Review of high-quality ultrasound imaging and reconstruction
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 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 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 fil-tered 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 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 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 lat-eral 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 diam-eter,robust principal component analysis,and linear attenuation weighting are explored to address the issue of poor resolu-tion.For F-DMAS-based methods,approaches to further enhance imaging quality and decrease computational cost are dis-cussed.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 advan-tages 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 learn-ing 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 prog-ress at home and abroad and discusses future development trends.Concerning beamforming algorithms,domestic and for-eign scholars focus on adaptive beamforming methods.Moreover,the future development and research trends of beamform-ing 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)syn-chronously 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 tra-ditional 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)combin-ing 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.
NETL
NSTL
万方数据
