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 肥胖在美国是一个严重且日益严重的问题。目前，68.5%的美国人口 超重，大约37.7%的超重人口肥胖。重大健康问题 与超重和肥胖有关，这一人群的“体型”与 对这些个体的医学成像的挑战降低了对该群体的保健的有效性。在 超声成像，腹部超声检查的质量显着影响肥胖。 基本上，超声图像依赖于到目标的声传播、反射，然后 传播回地面。波束形成的过程，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振幅反射敏感。典型地， 肥胖患者中额外脂肪和结缔组织层可显著降低超声成像 质量通过引入多径混响和相位畸变，模糊或扭曲这些低幅度 反思 然而，由于描述超声传播和反射的计算复杂性， 在非均匀介质中，波束形成器目前依赖于不描述传播的简化模型 物理直接我们提出了一个跨代的飞跃，我们如何接近超声波束成形，通过使用 物理和解剖学上真实的波传播模型和测量，可以有效地利用 数据驱动和快速发展的机器学习波束形成器的力量。一个自定义的高度逼真的模拟 我们开发的工具将使用人体精细结构的声学地图， 照相冷冻切片。这种基于物理的方法将使我们能够开发高质量的训练数据， 了解图像质量改善的物理机制。这些模拟将被校准， 离体和体内人类数据，以随后生成可用于训练机器的大数据集， 基于学习的实时波束形成器我们将重点关注两个来源的图像退化，我们有 被认为是特别有害的：多径混响和聚焦轮廓的畸变。的 提出的神经网络波束形成器过滤非相干噪声，如多径混响，并校正 射频通道信号中的畸变。 在训练波束形成器并实时实现它之后， 将进行，以确定高体重指数个体的图像质量改善，其中 诊断成像由于图像退化而成问题。这项技术是高度可移植到其他临床 场景，从心脏到经颅到产科成像，通过改变解剖模型。 此外，将从所学习的表示中提取的物理概念可以用于改进 超声设备的设计过程，包括发射序列和换能器。