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

项目摘要 经颅超声可以促进脑成像中的各种应用，例如，功能成像， 脑内出血检测、脑灌注评估和中风诊断。超声具有内在的 具有实时、便携、广泛可用、无创和无电离辐射的优点。因此，在本发明中， 经颅超声成像可能在时间敏感和动态环境中发挥独特的作用 其中X射线计算机断层扫描（CT）和磁共振成像（MRI）不可用。比如说， 经颅超声在创伤性脑损伤的初步评估中具有重要的潜力， 病人到医院的运输，以及在脑卒中患者的床边监测脑生理学， 加护病房尽管经颅超声成像具有很大的潜力，但其应用一直受到限制， 这主要是因为成年人的头骨会引起严重的相位畸变，导致超声波的高度退化 图像.如果声速（SOS）和轮廓能够被精确地校正来自颅骨的相位畸变， (i.e.,厚度分布）是先验已知的。颅骨轮廓和SOS可以通过CT估计， 目前是治疗计划的黄金标准方法。基于CT的方法远没有那么吸引人， 然而，对于超声成像目的，因为额外的CT扫描涉及电离辐射， 图像配准。我们提出了一种实时脉冲回波超声方法来估计颅骨轮廓， SOS使用深度学习（DL）方法，从颅骨反向散射超声射频（RF）信号。 所提出的方法基于这样一个科学前提，即这些射频信号包含极其丰富的信息 超声与颅骨的相互作用，颅骨轮廓和SOS信息被编码在 反向散射信号以复杂的方式，不能完全由简单的物理模型描述。我们 假设机器学习（ML）子类DL能够自动快速地提取颅骨 通过充分的培训，从射频信号中获取简档和SOS。本Trailblazer R21应用程序的目标是 开发和验证用于提取人类颅骨轮廓和SOS的DL方法，目标如下。目标1. 计算机模拟研究：使用合成数据开发和评价基于DL的颅骨轮廓和SOS提取算法。 目标二。实验研究：使用 实验数据目标3：初步成像研究：评价DL算法在经颅成像中的性能。 本研究的成功完成将促进常规（例如，b模式 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层析成像）。虽然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治疗的相位畸变校正， 神经调节和存在相位畸变的其它器官的超声成像。

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.