Unsupervised deep learning for ultrasound beamforming and beyond

用于超声波束形成及其他领域的无监督深度学习

• 批准号: 578544-2022
• 负责人: Rivaz, HassanH
• 金额: $ 1.82万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=751945
• 关键词: Unsupervised; deep; learning; ultrasound; beamforming

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.

尽管超声波是第二流行的医学成像方式，但它的潜力要大得多。具体来说，通道射频（RF）数据不适合用于可视化，因此，它被波束形成为熟悉的灰度b模式（亮度模式）超声图像。然而，这种转换是有损的，并且会破坏射频数据中的大部分信息。人工智能和超声的最新进展为利用这一废弃信息宝库进一步扩大超声应用提供了完美的结合。在超声波束形成中利用深度学习技术的一个关键挑战在于缺乏真实实验中的训练数据。更具体地说，真实实验中的真实或最佳图像质量是未知的；因此，通过仿真来训练深度模型。然而，模拟总是做出简化的假设，因此，不能完美地模拟真实的超声数据。这项合作有两个主要目的来解决这个问题：目标1。通过引入辅助任务对真实超声数据进行无监督微调。目标2。超越传统的统一反褶积波束形成和DL波束形成。该项目将与CREATIS实验室合作完成，该实验室是法国医学成像领域的主要实验室之一，拥有150名员工。这种合作的额外好处是三重的。首先，加拿大和法国小组提供互补的专门知识。其次，法国和加拿大是超声波行业的几个领导者。因此，这种合作促进了技术从学术界向工业界的转移。第三，这个项目很大程度上依赖于CREATIS实验室提供的数据库；因此，合作者的专业知识对于处理数据和分析结果至关重要。