高质量超声成像与重建研究综述  

Review of high-quality ultrasound imaging and reconstruction

作　　者：李云舒 马宸 黄丽红 高雪 闫鑫 汪源源[1] 郭翌[1] Li Yunshu;Ma Chen;Huang Lihong;Gao Xue;Yan Xin;Wang Yuanyuan;Guo Yi(Center for Biomedical Engineering,School of Information Science and Technology,Fudan University,Shanghai 200433,China;School of Information Science and Engineering,Shenyang University of Technology,Shenyang 110870,China)

机构地区：[1]复旦大学信息科学与工程学院生物医学工程中心,上海200433 [2]沈阳工业大学信息科学与工程学院,沈阳110870

出　　处：《中国图象图形学报》2024年第6期1628-1645,共18页Journal of Image and Graphics

基　　金：国家自然科学基金项目(62371139);上海市自然科学基金项目(22ZR1404800)。

摘　　要：医学超声作为一种无创、无辐射和实时医学成像模态,在重大疾病早期诊断和精准诊疗领域发挥重要作用。影像分辨率是超声仪器的核心指标,也是影响临床精准诊疗的关键。近年来,超声成像设备呈现多样化的发展趋势,以满足不同的临床应用场景,如超快速成像设备、便携成像设备等。然而,这些超声设备通常以牺牲成像质量来实现特定应用场景的要求,影响了其临床可用性。因此,为提升医学超声设备的诊断能力,研究如何获得高质量超声图像至关重要。本文回顾了近年来高质量超声图像成像的相关工作,从波束形成算法和高质量超声重建算法两方面进行介绍,波束形成算法方面,介绍了以延时叠加方法为代表的传统的非自适应方法,以及4类成像效果更优越但计算复杂度更高的自适应的波束形成方法,并对波束形成的深度学习类方法进行了简要介绍。对于高质量超声重建算法的讨论,则是从传统方法和深度学习方法两方面展开,并重点介绍了在高质量超声重建算法方面具有更广阔应用前景的深度学习技术,包括卷积神经网络方法、生成对抗网络方法等。最后,本文从研究方法的侧重点等方面比较国内外研究进展,并讨论了未来发展趋势。Medical ultrasound,as a noninvasive,radiation-free,real-time medical imaging modality,plays a crucial rolein the early and clinical diagnoses and treatment.Image resolution stands as a core indicator of ultrasound instruments,which significantly influences precise diagnosis.In recent years,ultrasound imaging devices have undergone a diversifieddevelopment to meet various clinical application scenarios,including ultra-fast and hand-held imaging devices.However,most advancement comes at the expense of reducing imaging quality to achieve high imaging frame rate or portable hard⁃ware system,which impacts their clinical applicability.Thus,obtaining high-quality ultrasound images is a pivotal issue.This study reviews extensive recent work on the high-quality ultrasound imaging by delving into beamforming algorithmsand high-quality ultrasound reconstruction methods.In the aspect of beamforming algorithms,we introduce traditional nonadaptive methods represented by Delay and Sum techniques,as well as four types of adaptive beamforming methods withsuperior imaging quality but higher computational complexity.In addition,a brief introduction to learning-based models forbeamforming is provided.Adaptive beamforming algorithms are currently a major research topic with the advantages of high imaging quality and the substantial development prospects.The study focuses on four main kinds of adaptive algorithms:minimum variance(MV)methods,coherence factor(CF)methods,short-lag spatial coherence(SLSC)methods,and fil⁃tered delay multiply and sum(F-DMAS)methods.Detailed analyses of modified algorithms based on the classic adaptivealgorithms and corresponding applications are presented.For each type of adaptive algorithm,a brief theoretical introduc⁃tion is provided.Subsequently,the study lists the most influential related literature in recent years,along with a short sum⁃mary to the methodology and final results.The primary challenge for MV-based methods is improving the accuracy of covari⁃ance matrix estimation and reducing c

关 键 词：超声成像 波束形成 自适应成像 图像重建 高分辨率 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]