Unsupervised deep learning for ultrasound beamforming and beyond

尽管超声波是第二大最受欢迎的医学成像方式，但其潜力要大得多。具体地，通道射频（RF）数据不适合于可视化，并且因此被波束成形为熟悉的灰度B模式（亮度模式）超声图像。然而，这种转换是有损的，并且破坏了RF数据中的大部分信息。人工智能和超声的最新进展提供了一个完美的组合，可以利用这些废弃信息的宝藏来进一步扩大超声的使用。在超声波束成形中利用深度学习（DL）的进步的关键挑战在于真实的实验中缺乏训练数据。更具体地说，真实的实验中的地面实况或最佳图像质量是未知的;因此，执行模拟以训练深度模型。然而，模拟总是简化假设，因此，不能完美地模拟真实世界的超声数据。这项合作有两个主要目标来解决这个问题：目标1。通过引入辅助任务对真实的超声数据进行无监督微调。除了传统的波束形成之外，还可以使用DL进行统一的去卷积和波束形成。该项目将与CREATIS实验室合作完成，CREATIS实验室是法国医学成像领域的主要实验室之一，拥有超过200名员工。合作的好处有三个方面。首先，加拿大和法国的团队提供了互补的专门知识。其次，法国和加拿大是超声波行业的几个领导者。因此，这种合作促进了从学术界到工业界的技术转让。第三，该项目在很大程度上依赖于CREATIS实验室提供的数据库;因此，合作者的专业知识在处理数据和分析结果方面至关重要。

Even though ultrasound is the second most popular medical imaging modality, its potential is far greater. Specifically, channel radio-frequency (RF) data is not suitable for visualization and, as such, is beamformed to the familiar gray-scale B-mode (brightness mode) ultrasound images. This conversion, however, is lossy and destroys most of the information in the RF data. Recent advances in AI and ultrasound provide a perfect combination for taking advantage of this trove of discarded information to further widen ultrasound use. A critical challenge in exploiting advances in deep learning (DL) in ultrasound beamforming lies in a lack of training data in real experiments. More specifically, the ground truth or optimal image quality in real experiments is unknown; therefore, simulations are performed to train deep models. However, simulations always make simplifying assumptions and, as such, do not perfectly model real-world ultrasound data. This collaboration has two main aims to address this issue:Aim 1. Unsupervised fine-tuning on real ultrasound data by introducing auxiliary tasks.Aim 2. Beyond conventional beamforming by unified deconvolution and beamforming with DL.This project will be done in collaboration with the CREATIS lab, which is one of the main French labs in medical imaging with a workforce of >200 members. The added benefits of the collaboration are three-fold. First, the Canadian and French teams provide complementary expertise. Second, France and Canada are home to several industry leaders in ultrasound. Therefore, this collaboration facilitates technology transfer from academia to industry. And third, this project relies heavily on a database provided by the CREATIS lab; hence, the collaborator's expertise is critical in processing the data and analyzing the results.