Deep Learning Image Enhancement for Point of Care Ultrasound

用于床旁超声的深度学习图像增强

• 批准号: 10614918
• 负责人: Ouwen Huang
• 金额: $ 3.91万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-03 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10614918
• 关键词: Abdomen; Acoustics; Adoption; Algorithms; Anatomy; Android; Apple; Architecture; Benchmarking; Blood Vessels; Cardiac; Cardiovascular system; Career Choice; Clinical; Cloud Computing; Clutterings; Computer software; Custom; Data; Devices; Diagnostic; Fatty acid glycerol esters; Future; Generations; Human; Image; Image Analysis; Image Enhancement; Impairment; Liver; Medical; Modeling; Morphologic artifacts; Muscle; Noise; Output; Overweight; Performance; Process; Provider; Publishing; Reader; Research; Research Personnel; Research Proposals; Running; Signal Transduction; Source; Speed; Sum; System; Tablet Computer; Time; Tissues; Training; Translating; Ultrasonography; Work; X-Ray Computed Tomography; abdominal wall; acoustic imaging; attenuation; clinical application; clinical translation; cost; data modeling; deep learning; deep learning model; experimental study; fetal; handheld mobile device; image processing; image reconstruction; imaging modality; imaging properties; improved; in silico; in vivo; interest; learning strategy; novel; obese patients; open source; operation; patient population; performance tests; point of care; portability; preference; real-time images; rib bone structure; signal processing; simulation; skills; tool; translational barrier; ultrasound

Project Summary Ultrasound has many clinical applications due to it’s non-invasive, non-ionizing, and real-time imaging properties. However, ultrasound still relies heavily on operator skills for image acquisition and interpretation. Operator skill is especially challenged in overweight and obese patient populations where imaging artifacts such as acoustic clutter are more prominent and decrease anatomical conspicuity. To decrease the interpretation burden faced by operators, we aim to develop a deep learning framework for real-time acoustic clutter artifact suppression. We generate preliminary in silico training data using a configurable cloud-compute tool that scales to an 8000 CPU cluster. This tool is ideal for deep learning methods as it significantly speeds up the turnaround time for simulating unique ultrasound acquisition configurations enabling data generation in days as opposed to months. In this project, we will open-source our cloud-compute simulations tools, improve our current in silico data model of acoustic clutter by incorporating human abdominal wall tissue information from medical CT scans, and assess our clutter correction model’s performance on in vivo data. To translate our model’s results for medical provider interpretation, image post-processing is necessary. In our recently published work, MimickNet, we use deep learning methods to successfully approximate post-processing algorithms found on some of the best clinical-grade ultrasound scanners. We propose extending MimickNet to incorporate post-processing approximations for anatomy-specific use cases such as cardiac and vascular imaging. This will provide more off-the-shelf tooling for researchers to translate their algorithmic research into image forms familiar to providers, thus easing clinical translation. Lastly, portable ultrasound hardware has significantly decreased in cost, enabling the widespread use of mobile point-of-care ultrasound (POCUS). Since many consumer devices contain hardware accelerators specific for deep learning applications, there is an opportunity to correct ultrasound artifacts in real-time, even while constrained to mobile hardware. Our preliminary data show that beamforming operations and MimickNet can run at > 100 frames-per-second on an NVIDIA P100 GPU. We propose developing a framework to transfer our image processing pipeline completely onto mobile hardware accelerators. This work will enable translating novel image processing algorithms as easy as downloading software. Our work in developing a deep learning framework for POCUS systems covers the full image reconstruction pipeline from simulated data to producing a clinical-grade image familiar to providers. This framework will provide a rapid translational path for improving ultrasound imaging quality on cheap and widely available mobile hardware.

项目摘要 超声具有许多临床应用，这是由于其无创，非离子化和实时成像 属性。 操作员的技能尤其是挑战，肥胖的患者人群成像伪像 例如，声学杂物更为突出，并减少了解剖结构 运营商面临的解释负担，我们旨在为实时开发一个深度学习框架 声学混乱的伪影抑制。 我们使用可配置的云计算工具工具在计算机培训培训数据中生成初步 8000 CPU群集。 模拟唯一独特的超声采集配置的周转时间在几天内启用数据生成 与这个项目相反。 通过合并人类腹壁组织信息，声学混乱的硅数据模型中的电流 从医疗CT扫描中，评估我们的混乱校正模型在体内数据上的性能。 为了翻译我们的模型的医疗提供者解释结果，图像后处理是 在我们最近发表的工作中，我们使用深度学习方法 在一些最佳的临床级超声扫描仪上发现了近似后处理算法 提出扩展Mimicknet以纳入特定于解剖学的后处理 诸如CardiACC和血管成像之类的案例为研究人员提供了更多的货架工具。 将算法研究转化为图像形成家庭的图像，从而减轻临床翻译。 最后，便携式超声已经大大降低了成本，使得广泛 使用移动点超声波（POCUS）。 特定于深度学习应用的加速器，有一个开放的选择 实时，即使分解为移动硬件。 操作和Mimicknet可以在我们提出的NVIDIA P100上以> 100帧的速度运行 开发一个框架以将我们的图像处理管道Compeline Compeline Compeline转移到移动硬件上 加速器。 软件。 我们为Pocus系统开发深度学习框架的工作涵盖了完整的图像 从模拟数据到产生临床级图像familia到提供者的重建管道 框架将提供快速的转化路径，以改善廉价和 广泛可用的移动硬件。