN-based spatio-temporal fetal US feature extraction and standard plane detection algorithm (called FUVAI) and evaluated the method on 50 freehand fetal US video scans. We compared FUVAI fetal biometric measurements with measurements made …
Epistemic-aware Vision-Language Foundation Model for Fetal Ultrasound Interpretation
National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University（国家多媒体软件工程技术研究中心，武汉大学计算机学院）；College of Computing and Data Science, Nanyang Technological University（computing and Data Science学院，南洋理工大学）
FAMSeg: Fetal Femur and Cranial Ultrasound Segmentation Using Feature-Aware Attention and Mamba Enhancement
Guangxi Key Laboratory of Machine Vision and Intelligent Control（广西机器视觉与智能控制重点实验室）；Wuzhou University（梧州大学）；Faculty of Innovation Engineering（创新工程学院）；Macau University of Science and Technology（澳门科技大学）；Guilin University of Electronic Technology（桂林电子科技大学）；Shenzhen Maternity and Child Healthcare Hospital（深圳妇幼保健医院）；Southern Medical University（南方医科大学）；College of Big Data and Software Engineering（大数据与软件工程学院）；College of Electronical and Information Engineering（电子与信息工程学院）
ERSR: An Ellipse-constrained pseudo-label refinement and symmetric regularization framework for semi-supervised fetal head segmentation in ultrasound images
School of Software, Northwestern Polytechnical University（软件学院，西北工业大学）；School of Computer Science and Technology, Wuhan University of Science and Technology（计算机科学与技术学院，武汉科技大学）；Department of Computer Science, School of Engineering, Shantou University（计算机科学系，汕大工程学院）；Department of Ultrasound, Peking University Shenzhen Hospital（超声科，北京大学深圳医院）；School of Intelligence Science and Technology, Nanjing University（智能科学与技术学院，南京大学）；Australian Institute of Health Innovation (AIHI), Macquarie University（澳大利亚健康创新研究所（AIHI），麦考瑞大学）；Faculty of Applied Science, Macao Polytechnic University（应用科学学院，澳门理工学院）；School of Computer Software, College of Intelligence and Computing, Tianjin University（计算机软件学院，智能计算学院，天津大学）
Ultrasound (US) imaging is highly effective with regards to both cost and versatility in real-time diagnosis; however, determination of fetal gender by US scan in the early stages of pregnancy is also a cause of sex-selective abortion. This work proposes a deep learning object detection approach to accurately mask fetal gender in US images in order to increase the accessibility of the technology. We demonstrate how the YOLOv5L architecture exhibits superior performance relative to other object detection models on this task. Our model achieves 45.8% AP[0.5:0.95], 92% F1-score and 0.006 False Po…
Cross-Modal Ultrasound-MRI Learning for Fetal Brain Ventricular Volumetry and Abnormality Screening
Harvard Medical School（哈佛医学院）；Shenzhen University（深圳大学）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；School of Biomedical Engineering and Informatics, Nanjing Medical University（南京医科大学生物医学工程与信息学院）
Learning semantic image quality for fetal ultrasound from noisy ranking annotation
DTU Compute, Technical University of Denmark（丹麦技术大学DTU计算学院）；University of Copenhagen（哥本哈根大学）；Copenhagen Academy of Medical Education and Simulation (CAMES), Rigshospitalet（哥本哈根医学教育与模拟学院（CAMES），国立医院（Rigshospitalet））；Pioneer Centre for AI, Copenhagen（哥本哈根人工智能先锋中心）
Romanian Institute of Science and Technology（罗马尼亚科学技术研究所）；University of Medicine and Pharmacy of Craiova（克拉约瓦医药大学）
The present paper puts forward an incipient study that uses a traditional segmentation method based on Zernike moments for extracting significant features from frames of fetal echocardiograms from first trimester color Doppler examinations. A distance based approach is then used on the obtained indicators to classify frames of three given categories that should be present in a normal heart condition. The computational tool shows promise in supporting the obstetrician in a rapid recognition of heart views during screening.
Fusing Radiomic Features with Deep Representations for Gestational Age Estimation in Fetal Ultrasound Images
Research Ireland Centre for Research Training in Machine Learning（爱尔兰研究培训机器学习研究中心）；School of Medicine, University College Dublin（都柏林大学医学院）；School of Veterinary Medicine, University College Dublin（都柏林大学兽医学院）；School of Computer Science, University College Dublin（都柏林大学计算机科学学院）
Imperial College London（伦敦帝国学院）；King’s College London（伦敦国王学院）；St Thomas’ Hospital（圣托马斯医院）
Prenatal screening with ultrasound can lower neonatal mortality significantly for selected cardiac abnormalities. However, the need for human expertise, coupled with the high volume of screening cases, limits the practically achievable detection rates. In this paper we discuss the potential for deep learning techniques to aid in the detection of congenital heart disease (CHD) in fetal ultrasound. We propose a pipeline for automated data curation and classification. During both training and inference, we exploit an auxiliary view classification task to bias features toward relevant cardiac stru…
Isfahan University of Technology（伊斯法罕理工大学）；University of Michigan（密歇根大学）
Ultrasound imaging is a standard examination during pregnancy that can be used for measuring specific biometric parameters towards prenatal diagnosis and estimating gestational age. Fetal head circumference (HC) is one of the significant factors to determine the fetus growth and health. In this paper, a multi-task deep convolutional neural network is proposed for automatic segmentation and estimation of HC ellipse by minimizing a compound cost function composed of segmentation dice score and MSE of ellipse parameters. Experimental results on fetus ultrasound dataset in different trimesters of …
Technical University of Denmark（丹麦技术大学）；CAMES Rigshospitalet（哥本哈根大学医院CAMES中心）
Confounding information in the form of text or markings embedded in medical images can severely affect the training of diagnostic deep learning algorithms. However, data collected for clinical purposes often have such markings embedded in them. In dermatology, known examples include drawings or rulers that are overrepresented in images of malignant lesions. In this paper, we encounter text and calipers placed on the images found in national databases containing fetal screening ultrasound scans, which correlate with standard planes to be predicted. In order to utilize the vast amounts of data a…
University College London（伦敦大学学院）；Elizabeth Garrett Anderson Institute for Women’s Health, University College London（伦敦大学学院伊丽莎白·加勒特·安德森妇女健康研究所）；NIHR University College London Hospitals Biomedical Research Centre（英国国家健康研究所伦敦大学学院医院生物医学研究中心）
During pregnancy, ultrasound examination in the second trimester can assess fetal size according to standardized charts. To achieve a reproducible and accurate measurement, a sonographer needs to identify three standard 2D planes of the fetal anatomy (head, abdomen, femur) and manually mark the key anatomical landmarks on the image for accurate biometry and fetal weight estimation. This can be a time-consuming operator-dependent task, especially for a trainee sonographer. Computer-assisted techniques can help in automating the fetal biometry computation process. In this paper, we present a uni…
Sagol Brain Institute, Tel Aviv Sourasky Medical Center（特拉维夫苏拉斯基医疗中心萨戈尔脑研究所）；University College London（伦敦大学学院）
Fetal growth assessment from ultrasound is based on a few biometric measurements that are performed manually and assessed relative to the expected gestational age. Reliable biometry estimation depends on the precise detection of landmarks in standard ultrasound planes. Manual annotation can be time-consuming and operator dependent task, and may results in high measurements variability. Existing methods for automatic fetal biometry rely on initial automatic fetal structure segmentation followed by geometric landmark detection. However, segmentation annotations are time-consuming and may be inac…
General Methods Make Great Domain-specific Foundation Models: A Case-study on Fetal Ultrasound
University of Copenhagen（哥本哈根大学）；Technical University of Denmark（丹麦技术大学）；Copenhagen Academy of Medical Education and Simulation (CAMES)（哥本哈根医学教育与模拟学院）；Copenhagen University Hospital Rigshospitalet（哥本哈根大学医院拉格比医院）；Brown University（布朗大学）；Pioneer Centre for AI（先锋人工智能中心）
One of the routine examinations that are used for prenatal care in many countries is ultrasound imaging. This procedure provides various information about fetus health and development, the progress of the pregnancy and, the baby's due date. Some of the biometric parameters of the fetus, like fetal head circumference (HC), must be measured to check the fetus's health and growth. In this paper, we investigated the effects of using multi-scale inputs in the network. We also propose a light convolutional neural network for automatic HC measurement. Experimental results on an ultrasound dataset of …
National Institute for Mathematical Sciences（国家数学科学研究所）；Yonsei University（延世大学）；Yonsei University College of Medicine（延世大学医学院）
Ultrasound diagnosis is routinely used in obstetrics and gynecology for fetal biometry, and owing to its time-consuming process, there has been a great demand for automatic estimation. However, the automated analysis of ultrasound images is complicated because they are patient-specific, operator-dependent, and machine-specific. Among various types of fetal biometry, the accurate estimation of abdominal circumference (AC) is especially difficult to perform automatically because the abdomen has low contrast against surroundings, non-uniform contrast, and irregular shape compared to other paramet…
Towards Objective Obstetric Ultrasound Assessment: Contrastive Representation Learning for Fetal Movement Detection
Department of Electrical and Computer Systems Engineering, Monash University（莫纳什大学电气与计算机系统工程系）；AIM for Health Lab, Faculty of Information Technology, Monash University（健康AIM实验室，信息技术学院，莫纳什大学）；School of Biomedical Engineering, University of Sydney（悉尼大学生物医学工程学院）；Department of Chemical & Biological Engineering, Monash University（莫纳什大学化学与生物工程系）；Department of Obstetrics & Gynaecology, Monash University（莫纳什大学妇产科系）
Identifying and interpreting fetal standard scan planes during 2D ultrasound mid-pregnancy examinations are highly complex tasks which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as we…
Shenzhen University（深圳大学）；Nanfang Medical University（南方医科大学）；The Chinese University of Hong Kong（香港中文大学）
Volumetric ultrasound has great potentials in promoting prenatal examinations. Automated solutions are highly desired to efficiently and effectively analyze the massive volumes. Segmentation and landmark localization are two key techniques in making the quantitative evaluation of prenatal ultrasound volumes available in clinic. However, both tasks are non-trivial when considering the poor image quality, boundary ambiguity and anatomical variations in volumetric ultrasound. In this paper, we propose an effective framework for simultaneous segmentation and landmark localization in prenatal ultra…
FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound
Guangxi Key Laboratory of Machine Vision and Intelligent Control, Wuzhou University（广西机器视觉与智能控制重点实验室，梧州大学）；College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；Department of Ultrasound, Shenzhen Maternal and Child Healthcare Hospital（深圳妇幼保健医院超声科）；Department of Ultrasound, Sichuan Provincial Maternity and Child Healthcare Hospital（四川省妇幼保健院超声科）；Faculty of Humanities and Arts, Macau University of Science and Technology（澳门科技大学人文艺术学院）
Sano Centre for Computational Medicine（萨诺计算医学中心）；Informatics Institute, University of Amsterdam（阿姆斯特丹大学信息学研究所）；The Medical Centre of Postgraduate Education（研究生教育医学中心）；Poznan University of Medical Sciences（波兹南医科大学）；Medical University of Warsaw（华沙医科大学）；Warsaw University of Technology（华沙理工大学）
Predicting fetal weight at birth is an important aspect of perinatal care, particularly in the context of antenatal management, which includes the planned timing and the mode of delivery. Accurate prediction of weight using prenatal ultrasound is challenging as it requires images of specific fetal body parts during advanced pregnancy which is difficult to capture due to poor quality of images caused by the lack of amniotic fluid. As a consequence, predictions which rely on standard methods often suffer from significant errors. In this paper we propose the Residual Transformer Module which exte…
In this work, we present an empirical study of DCGANs, including hyperparameter heuristics and image quality assessment, as a way to address the scarcity of datasets to investigate fetal head ultrasound. We present experiments to show the impact of different image resolutions, epochs, dataset size input, and learning rates for quality image assessment on four metrics: mutual information (MI), Fréchet inception distance (FID), peak-signal-to-noise ratio (PSNR), and local binary pattern vector (LBPv). The results show that FID and LBPv have stronger relationship with clinical image quality score…
Imperial College London（帝国理工学院）；King’s College London（伦敦国王学院）
Standard scan plane detection in fetal brain ultrasound (US) forms a crucial step in the assessment of fetal development. In clinical settings, this is done by manually manoeuvring a 2D probe to the desired scan plane. With the advent of 3D US, the entire fetal brain volume containing these standard planes can be easily acquired. However, manual standard plane identification in 3D volume is labour-intensive and requires expert knowledge of fetal anatomy. We propose a new Iterative Transformation Network (ITN) for the automatic detection of standard planes in 3D volumes. ITN uses a convolutiona…
USFetal: Tools for Fetal Brain Ultrasound Compounding
A. I. Virtanen Institute for Molecular Sciences, Faculty of Health Sciences, University of Eastern Finland（A.I.维塔内恩分子科学研究所，健康科学学院，东芬兰大学）；Munich Center for Machine Learning (MCML) at the Technical University of Munich（慕尼黑技术大学机器学习中心（MCML））；Department of Radiology, Massachusetts General Hospital（麻省总医院放射科）；Harvard Medical School and Boston Children’s Hospital（哈佛医学院和波士顿儿童医院）
