Toward ultrasound brain imaging via machine-learning-extracted skull profile and speed of sound

通过机器学习提取的头骨轮廓和声速进行超声脑成像

• 批准号: 10354529
• 负责人: Aiguo Han
• 金额: $ 23.41万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10354529
• 关键词: 3-Dimensional; Acoustics; Address; Adult; Algorithms; Architecture; Base of the Brain; Blood flow; Brain; Brain imaging; Cerebral hemisphere hemorrhage; Cerebrovascular Disorders; Data; Detection; Diagnosis; Environment; Evaluation; Functional Imaging; Future; Goals; Gold; Hospitals; Human; Image; Image Enhancement; Imaging Techniques; Intensive Care Units; Ionizing radiation; Knowledge; Location; Machine Learning; Magnetic Resonance Imaging; Maps; Measures; Methods; Modality; Modeling; Monitor; Organ; Performance; Perfusion; Phase; Physiologic pulse; Physiology; Play; Property; Resolution; Rest; Role; Scanning; Signal Transduction; Spatial Distribution; Speed; Stroke; Techniques; Testing; Thick; Time; Training; Transportation of Patients; Traumatic Brain Injury; Ultrasonography; X-Ray Computed Tomography; base; contrast enhanced; cranium; deep learning; deep learning algorithm; design; disease diagnosis; experimental study; imaging modality; imaging probe; imaging study; in silico; in vivo; learning strategy; machine learning method; microCT; nervous system disorder; neuroregulation; optoacoustic tomography; physical model; portability; radio frequency; skull base; sound; standard care; stroke patient; transmission process; trauma care; treatment planning; ultrasound

Project Summary Transcranial ultrasound could facilitate a broad variety of applications in brain imaging, e.g., functional imaging, intracerebral hemorrhage detection, brain perfusion evaluation, and stroke diagnosis. Ultrasound has the intrinsic advantages of being real-time, portable, widely available, noninvasive, and free from ionizing radiation. Thus, transcranial ultrasound imaging could potentially play a unique role in a time-sensitive and dynamic environment where X-ray computed tomography (CT) and magnetic resonance imaging (MRI) are unavailable. For instance, transcranial ultrasound has significant potentials in the initial assessment of traumatic brain injury during the transportation of patients to the hospital, and in bedside monitoring of brain physiology for stroke patients in an intensive care unit. Despite its promising potentials, the use of transcranial ultrasound imaging has been limited, largely because adult human skulls cause severe phase aberration, leading to highly degraded ultrasound images. Phase aberration from the skull can be accurately corrected if the speed of sound (SOS) and profile (i.e., thickness distribution) of the skull are known a priori. The skull profile and SOS can be estimated by CT, currently the gold standard approach for treatment planning. The CT-based approach is far less appealing, however, for ultrasound imaging purposes because of the additional CT scans that involve ionizing radiation and image co-registration. We propose a real-time pulse-echo ultrasound approach to estimate the skull profile and SOS using deep learning (DL) methods with ultrasound radiofrequency (RF) signals backscattered from the skull. The proposed approach rests on the scientific premise that these RF signals contain extremely rich information of the interaction between ultrasound and skulls, and the information of skull profile and SOS is encoded in the backscattered signals in a convoluted way that cannot be fully described by simple physical models. We hypothesize that DL, a subclass of machine learning (ML), is capable of automatically and rapidly extracting skull profile and SOS from RF signals with sufficient training. The objective of this Trailblazer R21 application is to develop and validate DL methods for extracting the human skull profile and SOS, with the following aims. Aim 1. In silico study: Develop and evaluate DL-based skull profile and SOS extraction algorithms using synthetic data. Aim 2. Experimental study: Evaluate DL algorithms’ performance in skull profile and SOS extraction using experimental data. Aim 3. Pilot imaging study: Evaluate DL algorithms’ performance in transcranial imaging. Successful completion of this study will facilitate the transcranial application of both conventional (e.g., B-mode imaging, blood flow imaging, and contrast-enhanced ultrasound) and emerging ultrasound imaging methods (e.g, super-resolution imaging and photoacoustic tomography). Although the current application focuses on brain imaging, our method can be extended to phase aberration correction for ultrasound-based brain treatment, neuromodulation, and ultrasound imaging of other organs where phase aberration exists.

项目摘要 经颅超声可以促进脑成像中的广泛应用，例如，功能成像， 脑内出血检测、脑血流灌注评估和中风诊断。超声波具有内在的 具有实时、便携、广泛使用、无创、无电离辐射等优点。因此， 在时间敏感和动态的环境中，经颅超声成像可能会发挥独特的作用 没有X射线计算机断层扫描(CT)和磁共振成像(MRI)的地方。例如, 经颅超声在颅脑损伤早期评估中具有重要的潜力。 卒中患者送往医院和床边脑生理监测的研究 重症监护室。尽管有很大的潜力，但经颅超声成像的使用一直受到限制， 这在很大程度上是因为成人头骨会导致严重的相位差，从而导致超声波的高度退化 图像。如果声速(SOS)和轮廓可以精确地校正来自颅骨的相位像差 (即，头骨的厚度分布)是先验已知的。头骨侧貌和SOS可以通过CT来估计， 目前治疗计划的黄金标准方法。基于CT的方法远没有那么有吸引力， 然而，出于超声成像的目的，因为额外的CT扫描涉及电离辐射和 图像联合配准。我们提出了一种实时脉冲回波超声方法来估计颅骨轮廓和 使用深度学习(DL)方法和从颅骨反向散射的超声射频(RF)信号进行SOS。 建议的方法基于这样的科学前提，即这些射频信号包含极其丰富的信息 超声与头骨的相互作用，颅骨轮廓和SOS信息被编码在 复杂的反向散射信号，不能用简单的物理模型完全描述。我们 假设机器学习(ML)的子类DL能够自动、快速地提取颅骨 经过充分训练，从射频信号中提取轮廓和求救信号。此开拓者R21应用程序的目标是 开发和验证用于提取人类头骨轮廓和SOS的DL方法，目标如下。目标1。 在硅胶研究中：利用合成数据开发和评估基于DL的头骨侧写和SOS提取算法。 目的2.实验研究：评估DL算法在颅骨轮廓和SOS提取中的性能 实验数据。目的3.先导成像研究：评估DL算法在经颅成像中的性能。 这项研究的成功完成将促进经颅应用两种常规(例如B型) 成像、血流成像和对比增强超声)和新兴的超声成像方法(例如， 超分辨率成像和光声断层成像)。尽管目前的应用主要集中在大脑 成像，我们的方法可以扩展到基于超声的脑部治疗的相位像差校正， 神经调节，以及存在相位偏差的其他器官的超声成像。