浏览全部资源
扫码关注微信
纸质出版日期： 2024-06-16 ，
移动端阅览
李云舒， 马宸， 黄丽红， 高雪， 闫鑫， 汪源源， 郭翌. 2024. 高质量超声成像与重建研究综述. 中国图象图形学报， 29(06):1628-1645
Li Yunshu， Ma Chen， Huang Lihong， Gao Xue， Yan Xin， Wang Yuanyuan， Guo Yi. 2024. Review of high-quality ultrasound imaging and reconstruction. Journal of Image and Graphics， 29(06):1628-1645
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 role in 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 diversified development 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 hardware 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 algorithms and high-quality ultrasound reconstruction methods. In the aspect of beamforming algorithms， we introduce traditional non-adaptive methods represented by Delay and Sum techniques， as well as four types of adaptive beamforming methods with superior imaging quality but higher computational complexity. In addition， a brief introduction to learning-based models for beamforming 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 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 study lists the most influential related literature in recent years， along with a short summary to the methodology and final results. The primary challenge for MV-based methods is improving the accuracy of covariance matrix estimation and reducing computational complexity. To address this problem， the study 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， combining multi-line acquisition with the lower-complexity F-DMAS algorithm increases the frame rates while maintaining the high quality of images. F-DMAS can also be combined with a pixel-based beamformer to improve the contrast of the generated images and suppress the clutter. Finally， the study provides an analysis of the advantages and disadvantages of each method in terms of resolution， contrast， noise suppression， and robustness. For high-quality ultrasound reconstruction algorithms， the discussion primarily focuses on two aspects： conventional 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. By contrast， deep learning methods， which are 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 study enumerates several approaches， such as learning the nonlinear mapping between low-quality image subspaces reconstructed from a single plane wave and high-quality image subspaces reconstructed from synthetic aperture measurements through CNN. This approach 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 study introduces several improved algorithms that achieve higher-quality acquisition of images， stronger robustness of algorithms， and higher image frame coherence to better satisfy the specific demand for clinical applications. Finally， the study conducts an overall comparative analysis of research progress at home and abroad and discusses future development trends. Concerning beamforming algorithms， domestic and foreign scholars focus on adaptive beamforming methods. Moreover， the future development and research trends of beamforming algorithms can be primarily summarized as follows： 1） reducing the computational complexity of adaptive beamforming methods to improve their real-time performance； 2） deepening research on learning-based beamforming algorithms； 3） synchronously increasing the imaging frame rate and image quality in ultrafast ultrasound imaging； and 4） integrating different beamforming methods to fully leverage the advantages of various approaches. In terms of high-quality ultrasound image reconstruction， studies predominantly focus on deep learning technology. Relatively few studies are available on using traditional methods for super-resolution reconstruction. The research on deep learning methods has shifted from CNNs to GANs or their fusion. Finally， future prospects for high-quality ultrasound image reconstruction are proposed： 1） combining traditional methods with deep learning techniques， and 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.
超声成像波束形成自适应成像图像重建高分辨率
ultrasonic imagingbeamformingadaptive imagingimage reconstructionsuper-resolution
Asl B M and Mahloojifar A. 2012. A low-complexity adaptive beamformer for ultrasound imaging using structured covariance matrix. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 59（4）： 660-667 ［DOI： 10.1109/TUFFC.2012.2244http://dx.doi.org/10.1109/TUFFC.2012.2244］
Camacho J， Parrilla M and Fritsch C. 2009. Phase coherence imaging. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 56（5）： 958-974 ［DOI： 10.1109/TUFFC.2009.1128http://dx.doi.org/10.1109/TUFFC.2009.1128］
Capon J. 1969. High-resolution frequency-wavenumber spectrum analysis. Proceedings of the IEEE， 57（8）： 1408-1418 ［DOI： 10.1109/PROC.1969.7278http://dx.doi.org/10.1109/PROC.1969.7278］
Chen Y R， Liu J， Luo X B and Luo J W. 2021. ApodNet： learning for high frame rate synthetic transmit aperture ultrasound imaging. IEEE Transactions on Medical Imaging， 40（11）： 3190-3204 ［DOI： 10.1109/TMI.2021.3084821http://dx.doi.org/10.1109/TMI.2021.3084821］
Deylami A M and Asl B M. 2017. A fast and robust beamspace adaptive beamformer for medical ultrasound imaging. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 64（6）： 947-958 ［DOI： 10.1109/TUFFC.2017.2685525http://dx.doi.org/10.1109/TUFFC.2017.2685525］
Esmailian K and Asl B M. 2022. Correlation-based modified delay-multiply-and-sum beamforming applied to medical ultrasound imaging. Computer Methods and Programs in Biomedicine， 226： #107171 ［DOI： 10.1016/j.cmpb.2022.107171http://dx.doi.org/10.1016/j.cmpb.2022.107171］
Fuhrmann D R. 1991. Application of Toeplitz covariance estimation to adaptive beamforming and detection. IEEE Transactions on Signal Processing， 39（10）： 2194-2198 ［DOI： 10.1109/78.91176http://dx.doi.org/10.1109/78.91176］
Gifani P， Behnam H， Haddadi F， Sani Z A and Shojaeifard M. 2016. Temporal super resolution enhancement of echocardiographic images based on sparse representation. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 63（1）： 6-19 ［DOI： 10.1109/TUFFC.2015.2493881http://dx.doi.org/10.1109/TUFFC.2015.2493881］
Glasner D， Bagon S and Irani M. 2009. Super-resolution from a single image//Proceedings of the 12th IEEE International Conference on Computer Vision. Kyoto， Japan： IEEE： 349-356 ［DOI： 10.1109/ICCV.2009.5459271http://dx.doi.org/10.1109/ICCV.2009.5459271］
Goodfellow I J， Pouget-Abadie J， Mirza M， Xu B， Warde-Farley D， Ozair S， Courville A and Bengio Y. 2014. Generative adversarial nets//Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal， Canada： MIT Press： 2672-2680
Guo H， Xie H W， Zhou G Q， Nguyen N Q and Prager R W. 2023. Pixel-based approach to delay multiply and sum beamforming in combination with Wiener filter for improving ultrasound image quality. Ultrasonics， 128： #106864 ［DOI： 10.1016/j.ultras.2022.106864http://dx.doi.org/10.1016/j.ultras.2022.106864］
Ho J， Jain A and Abbeel P. 2020. Denoising diffusion probabilistic models//Proceedings of the 34th International Conference on Neural Information Processing Systems. Vancouver， Canada： Curran Associates Inc.： #574
Holfort I K， Gran F and Jensen J A. 2008. Plane wave medical ultrasound imaging using adaptive beamforming//Proceedings of the 5th IEEE Sensor Array and Multichannel Signal Processing Workshop. Darmstadt， Germany： IEEE： 288-292 ［DOI： 10.1109/SAM.2008.4606874http://dx.doi.org/10.1109/SAM.2008.4606874］
Hollman K W， Rigby K W and O’Donnell M. 1999. Coherence factor of speckle from a multi-row probe//1999 IEEE Ultrasonics Symposium. Tahoe， USA： IEEE： 1257-1260 ［DOI： 10.1109/ULTSYM.1999.849225http://dx.doi.org/10.1109/ULTSYM.1999.849225］
Hung K W and Siu W C. 2012. Robust soft-decision interpolation using weighted least squares. IEEE Transactions on Image Processing， 21（3）： 1061-1069 ［DOI： 10.1109/TIP.2011.2168416http://dx.doi.org/10.1109/TIP.2011.2168416］
Jensen K and Anastassiou D. 1995. Subpixel edge localization and the interpolation of still images. IEEE Transactions on Image Processing， 4（3）： 285-295 ［DOI： 10.1109/83.366477http://dx.doi.org/10.1109/83.366477］
Jin K H， McCann M T， Froustey E and Unser M. 2017. Deep convolutional neural network for inverse problems in imaging. IEEE Transactions on Image Processing， 26（9）： 4509-4522 ［DOI： 10.1109/TIP.2017.2713099http://dx.doi.org/10.1109/TIP.2017.2713099］
Khor H G， Ning G C， Zhang X R and Liao H. 2022. Ultrasound speckle reduction using wavelet-based generative adversarial network. IEEE Journal of Biomedical and Health Informatics， 26（7）： 3080-3091 ［DOI： 10.1109/JBHI.2022.3144628http://dx.doi.org/10.1109/JBHI.2022.3144628］
Lan Z F， Jin L， Feng S， Zheng C C， Han Z H and Peng H. 2021. Joint generalized coherence factor and minimum variance beamformer for synthetic aperture ultrasound imaging. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 68（4）： 1167-1183 ［DOI： 10.1109/TUFFC.2020.3035412http://dx.doi.org/10.1109/TUFFC.2020.3035412］
LeCun Y， Bottou L， Bengio Y and Haffner P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE， 86（11）： 2278-2324 ［DOI： 10.1109/5.726791http://dx.doi.org/10.1109/5.726791］
Lediju M A， Trahey G E， Byram B C and Dahl J J. 2011. Short-lag spatial coherence of backscattered echoes： imaging characteristics. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 58（7）： 1377-1388 ［DOI： 10.1109/TUFFC.2011.1957http://dx.doi.org/10.1109/TUFFC.2011.1957］
Lei Z Y， Gao S C， Hasegawa H， Zhang Z M， Zhou M C and Sedraoui K. 2023. Fully complex-valued gated recurrent neural network for ultrasound imaging. IEEE Transactions on Neural Networks and Learning Systems， 99： 1-14 ［DOI： 10.1109/TNNLS.2023.3282231http://dx.doi.org/10.1109/TNNLS.2023.3282231］
Li C W， Li X D and Zhang L C. 2005. Development and innovation of medical ultrasonic image technology. China Medical Equipment， 2（2）： 45-47
李朝伟， 李晓东， 张良才. 2005. 医学超声影像技术的发展创新. 中国医学装备， 2（2）： 45-47 ［DOI： 10.3969/J.ISSN.1672-8270.2005.02.019http://dx.doi.org/10.3969/J.ISSN.1672-8270.2005.02.019］
Li P C and Li M L. 2003. Adaptive imaging using the generalized coherence factor. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 50（2）： 128-141 ［DOI： 10.1109/TUFFC.2003.1182117http://dx.doi.org/10.1109/TUFFC.2003.1182117］
Li X and Orchard M T. 2001. New edge-directed interpolation. IEEE Transactions on Image Processing， 10（10）： 1521-1527 ［DOI： 10.1109/83.951537http://dx.doi.org/10.1109/83.951537］
Lu J F， Millioz F， Garcia D， Salles S， Liu W Y and Friboulet D. 2020. Reconstruction for diverging-wave imaging using deep convolutional neural networks. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 67（12）： 2481-2492 ［DOI： 10.1109/TUFFC.2020.2986166http://dx.doi.org/10.1109/TUFFC.2020.2986166］
Lu J Y and Greenleaf J F. 1990. Ultrasonic nondiffracting transducer for medical imaging. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 37（5）： 438-447 ［DOI： 10.1109/58.105250http://dx.doi.org/10.1109/58.105250］
Lu J Y， Lee P Y and Huang C C. 2022. Improving image quality for single-angle plane wave ultrasound imaging with convolutional neural network beamformer. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 69（4）： 1326-1336 ［DOI： 10.1109/TUFFC.2022.3152689http://dx.doi.org/10.1109/TUFFC.2022.3152689］
Luijten B， Cohen R， de Bruijn F J， Schmeitz H A W， Mischi M， Eldar Y C and van Sloun R J G. 2020. Adaptive ultrasound beamforming using deep learning. IEEE Transactions on Medical Imaging， 39（12）： 3967-3978 ［DOI： 10.1109/TMI.2020.3008537http://dx.doi.org/10.1109/TMI.2020.3008537］
Mallart R and Fink M. 1991. The van Cittert-Zernike theorem in pulse echo measurements. The Journal of the Acoustical Society of America， 90（5）： 2718-2727 ［DOI： 10.1121/1.401867http://dx.doi.org/10.1121/1.401867］
Matrone G， Ramalli A， D’Hooge J， Tortoli P and Magenes G. 2020. A comparison of coherence-based beamforming techniques in high-frame-rate ultrasound imaging with multi-line transmission. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 67（2）： 329-340 ［DOI： 10.1109/TUFFC.2019.2945365http://dx.doi.org/10.1109/TUFFC.2019.2945365］
Matrone G， Savoia A S， Caliano G and Magenes G. 2015. The delay multiply and sum beamforming algorithm in ultrasound B-mode medical imaging. IEEE Transactions on Medical Imaging， 34（4）： 940-949 ［DOI： 10.1109/TMI.2014.2371235http://dx.doi.org/10.1109/TMI.2014.2371235］
Muresan D D and Parks T W. 2004. Adaptively quadratic （AQua） image interpolation. IEEE Transactions on Image Processing， 13（5）： 690-698 ［DOI： 10.1109/TIP.2004.826097http://dx.doi.org/10.1109/TIP.2004.826097］
Nair A A， Tran T D and Bell M A L. 2018. Robust short-lag spatial coherence imaging. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 65（3）： 366-377 ［DOI： 10.1109/TUFFC.2017.2780084http://dx.doi.org/10.1109/TUFFC.2017.2780084］
Nair A A， Tran T D， Reiter A and Bell M A L. 2019. A generative adversarial neural network for beamforming ultrasound images： invited presentation//Proceedings of the 53rd Annual Conference on Information Sciences and Systems （CISS）. Baltimore， USA： IEEE： 1-6 ［DOI： 10.1109/CISS.2019.8692835http://dx.doi.org/10.1109/CISS.2019.8692835］
Nguyen N Q and Prager R W. 2016. High-resolution ultrasound imaging with unified pixel-based beamforming. IEEE Transactions on Medical Imaging， 35（1）： 98-108 ［DOI： 10.1109/TMI.2015.2456982http://dx.doi.org/10.1109/TMI.2015.2456982］
Nguyen N Q and Prager R W. 2017. Minimum variance approaches to ultrasound pixel-based beamforming. IEEE Transactions on Medical Imaging， 36（2）： 374-384 ［DOI： 10.1109/TMI.2016.2609889http://dx.doi.org/10.1109/TMI.2016.2609889］
Nguyen N Q and Prager R W. 2018. A spatial coherence approach to minimum variance beamforming for plane-wave compounding. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 65（4）： 522-534 ［DOI： 10.1109/TUFFC.2018.2793580http://dx.doi.org/10.1109/TUFFC.2018.2793580］
Patel V and Mistree K. 2013. A review on different image interpolation techniques for image enhancement. International Journal of Emerging Technology and Advanced Engineering， 3（12）： 129-133
Peng H. 2008. Introduction to Ultrasound Imaging Algorithms. Hefei： University of Science and Technology of China Press
彭虎. 2008. 超声成像算法导论. 合肥： 中国科学技术大学出版社
Perdios D， Vonlanthen M， Besson A， Martinez F， Arditi M and Thiran J P. 2018. Deep convolutional neural network for ultrasound image enhancement//2018 IEEE International Ultrasonics Symposium （IUS）. Kobe， Japan： IEEE： 1-4 ［DOI： 10.1109/ULTSYM.2018.8580183http://dx.doi.org/10.1109/ULTSYM.2018.8580183］
Perdios D， Vonlanthen M， Martinez F， Arditi M and Thiran J P. 2022. CNN-based image reconstruction method for ultrafast ultrasound imaging. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 69（4）： 1154-1168 ［DOI： 10.1109/TUFFC.2021.3131383http://dx.doi.org/10.1109/TUFFC.2021.3131383］
Prieur F， Rindal O M H and Austeng A. 2018. Signal coherence and image amplitude with the filtered delay multiply and sum beamformer. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 65（7）： 1133-1140 ［DOI： 10.1109/TUFFC.2018.2831789http://dx.doi.org/10.1109/TUFFC.2018.2831789］
Qi Y X， Wang Y Y and Guo W. 2018. Joint subarray coherence and minimum variance beamformer for multitransmission ultrasound imaging modalities. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 65（9）： 1600-1617 ［DOI： 10.1109/TUFFC.2018.2851073http://dx.doi.org/10.1109/TUFFC.2018.2851073］
Qi Y X， Wang Y Y， Yu J H and Guo Y. 2019a. Eigenspace-based minimum variance beamformer for short-lag spatial coherence medical ultrasound imaging. Journal of Medical Imaging and Health Informatics， 9（9）： 1955-1960 ［DOI： 10.1166/JMIHI.2019.2821http://dx.doi.org/10.1166/JMIHI.2019.2821］
Qi Y X， Wang Y Y， Yu J H and Guo Y. 2019b. Short-lag spatial coherence imaging using minimum variance beamforming on dual apertures. BioMedical Engineering OnLine， 18（1）： 1-18 ［DOI： 10.1186/s12938-019-0671-0http://dx.doi.org/10.1186/s12938-019-0671-0］
Salari A and Asl B M. 2021. User parameter-free minimum variance beamformer in medical ultrasound imaging. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 68（7）： 2397-2406 ［DOI： 10.1109/TUFFC.2021.3065876http://dx.doi.org/10.1109/TUFFC.2021.3065876］
Seoni S， Salvi M， Matrone G and Meiburger K M. 2022. Ultrasound image beamforming optimization using a generative adversarial network//2022 IEEE International Ultrasonics Symposium （IUS）. Venice， Italy： IEEE： 1-4 ［DOI： 10.1109/IUS54386.2022.9957306http://dx.doi.org/10.1109/IUS54386.2022.9957306］
Shi J， Wang L L， Wang S S， Chen Y X， Wang Q， Wei D M， Liang S J， Peng J L， Yi J J， Liu S F， Ni D， Wang M L， Zhang D Q and Shen D G. 2020. Applications of deep learning in medical imaging： a survey. Journal of Image and Graphics， 25（10）： 1953-1981
施俊， 汪琳琳， 王珊珊， 陈艳霞， 王乾， 魏冬铭， 梁淑君， 彭佳林， 易佳锦， 刘盛锋， 倪东， 王明亮， 张道强， 沈定刚. 2020. 深度学习在医学影像中的应用综述. 中国图象图形学报， 25（10）： 1953-1981 ［DOI： 10.11834/jig.200255http://dx.doi.org/10.11834/jig.200255］
Sun N and Li H N. 2019. Super resolution reconstruction of images based on interpolation and full convolutional neural network and application in medical fields. IEEE Access， 7： 186470-186479 ［DOI： 10.1109/ACCESS.2019.2960828http://dx.doi.org/10.1109/ACCESS.2019.2960828］
Szegedy C， Liu W， Jia Y Q， Sermanet P， Reed S， Anguelov D， Erhan D， Vanhoucke V and Rabinovich A. 2015. Going deeper with convolutions//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston， USA： IEEE： 1-9 ［DOI： 10.1109/CVPR.2015.7298594http://dx.doi.org/10.1109/CVPR.2015.7298594］
Tanter M and Fink M. 2014. Ultrafast imaging in biomedical ultrasound. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 61（1）： 102-119 ［DOI： 10.1109/TUFFC.2014.2882http://dx.doi.org/10.1109/TUFFC.2014.2882］
Tran D V， Li-Thiao-Té S， Luong M， Le-Tien T， Dibos F and Rocchisani J M. 2016. Example-based super-resolution for enhancing spatial resolution of medical images//Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society （EMBC）. Orlando， USA： IEEE： 457-460 ［DOI： 10.1109/EMBC.2016.7590738http://dx.doi.org/10.1109/EMBC.2016.7590738］
Trinh D H， Luong M， Dibos F， Rocchisani J M， Pham C D and Nguyen T Q. 2014. Novel example-based method for super-resolution and denoising of medical images. IEEE Transactions on Image Processing， 23（4）： 1882-1895 ［DOI： 10.1109/TIP.2014.2308422http://dx.doi.org/10.1109/TIP.2014.2308422］
Wang Y D， Zheng C C and Peng H. 2019a. Dynamic coherence factor based on the standard deviation for coherent plane-wave compounding. Computers in Biology and Medicine， 108： 249-262 ［DOI： 10.1016/J.COMPBIOMED.2019.03.022http://dx.doi.org/10.1016/J.COMPBIOMED.2019.03.022］
Wang Y H and Li P C. 2014. SNR-dependent coherence-based adaptive imaging for high-frame-rate ultrasonic and photoacoustic imaging. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 61（8）： 1419-1432 ［DOI： 10.1109/TUFFC.2014.3051http://dx.doi.org/10.1109/TUFFC.2014.3051］
Wang Y M， Qi Y X and Wang Y Y. 2019b. Array smoothing coherence factor in the plane-wave ultrasound imaging. Journal of Medical Imaging and Health Informatics， 9（7）： 1483-1490 ［DOI： 10.1166/jmihi.2019.2748http://dx.doi.org/10.1166/jmihi.2019.2748］
Wang Y M， Qi Y X and Wang Y Y. 2020a. A mixed transmitting-receiving beamformer with a robust generalized coherence factor： enhanced resolution and contrast. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 67（8）： 1573-1589 ［DOI： 10.1109/TUFFC.2020.2977942http://dx.doi.org/10.1109/TUFFC.2020.2977942］
Wang Y N， Kempski K， Kang J U and Bell M A L. 2020b. A conditional adversarial network for single plane wave beamforming//2020 IEEE International Ultrasonics Symposium （IUS）. Las Vegas， USA： IEEE： 1-4 ［DOI： 10.1109/IUS46767.2020.9251729http://dx.doi.org/10.1109/IUS46767.2020.9251729］
Wang Y Y， Su T and Zhang S. 2019c. Multi-line acquisition with delay multiply and sum beamforming in phased array ultrasound imaging， validation of simulation and in vitro. Ultrasonics， 96： 123-131 ［DOI： 10.1016/J.ULTRAS.2019.02.004http://dx.doi.org/10.1016/J.ULTRAS.2019.02.004］
Wiacek A， Gonzlez E and Bell M A L. 2020. CohereNet： a deep learning architecture for ultrasound spatial correlation estimation and coherence-based beamforming. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 67（12）： 2574-2583 ［DOI： 10.1109/TUFFC.2020.2982848http://dx.doi.org/10.1109/TUFFC.2020.2982848］
Wiacek A， Rindal O M H， Falomo E， Myers K， Fabrega-Foster K， Harvey S and Lediju Bell M A. 2019. Robust short-lag spatial coherence imaging of breast ultrasound data： initial clinical results. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 66（3）： 527-540 ［DOI： 10.1109/TUFFC.2018.2883427http://dx.doi.org/10.1109/TUFFC.2018.2883427］
Wiley C A. 1985. Synthetic aperture radars. IEEE Transactions on Aerospace and Electronic Systems， AES-21（3）： 440-443 ［DOI： 10.1109/TAES.1985.310578http://dx.doi.org/10.1109/TAES.1985.310578］
Xie H W， Guo H， Zhou G Q， Nguyen N Q and Prager R W. 2022. Improved ultrasound image quality with pixel-based beamforming using a Wiener-filter and a SNR-dependent coherence factor. Ultrasonics， 119： #106594 ［DOI： 10.1016/J.ULTRAS.2021.106594http://dx.doi.org/10.1016/J.ULTRAS.2021.106594］
Yan H， Liu Y L， Jin L W and Bai X. 2023. The development， application， and future of LLM similar to ChatGPT. Journal of Image and Graphics， 28（9）： 2749-2762
严昊， 刘禹良， 金连文， 白翔. 2023. 类ChatGPT大模型发展、应用和前景. 中国图象图形学报， 28（9）： 2749-2762 ［DOI： 10.11834/JIG.230536http://dx.doi.org/10.11834/JIG.230536］
Yan X， Qi Y X， Wang Y M and Wang Y Y. 2021. High resolution， high contrast beamformer using minimum variance and plane wave nonlinear compounding with low complexity. Sensors， 21（2）： #394 ［DOI： 10.3390/S21020394http://dx.doi.org/10.3390/S21020394］
Yan X， Qi Y X， Wang Y M and Wang Y Y. 2022. Regional-lag signed delay multiply and sum beamforming in ultrafast ultrasound imaging. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 69（2）： 580-591 ［DOI： 10.1109/TUFFC.2021.3127878http://dx.doi.org/10.1109/TUFFC.2021.3127878］
Yang J C， Wright J， Huang T and Ma Y. 2008. Image super-resolution as sparse representation of raw image patches//Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage， USA： IEEE： 1-8 ［DOI： 10.1109/CVPR.2008.4587647http://dx.doi.org/10.1109/CVPR.2008.4587647］
Yang J C， Wright J， Huang T S and Ma Y. 2010. Image super-resolution via sparse representation. IEEE Transactions on Image Processing， 19（11）： 2861-2873 ［DOI： 10.1109/TIP.2010.2050625http://dx.doi.org/10.1109/TIP.2010.2050625］
Zhang Q， Li B， Shen M F and Yang J Y. 2016. A novel ultrasonic image zooming algorithm based on sparse representation//Proceedings of the 3rd International Conference on Systems and Informatics （ICSAI）. Shanghai， China： IEEE： 861-865 ［DOI： 10.1109/ICSAI.2016.7811071http://dx.doi.org/10.1109/ICSAI.2016.7811071］
Zhang Y L， Hu Y， Higashita R and Liu J. 2022. A review of generative adversarial networks and the application in medical image. Journal of Image and Graphics， 27（3）： 687-703
张颖麟， 胡衍， 东田理沙， 刘江. 2022. 生成对抗式网络及其医学影像应用研究综述. 中国图象图形学报， 27（3）： 687-703 ［DOI： 10.11834/JIG.210247http://dx.doi.org/10.11834/JIG.210247］
Zhao J X. 2017. Studies on the Adaptive Beamforming Methods in High-Frame-Rate Ultrasound Imaging. Shanghai： Fudan University
赵金鑫. 2017. 高帧率医学超声成像的自适应波束形成方法研究. 上海： 复旦大学
Zhou Z X， Guo Y and Wang Y Y. 2021a. Handheld ultrasound video high-quality reconstruction using a low-rank representation multipathway generative adversarial network. IEEE Transactions on Neural Networks and Learning Systems， 32（2）： 575-588 ［DOI： 10.1109/TNNLS.2020.3025380http://dx.doi.org/10.1109/TNNLS.2020.3025380］
Zhou Z X， Guo Y and Wang Y Y. 2021b. Ultrasound deep beamforming using a multiconstrained hybrid generative adversarial network. Medical Image Analysis， 71： #102086 ［DOI： 10.1016/J.MEDIA.2021.102086http://dx.doi.org/10.1016/J.MEDIA.2021.102086］
Zhou Z X， Wang Y Y， Guo Y， Jiang X M and Qi Y X. 2020a. Ultrafast plane wave imaging with line-scan-quality using an ultrasound-transfer generative adversarial network. IEEE Journal of Biomedical and Health Informatics， 24（4）： 943-956 ［DOI： 10.1109/JBHI.2019.2950334http://dx.doi.org/10.1109/JBHI.2019.2950334］
Zhou Z X， Wang Y Y， Guo Y， Qi Y X and Yu J H. 2020b. Image quality improvement of hand-held ultrasound devices with a two-stage generative adversarial network. IEEE Transactions on Biomedical Engineering， 67（1）： 298-311 ［DOI： 10.1109/TBME.2019.2912986http://dx.doi.org/10.1109/TBME.2019.2912986］
Zhou Z X， Wang Y Y， Yu J H， Guo W and Li Z J. 2019. Super-resolution reconstruction of plane-wave ultrasound image based on a multi-angle parallel U-Net with maxout unit and novel loss function. Journal of Medical Imaging and Health Informatics， 9（1）： 109-118 ［DOI： 10.1166/JMIHI.2019.2548http://dx.doi.org/10.1166/JMIHI.2019.2548］
Zhou Z X， Wang Y Y， Yu J H， Guo Y， Guo W and Qi Y X. 2018. High spatial-temporal resolution reconstruction of plane-wave ultrasound images with a multichannel multiscale convolutional neural network. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 65（11）： 1983-1996 ［DOI： 10.1109/TUFFC.2018.2865504http://dx.doi.org/10.1109/TUFFC.2018.2865504］
Ziksari M S and Asl B M. 2021. Minimum variance combined with modified delay multiply-and-sum beamforming for plane-wave compounding. IEEE Transactions on Ultrasonics， Ferroelectrics， and Frequency Control， 68（5）： 1641-1652 ［DOI： 10.1109/TUFFC.2020.3043795http://dx.doi.org/10.1109/TUFFC.2020.3043795］
相关作者
相关机构
京公网安备11010802024621