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李云舒1, 马宸1, 黄丽红1, 高雪1, 闫鑫2, 汪源源1, 郭翌1(1.复旦大学信息科学与工程学院生物医学工程中心;2.沈阳工业大学信息科学与工程学院)

摘 要
A review of high quality ultrasound imaging and reconstruction

Li Yunshu, Ma Chen1, Huang Lihong1, Gao Xue1, Yan Xin2, Wang Yuanyuan1, Guo Yi1(1.Center for Biomedical Engineering,School of Information Science and Technology,Fudan University;2.School of Information Science and Engineering,Shenyang University of Technology)

Medical ultrasound, as a non-invasive, radiation-free, real-time medical imaging modality, holds a crucial role in the early and clinical diagnoses and treatment. Image resolution stands as a core indicator of ultrasound instruments, significantly influencing precise diagnosis. In recent years, ultrasound imaging devices have undergone a diversified development to meet various clinical application scenarios, including ultra-fast and hand-held imaging devices. However, most of advancements come at the expense of reducing imaging quality to achieve high imaging frame rate or portable hardware system, thereby impacting their clinical applicability. Hence, it is a pivotal issue to study: how to obtain high-quality ultrasound images. This paper reviews extensive recent work on the high-quality ultrasound imaging, delving into beamforming algorithms and high-quality ultrasound reconstruction methods. In the aspect of beamforming algorithms, we introduce traditional non-adaptive methods represented by Delay and Sum (DAS) techniques, and four types of adaptive beamforming methods with superior imaging quality but higher computational complexity. Additionally, a brief introduction to learning-based models for beamforming is also provided. As the advantages of high imaging quality and the substantial development prospects, adaptive beamforming algorithms are currently a hot research topic. The paper focuses on four main kinds of adaptive algorithms: Minimum Variance (MV) methods, Coherence Factor (CF) methods, Short-Lag Spatial Coherence (SLSC) methods, and Filtered Delay Multiply and Sum (F-DMAS) methods. Detailed analyses of modified algorithms based on the classic adaptive algorithms and corresponding applications are presented. For each type of adaptive algorithm, a brief theoretical introduction is provided. Subsequently, the paper lists the most influential related literatures in recent years, along with a short summary to their methodology and the final results. The primary challenge for MV-based methods is improving the accuracy of covariance matrix estimation and reducing computational complexity. To address this, the paper introduces several approaches, such as reducing beamforming dimensionality, designing covariance matrix based on Toeplitz structure, and learning adaptive weights using neural networks. For CF-based methods, improved coherence factor methods and other related methods are introduced. Compared with the traditional CF-based methods, the former can greatly improve the lateral resolution and signal-to-noise ratio of images, while the latter can suppress the dark region artifacts and alleviate the excessive suppression of coherence factor. For SLSC-based methods, techniques like adaptive synthesis of dual pore diameter, robust principal component analysis, and linear attenuation weighting are explored to address the issue of poor resolution. For F-DMAS-based methods, approaches to further enhance imaging quality and decrease computational cost are discussed. For instance, by combining multi-line acquisition (MLA) with the lower-complexity F-DMAS algorithm, it is possible to increase the frame rates while maintaining the high quality of images. And F-DMAS can also be combined with a pixel-based (PB) beamformer to improve the contrast of the generated images and suppress the clutter. Finally, the paper provides an analysis of the advantages and disadvantages of each method in terms of resolution, contrast, noise suppression, and robustness. As for high-quality ultrasound reconstruction algorithms, the discussion primarily focuses on two aspects: conventional methods and deep learning-based methods. Conventional methods, including interpolation, sparse representation-based methods, and example-based methods, aim to enhance the spatiotemporal resolution and reduce noise of images. In contrast, deep learning methods, capable of fully utilizing prior knowledge to automatically learn gray distribution mapping between images from different domains (centers), present broader application prospects in high-quality ultrasound reconstruction algorithms. For convolutional neural network (CNN)-based methods, the paper enumerates several approaches, such as learning the nonlinear mapping between low-quality image subspaces reconstructed from a single plane wave (PW) and high-quality image subspaces reconstructed from synthetic aperture (SA) measurements through CNN. This can accurately preserve complete speckle patterns while improving lateral resolution. The image reconstruction method based on a two-stage CNN can produce high-quality images from ultra-fast ultrasound imaging while ensuring high frame rates. Regarding Generative Adversarial Network (GAN)-based methods, the paper introduces several improved algorithms which achieve higher quality acquisition of images, stronger robustness of algorithms, higher image frame coherence to better satisfy the specific demand for clinical applications. Finally, the paper conducts an overall comparative analysis of research progress at home and abroad and discusses future development trends. Concerning beamforming algorithms, both domestic and foreign scholars focus on adaptive beamforming methods. Besides, the future development and research trends of beamforming algorithms can be primarily summarized as follows: 1) Studying how to reduce the computational complexity of adaptive beamforming methods to improve their real-time performance. 2) Deepening research on learning-based beamforming algorithms. 3) Investigating how to synchronously increase the imaging frame rate and image quality in ultrafast ultrasound imaging. 4) Integrating different beamforming methods to fully leverage the advantages of various approaches. In terms of high-quality ultrasound image reconstruction, researches predominantly focus on deep learning technology. There are relatively few studies on using traditional methods for super-resolution reconstruction. The research on deep learning methods has shifted from convolutional neural networks to generative adversarial networks or the fusion of them. Finally, future prospects for high-quality ultrasound image reconstruction are proposed: 1) Combining traditional methods with deep learning techniques. 2) Introducing diffusion models and foundation models into the field of high-resolution ultrasound image reconstruction to further enhance the quality of generated images. The synergy of traditional and deep learning-based methods, coupled with the introduction of innovative and advanced technology, holds great promise for propelling high-resolution ultrasound image reconstruction into new frontiers and contributes significantly to the advancement of healthcare services.