Next-Generation Ultrasound Localization Microscopy

下一代超声定位显微镜

• 批准号: 10039725
• 负责人: Pengfei Song
• 金额: $ 56.53万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2024-09-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10039725
• 关键词: 3-Dimensional; 3D ultrasound; 4T1; Address; Adopted; Algorithms; Alzheimer' s Disease; Animals; Biological Markers; Blood; Blood Vessels; Blood capillaries; Blood flow; Breast Carcinoma; Cardiovascular Diseases; Chickens; Clinic; Clinical; Clinical Treatment; Complex; Data; Detection; Development; Diagnosis; Disease; Embryo; Evaluation; Goals; Health; Hour; Human; Illinois; Image; Image Analysis; Imaging Device; Imaging Techniques; Incentives; Inflammation; Knowledge; Label; Magnetic Resonance Imaging; Malignant Neoplasms; Measures; Medicine; Metabolic; Methods; Microbubbles; Microcirculation; Microscopy; Modality; Modeling; Modification; Monitor; Morphologic artifacts; Mus; Neurodegenerative Disorders; Noise; Nutrient; Organ; Oxygen; Pathogenesis; Pathology; Patients; Penetration; Performance; Property; Provider; Resolution; Series; Signal Transduction; Skin; Speed; Structure; Surface; Techniques; Three-Dimensional Imaging; Time; Tissues; Training; Transducers; Transportation; Ultrasonic Transducer; Ultrasonography; Universities; Variant; Vendor; X-Ray Computed Tomography; base; clinical Diagnosis; clinical application; clinical imaging; clinical practice; clinically relevant; clinically significant; comparative; computerized data processing; cost; data acquisition; deep learning; deep neural network; hemodynamics; imaging detection; imaging modality; in vivo; in vivo imaging; innovation; instrumentation; microCT; microscopic imaging; next generation; non-invasive imaging; novel; novel therapeutics; optical imaging; parallel computer; performance tests; quantitative ultrasound; real-time images; therapy development; tool; tumor; two photon microscopy

Project Summary/Abstract Abnormal alterations of tissue microcirculation are often associated with early stage of tissue pathology. Detection and characterization of these early microvascular abnormalities can greatly benefit clinical diagnosis and treatment monitoring as well as facilitating the creation of new therapies to counter disease development. For decades, there has been a longstanding quest for the development of a clinical imaging modality that can noninvasively and directly image such tissue microvascular variations. To date, however, such an imaging method remains elusive due to the fundamental compromise between imaging spatial resolution and depth penetration. Therefore, the long-term objective of this project is to fulfill this unmet clinical need by developing the next-generation ultrasound localization microscopy (ULM), which is an ultrasound-based imaging technique that can directly assess structural and functional tissue microvasculature in vivo in humans at a clinically relevant depth. Different from other imaging modalities, ULM is not limited by the resolution-penetration compromise: ULM can noninvasively image capillary-scale microvessels at several centimeters depth and quantitatively measure their blood flow speed (as low as 1 mm/s). Such combination of deep imaging penetration and exquisite spatial resolution and the unique functionality of measuring small vessel blood flow speed make ULM a promising technique for many clinical applications including cancer and cardiovascular diseases. At present, however, ULM is not ready for clinical use due to several key technical limitations: 1) ULM data acquisition is very slow (tens of seconds with breath holding); 2) ULM post-processing is very expensive computationally (several hours to generate a single 2D ULM image); 3) ULM is difficult to be extended to 3D imaging (which is important for comprehensive evaluation of tissue microvasculature such as in cancer applications). These limitations largely forbids ULM from being effectively used in the clinic to provide useful microvascular biomarkers. In this proposal, we will concentrate on addressing these technical barriers and transform ULM to a truly useful clinical imaging tool. Our approach synergistically combines deep learning (DL), parallel computing, and ultrafast 3D ultrasound imaging to fundamentally shorten ULM data acquisition time, substantially accelerate ULM post-processing, and enhance ULM to 3D imaging. Our first aim will develop and validate DL-based ULM data processing algorithms that would enable real-time 4D morphometric ULM and fast 3D quantitative ULM. Our method uniquely collects real labeled optical imaging data on a chicken embryo microvessel model for DL training. Our second aim will focus on realizing 3D-ULM on a 2D row-column-addressing transducer with ultrafast 3D plane wave imaging. We will develop a DL-based beamforming technique to enable high-fidelity 3D microbubble imaging for robust 3D-ULM. Our final aim will focus on validating the in vivo performance of the newly developed 3D-ULM imaging techniques on a mouse tumor model. We will be collaborating with world-renowned experts in deep learning, optical imaging, and comparative medicine at the University of Illinois to accomplish these aims of the proposal.

项目总结/摘要 组织微循环的异常改变通常与组织病理学的早期阶段相关。 这些早期微血管异常的检测和表征可以极大地有利于临床诊断 和治疗监测以及促进新疗法的创造以对抗疾病的发展。 几十年来，人们一直在寻求开发一种临床成像模式， 对这种组织微血管变化进行非侵入性和直接成像。然而，迄今为止，这种成像 由于成像空间分辨率和深度之间的根本折衷， 渗透。因此，本项目的长期目标是通过开发 下一代超声定位显微镜（乌尔姆），这是一种基于超声的成像技术 其可以在临床相关的条件下直接评估人体内的结构和功能组织微血管系统， 深入与其他成像模式不同，乌尔姆不受分辨率-穿透折衷的限制： 乌尔姆可以无创地对几厘米深的毛细血管进行定量成像， 测量他们的血液流速（低至1 mm/s）。这种结合深刻的成像渗透和精致 空间分辨率和测量小血管血流速度的独特功能使乌尔姆成为一种有前途的 该技术用于许多临床应用，包括癌症和心血管疾病。然而，目前，乌尔姆 由于几个关键的技术限制，ULM还没有准备好用于临床使用：1）乌尔姆数据采集非常慢（数十个 2）乌尔姆后处理在计算上是非常昂贵的（几个小时， 生成单个2D乌尔姆图像）; 3）乌尔姆难以扩展到3D成像（这对于 组织微脉管系统的综合评价，例如在癌症应用中）。这些限制在很大程度上 阻止了乌尔姆在临床上有效地用于提供有用的微血管生物标志物。在这项提案中， 我们将集中精力解决这些技术障碍，并将乌尔姆转变为真正有用的临床成像 工具.我们的方法协同结合了深度学习（DL），并行计算和超快3D超声 成像以从根本上缩短乌尔姆数据采集时间，显著加速乌尔姆后处理，以及 将乌尔姆增强为3D成像。我们的第一个目标是开发和验证基于DL的乌尔姆数据处理算法 这将实现实时4D形态测量乌尔姆和快速3D定量乌尔姆。我们的方法独特地收集 用于DL训练的鸡胚微血管模型上的真实的标记的光学成像数据。我们的第二个目标是 专注于在具有超快3D平面波成像的2D行列寻址换能器上实现3D-ULM。 我们将开发一种基于DL的波束形成技术，以实现高保真3D微泡成像， 3D-ULM。我们的最终目标将集中于验证新开发的3D-ULM成像的体内性能 技术在小鼠肿瘤模型上的应用。我们将与世界知名的深度学习专家合作， 光学成像和比较医学在伊利诺伊大学，以实现这些目标的建议。