Deep Learning Image Enhancement for Point of Care Ultrasound

项目摘要 超声具有无创、非电离、实时成像等特点，在临床上有着广泛的应用 特性.然而，超声仍然严重依赖于操作者的图像采集和解释技能。 在超重和肥胖患者人群中，操作员技能尤其具有挑战性， 例如声学杂波更加突出并降低了解剖学的显著性。降低 为了减轻运营商面临的解释负担，我们的目标是开发一个深度学习框架， 声杂波伪影抑制 我们使用可配置的云计算工具生成初步的计算机训练数据， 8000 CPU集群。该工具非常适合深度学习方法，因为它可以显著加快 用于模拟独特超声采集配置的周转时间，可在数天内生成数据 而不是数月在这个项目中，我们将开源我们的云计算模拟工具，改进我们的 通过结合人体腹壁组织信息的声杂波的当前计算机数据模型 从医学CT扫描，并评估我们的杂波校正模型在体内数据的性能。 为了将我们的模型结果转化为医疗提供者的解释，图像后处理是 必要在我们最近发表的作品MimickNet中，我们使用深度学习方法成功地 在一些最好的临床级超声扫描仪上发现的近似后处理算法。我们 建议扩展MimickNet，以纳入针对特定解剖结构使用的后处理近似 例如心脏和血管成像。这将为研究人员提供更多现成的工具， 将他们的算法研究转化为提供者熟悉的图像形式，从而简化临床翻译。 最后，便携式超声硬件的成本显著降低，使得能够广泛应用于医疗领域。 使用移动的床旁超声（POCUS）。由于许多消费者设备包含硬件 针对深度学习应用程序的加速器，有机会纠正超声伪影， 实时的，即使是在受限于移动的硬件的情况下。我们的初步数据显示， 操作和MimickNet可以在NVIDIA P100 GPU上以每秒> 100帧的速度运行。我们提出 开发一个框架，将我们的图像处理管道完全转移到移动的硬件上 加速器这项工作将使翻译新的图像处理算法一样容易下载 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 从模拟数据到生成提供商熟悉的临床级图像的重建管道。这 框架将提供快速的平移路径，用于以便宜且 广泛使用的移动的硬件。

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.