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 经颅超声可以在大脑成像中构建各种应用，例如功能成像， 脑内出血检测，脑灌注评估和中风诊断。超声具有固有的 实时，便携，广泛可用，无创和没有电离辐射的优点。那， 经颅超声成像可能在时间敏感和动态环境中起着独特的作用 X射线计算机断层扫描（CT）和磁共振成像（MRI）不可用。例如， thrananial超声在初步评估脑外伤的最初评估中具有重要潜力 将患者运输到医院，并在床边监测中风患者的脑生理监测 重症监护室。尽管具有承诺的潜力，但thrancranial超声成像的使用仍然有限， 主要是因为成年人的头骨会导致严重的相差，导致高度退化的超声 图像。如果声音速度（SOS）和配置文件的速度，可以准确纠正头骨的相差 （即厚度分布）的头骨已知。可以通过CT估算头骨轮廓和SOS 目前，用于治疗计划的黄金标准方法。基于CT的方法的吸引力远不那么吸引人 但是，出于超声成像的目的，由于其他CT扫描涉及电离辐射和 图像共同注册。我们提出了一种实时脉搏回波超声方法来估计头骨轮廓和 SOS使用深度学习（DL）方法具有超声射频（RF）信号从头骨进行反向散射。 拟议的方法基于科学前提，即这些RF信号包含非常丰富的信息 超声和头骨之间的相互作用以及头骨轮廓和SOS的信息已编码 反向散射的信号以一种令人费解的方式，无法通过简单的物理模型充分描述。我们 假设DL是机器学习的子类（ML），能够自动迅速提取头骨 带有足够培训的RF信号的个人资料和SO。此开拓者R21应用程序的目的是 开发和验证DL方法以提取人类头骨剖面和SOS，并具有以下目的。目标1。 在Silico研究中：使用合成数据开发和评估基于DL的颅骨轮廓和SOS提取算法。 AIM 2。实验研究：评估DL算法在Skull轮廓和SOS提取中的性能 实验数据。 AIM 3。试验成像研究：评估DL算法在经颅成像中的性能。 这项研究的成功完成将有助于传统的经颅应用（例如，B模式 成像，血流成像和对比增强的超声）和新兴的超声成像方法（例如， 超分辨率成像和光声断层扫描）。尽管目前的应用集中在大脑上 成像，我们的方法可以扩展到基于超声的大脑治疗的相位畸变校正， 神经调节以及存在相差的其他器官的超声成像。