A machine learning ultrasound beamformer based on realistic wave physics for high body mass index imaging

基于真实波物理学的机器学习超声波束形成器，用于高体重指数成像

• 批准号: 10595030
• 负责人: Gianmarco Pinton
• 金额: $ 44.86万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10595030
• 关键词: 3-Dimensional; Abdomen; Acoustics; Address; Affect; Anatomic Models; Anatomy; Back; Calibration; Cardiac; Carotid Arteries; Cephalic; Characteristics; Clinical; Complex; Connective Tissue; Cryoultramicrotomy; Custom; Data; Data Set; Databases; Dependence; Diagnosis; Diagnostic Imaging; Discipline of obstetrics; Effectiveness; Equipment; Fatty acid glycerol esters; Health; Healthcare; Human; Human body; Image; Individual; Learning; Left; Link; Liver; Machine Learning; Maps; Measurement; Medical Imaging; Methodology; Methods; Modeling; Modernization; Noise; Obesity; Overweight; Patients; Phase; Photography; Physics; Population; Process; Property; Public Health; Resolution; Signal Transduction; Source; Structure; Surface; Techniques; Technology; Texture; Time; Tissues; Training; Transducers; Translating; Ultrasonic wave; Ultrasonography; United States; Weight; deep learning; design; health care quality; high body mass index; human data; human study; image reconstruction; imaging modality; imaging system; improved; in vivo; in vivo imaging; large datasets; learning strategy; liver imaging; neural; neural network; obese patients; prototype; radio frequency; simulation; soft tissue; sound; tissue phantom; tool; transmission process; ultrasound

PROJECT SUMMARY Obesity is a significant and growing problem in the United States. Currently, 68.5% of the U.S. population is overweight, with approximately 37.7% of the overweight population being obese. The significant health problems associated with overweightedness and obesity, the “body habitus” of this population combined with the significant challenges in medical imaging of these individuals reduces the effectiveness of healthcare for this population. In ultrasound imaging, the quality of abdominal ultrasound exams are significantly affected by obesity. Fundamentally, an ultrasound image relies on acoustic propagation to a target, reflection, and then propagation back to the surface. The process of beamforming, which converts the surface measurement to an image, is sensitive to the low amplitude reflections from different tissue layers and tissue properties. Typically, the additional fat and connective tissue layers in obese patients can significantly degrade ultrasound image quality by introducing multi-path reverberation and phase aberration that obscure or distort these low amplitude reflections. However, due to the computational complexity of describing ultrasound propagation and reflection in heterogeneous media, beamformers currently rely on simplified models that do not describe the propagation physics directly. We propose a generational leap in how we approach ultrasound beamforming by using physically and anatomically realistic wave propagation models and measurements that can effectively harness the power of data-driven and rapidly evolving machine learning beamformers. A custom highly realistic simulation tool that we have developed will use acoustical maps of the fine structures in the human body based on photographic cryosections. This physics-based approach will allow us to develop high quality training data and to understand the physical mechanisms for image quality improvement. These simulations will be calibrated to ex vivo and in vivo human data to subsequently generate a large data set that can be used to train a machine- learning-based real-time beamformer. We will focus on two sources of image degradation which we have identified to be particularly deleterious: multipath reverberation and aberration of the focusing profile. The proposed neural network beamformer filters incoherent noise, such as multi-path reverberation, and corrects aberration in the radiofrequency channel signals. After training the beamformer and implementing it in real-time, a pilot human study in liver ultrasound imaging will be conducted to determine the improvement in image quality in high-body-mass index individuals, where diagnostic imaging is problematic due to image degradation. This technique is highly translatable to other clinical scenarios, varying from cardiac to transcranial to obstetric imaging, by changing the anatomical model. Furthermore, the physical concepts that will be extracted from the learned representation, can be used to improve the design process for ultrasound equipment, including transmit sequences, and transducers.

项目摘要 在美国，肥胖是一个重大且日益严重的问题。目前，美国68.5％的人口是 超重，大约有37.7％的超重人口肥胖。重大健康问题 与超重和肥胖有关，该人群的“身体习惯”与重要的 这些人的医学成像挑战降低了医疗保健对该人群的有效性。在 超声成像，腹部超声检查的质量受肥胖症的显着影响。 从根本上讲，超声图像依靠声学传播到目标，反射，然后 传播回到表面。波束形成过程，将表面测量转换为 图像对来自不同组织层和组织特性的低放大器反射敏感。通常， 肥胖患者的额外脂肪和连接的组织层可显着降低超声图像 质量通过引入多路重复和相差，使这些低放大器造 反思。 但是，由于描述超声传播和反射的计算复杂性 异构介质，光束形式目前依赖于不描述传播的简化模型 物理直接。我们提出了如何通过使用超声梁的世代相传的飞跃 可以有效利用的物理和解剖上现实的波传播模型和测量 数据驱动和迅速发展的机器学习波束形式的功能。自定义的高度现实模拟 我们开发的工具将使用基于人体中精细结构的声学图。 摄影冷冻切片。这种基于物理的方法将使我们能够制定高质量的培训数据，并 了解图像质量改进的物理机制。这些模拟将被校准 离体和体内人类数据随后生成一个可用于训练机器的大数据集 - 基于学习的实时波束形式。我们将重点介绍我们拥有的两个图像降解来源 被确定为特别删除：多径再生和聚焦曲线的像差。 提出的神经网络波束形成器过滤不连贯的噪声，例如多路恢复并纠正 射频信号中的像差。 训练波束形式并实时实施后，肝超声成像中的一项试验人类研究 将进行以确定高身质量指数个体的图像质量的改善， 由于图像降解，诊断成像是有问题的。该技术可以高度翻译成其他临床 场景通过改变解剖模型，从心脏到经颅形成到产科成像不等。 此外，可以从学习的表示形式中提取的物理概念可用于改进 超声设备的设计过程，包括发射序列和传感器。