Real-Time Freehand Ultrasound Molecular Imaging with Deep Learning

项目摘要 近年来，早期检测降低了乳腺癌的死亡率；然而，目前的成像工具很差 阳性的预测价值，并可能导致过度诊断。超声分子成像(Umi)是一种很有前途的技术。 该工具可以提供非侵入性、非电离、实时、徒手的乳腺癌肿瘤评估 关爱。UMI使用靶向超声造影剂(UCAS)来区分良恶性 并有可能减少假阳性率和过度诊断。然而，UMI图像质量较差 导致研究人员在实时和徒手成像的优点之间进行权衡，以获得更好的图像质量，从而导致 更长的考试时间，更高的UCA剂量，以及可能错过目标。本项目将开发一种新的实时 基于深度学习的徒手UMI方法实现了对乳腺癌的良好检测。 提出了一种基于时空卷积神经网络的fi粘附性检测方法， 其实时指示疾病，同时抑制来自组织和自由UCA的背景噪声。一种物理学- 将构建乳房UMI的驱动模拟器，并使用体模和水箱测量进行校准。 模拟UMI时间序列的数据库将被组装并用于训练时空CNN以识别 依附于乌卡斯。最后，CNN将部署在一个原型的手绘UMI系统上，运行 超过每秒30帧。该系统将在UMI的乳腺癌检测研究中进行测试 转基因小鼠模型。这项研究将使用针对一种新的生物标记物(B7-H3)的UCA，该标记物是高度特异的fic 对于癌症来说，并且被认为与进展为浸润性乳腺癌的可能性密切相关。 实时手绘方法是SignifiCant，因为它使操作员能够自由询问(和重访) 一次UCA注射多个靶点，减少检查时间和UCA剂量。建议的方法是 创新是因为它使用CNN来更好地利用在UMI期间已经获得的数据。拟议的系统 将能够使用B7-H3的实时手绘UMI以高图像质量检测乳腺癌，有可能 减少假阳性和过度诊断，从而减少不必要的测试、活组织检查、成本和患者痛苦。 K99阶段将提供分子成像、UCA Synthe- SIS和靶向，动物研究设计，小动物UMI，临床UMI和其他需要的专门准备 过渡到UMI的教职和独立的研究生涯，研究成像方法。这些 培训活动将提供完成R00阶段拟议动物研究所需的技能。

Project Summary Earlier detection has reduced breast cancer mortality in recent years; however, current imaging tools have poor positive predictive value and likely contribute to overdiagnosis. Ultrasound molecular imaging (UMI) is a promising tool that can provide noninvasive, non-ionizing, real-time, freehand breast cancer tumor assessment at the point of care. UMI uses targeted ultrasound contrast agents (UCAs) to differentiate between benign and malignant lesions and has the potential to reduce false positive rates and overdiagnosis. However, poor UMI image quality has led researchers to trade the benefits of real-time and freehand imaging for better image quality, resulting in longer exam times, higher UCA dosage, and potentially missed targets. This project will develop a new real-time freehand UMI approach based on deep learning to achieve excellent detection of breast cancer. A spatiotemporal convolutional neural network (CNN) approach is proposed to specifically detect adherent UCAs, which indicate disease, in real-time while suppressing background noise from tissue and free UCAs. A physics- driven simulator of breast UMI will be constructed and calibrated with phantom and water tank measurements. A database of simulated UMI time series will be assembled and used to train the spatiotemporal CNN to identify adherent UCAs. Finally, the CNN will be deployed on a prototype real-time freehand UMI system, operating in excess of 30 frames per second. This system will be tested in a UMI study of breast cancer detection in a transgenic mouse model. This study will use UCAs targeted to a novel biomarker (B7-H3) that is highly specific for cancer and is believed to correlate strongly with likelihood of progression to invasive breast cancer. The real-time freehand approach is significant because it enables the operator to freely interrogate (and revisit) multiple targets with a single UCA injection, reducing exam times and UCA dosage. The proposed approach is innovative because it uses CNNs to better utilize data that is already acquired during UMI. The proposed system will be able to detect breast cancer with high image quality using real-time freehand UMI of B7-H3, potentially reducing false positives and overdiagnosis and thus unnecessary tests, biopsies, costs, and patient distress. The K99 phase will provide dedicated training and career growth opportunities in molecular imaging, UCA synthe- sis and targeting, animal study design, small animal UMI, clinical UMI, and other dedicated preparation needed to transition to a faculty position and an independent research career studying imaging methods for UMI. These training activities will provide the skills necessary to complete the proposed animal study in the R00 phase.