标题： 基于深度任务的波束成形和信道数据增强用于超声成像的改进 显示英文标题

标题： Deep Task-Based Beamforming and Channel Data Augmentations for Enhanced Ultrasound Imaging

摘要： 本文介绍了一种基于深度学习的任务导向超声波束形成框架，旨在通过直接将特定的临床任务整合到波束形成过程中来提高临床结果。任务导向波束形成不仅优化图像质量，还针对特定的临床任务（例如病灶分类）进行性能优化。所提出的框架探索了两种方法：(1) 联合波束形成器和分类器 (JBC)，用于分类波束形成器生成的超声图像以提供图像质量改进反馈；(2) 通道数据分类波束形成器 (CDCB)，在波束形成器瓶颈层的通道数据表示中直接结合分类。此外，我们引入了通道数据增强技术以应对由噪声和有限体内数据带来的挑战。数值评估表明，使用通道数据增强训练可以显著改善图像质量。所提出的方法与传统的延迟求和 (DAS) 和最小方差 (MV) 波束形成技术进行了对比评估，显示出在图像对比度和临床相关性方面更优越的性能。在所有方法中，CDCB 方法取得了最佳结果，在图像质量和临床相关性方面优于其他方法。这些方法在提高超声成像的临床相关性和图像质量方面表现出巨大的潜力。 显示英文摘要

摘要： This paper introduces a deep learning (DL)-based framework for task-based ultrasound (US) beamforming, aiming to enhance clinical outcomes by integrating specific clinical tasks directly into the beamforming process. Task-based beamforming optimizes the beamformer not only for image quality but also for performance on a particular clinical task, such as lesion classification. The proposed framework explores two approaches: (1) a Joint Beamformer and Classifier (JBC) that classifies the US images generated by the beamformer to provide feedback for image quality improvement; and (2) a Channel Data Classifier Beamformer (CDCB) that incorporates classification directly at the channel data representation within the beamformer's bottleneck layer. Additionally, we introduce channel data augmentations to address challenges posed by noisy and limited in-vivo data. Numerical evaluations demonstrate that training with channel data augmentations significantly improves image quality. The proposed methods were evaluated against conventional Delay-and-Sum (DAS) and Minimum Variance (MV) beamforming techniques, demonstrating superior performance in terms of both image contrast and clinical relevance. Among all methods, the CDCB approach achieves the best results, outperforming others in terms of image quality and clinical relevance. These approaches exhibit significant potential for improving clinical relevance and image quality in ultrasound imaging.