Most Cited Articles

A statistical model is presented that represents the distributions of major tissue classes in single-channel magnetic resonance (MR) cerebral images. Using the model, cerebral images are segmented into gray matter, white matter, and cerebrospinal fluid (CSF). The model accounts for random noise, magnetic field inhomogeneities, and biological variations of the tissues. Intensity measurements are mo...

提出了一种统计模型，用于表示单通道磁共振（MR）脑图像中主要组织类别的分布。利用该模型，将脑图像分割为灰质、白质和脑脊液（CSF）。该模型考虑了随机噪声、磁场不均匀性和组织的生物学变异。强度测量被...

Although diagnostic medical imaging provides enormous benefits in the early detection and accuracy diagnosis of various diseases, there are growing concerns on the potential side effect of radiation induced genetic, cancerous and other diseases. How to reduce radiation dose while maintaining the diagnostic performance is a major challenge in the computed tomography (CT) field. Inspired by the compressive sensing theory, the sparse constraint in terms of total variation (TV) minimization has already led to promising results for low-dose CT reconstruction. Compared to the discrete gradient transform used in the TV method, dictionary learning is proven to be an effective way for sparse representation. On the other hand, it is important to consider the statistical property of projection data in the low-dose CT case. Recently, we have developed a dictionary learning based approach for low-dose X-ray CT. In this paper, we present this method in detail and evaluate it in experiments. In our method, the sparse constraint in terms of a redundant dictionary is incorporated into an objective function in a statistical iterative reconstruction framework. The dictionary can be either predetermined before an image reconstruction task or adaptively defined during the reconstruction process. An alternating minimization scheme is developed to minimize the objective function. Our approach is evaluated with low-dose X-ray projections collected in animal and human CT studies, and the improvement associated with dictionary learning is quantified relative to filtered backprojection and TV-based reconstructions. The results show that the proposed approach might produce better images with lower noise and more detailed structural features in our selected cases. However, there is no proof that this is true for all kinds of structures.

尽管诊断性医学成像在多种疾病的早期检测和准确诊断中提供了巨大益处，但人们对辐射诱导的遗传性、癌性及其他疾病的潜在副作用越来越担忧。如何在保持诊断性能的同时降低辐射剂量是计算机断层扫描（CT）领域的一个主要挑战。受压缩感知理论的启发，基于全变差（TV）最小化的稀疏约束已在低剂量CT重建中取得了有希望的结果。与TV方法中使用的离散梯度变换相比，字典学习被证明是一种有效的稀疏表示方式。另一方面，在低剂量CT情况下，考虑投影数据的统计特性也很重要。最近，我们开发了一种基于字典学习的低剂量X射线CT方法。本文详细介绍了该方法并通过实验进行了评估。在我们的方法中，将冗余字典的稀疏约束纳入统计迭代重建框架的目标函数中。字典可以在图像重建任务前预定义，也可以在重建过程中自适应定义。我们开发了一种交替最小化方案来最小化目标函数。我们使用动物和人类CT研究中收集的低剂量X射线投影来评估我们的方法，并相对于滤波反投影和基于TV的重建量化了字典学习的改进。结果表明，在我们选择的案例中，所提出的方法可能产生噪声更低、结构特征更详细的更好图像。然而，没有证据表明这对所有类型的结构都成立。

The National Library of Medicine (NLM) is developing a digital chest X-ray (CXR) screening system for deployment in resource constrained communities and developing countries worldwide with a focus on early detection of tuberculosis. A critical component in the computer-aided diagnosis of digital CXRs is the automatic detection of the lung regions. In this paper, we present a nonrigid registration-driven robust lung segmentation method using image retrieval-based patient specific adaptive lung models that detects lung boundaries, surpassing state-of-the-art performance. The method consists of three main stages: 1) a content-based image retrieval approach for identifying training images (with masks) most similar to the patient CXR using a partial Radon transform and Bhattacharyya shape similarity measure, 2) creating the initial patient-specific anatomical model of lung shape using SIFT-flow for deformable registration of training masks to the patient CXR, and 3) extracting refined lung boundaries using a graph cuts optimization approach with a customized energy function. Our average accuracy of 95.4% on the public JSRT database is the highest among published results. A similar degree of accuracy of 94.1% and 91.7% on two new CXR datasets from Montgomery County, MD, USA, and India, respectively, demonstrates the robustness of our lung segmentation approach.

美国国家医学图书馆（NLM）正在开发一种数字胸部X光（CXR）筛查系统，用于资源受限的社区和发展中国家，重点关注结核病的早期检测。在数字CXR的计算机辅助诊断中，自动检测肺部区域是一个关键组成部分。本文提出了一种非刚性配准方法...

Cerebral microbleeds (CMBs) are small haemorrhages nearby blood vessels. They have been recognized as important diagnostic biomarkers for many cerebrovascular diseases and cognitive dysfunctions. In current clinical routine, CMBs are manually labelled by radiologists but this procedure is laborious, time-consuming, and error prone. In this paper, we propose a novel automatic method to detect CMBs from magnetic resonance (MR) images by exploiting the 3D convolutional neural network (CNN). Compared with previous methods that employed either low-level hand-crafted descriptors or 2D CNNs, our method can take full advantage of spatial contextual information in MR volumes to extract more representative high-level features for CMBs, and hence achieve a much better detection accuracy. To further improve the detection performance while reducing the computational cost, we propose a cascaded framework under 3D CNNs for the task of CMB detection. We first exploit a 3D fully convolutional network (FCN) strategy to retrieve the candidates with high probabilities of being CMBs, and then apply a well-trained 3D CNN discrimination model to distinguish CMBs from hard mimics. Compared with traditional sliding window strategy, the proposed 3D FCN strategy can remove massive redundant computations and dramatically speed up the detection process. We constructed a large dataset with 320 volumetric MR scans and performed extensive experiments to validate the proposed method, which achieved a high sensitivity of 93.16% with an average number of 2.74 false positives per subject, outperforming previous methods using low-level descriptors or 2D CNNs by a significant margin. The proposed method, in principle, can be adapted to other biomarker detection tasks from volumetric medical data.

脑微出血是血管附近的小出血。它们已被认为是许多脑血管疾病和认知功能障碍的重要诊断生物标志物。在当前的临床常规中，脑微出血由放射科医生手动标记，但这一过程费力、耗时且容易出错。在本文中，我们提出了一种新颖的自动检测脑微出血的方法……

With the increased interest in computer-aided image analysis methods, there is a greater need for objective methods of algorithm evaluation. Validation of in vivo MRI studies is complicated by a lack of reference data and the difficulty of constructing anatomically realistic physical phantoms. The authors present here an extensible MRI simulator that efficiently generates realistic three-dimensional (3-D) brain images using a hybrid Bloch equation and tissue template simulation that accounts for image contrast, partial volume, and noise. This allows image analysis methods to be evaluated with controlled degradations of image data.

随着对计算机辅助图像分析方法的兴趣增加，对算法评估的客观方法的需求也更大。体内MRI研究的验证因缺乏参考数据和构建解剖学逼真的物理体模的困难而变得复杂。作者在此介绍一种可扩展的MRI模拟器，能够高效生成逼真的三维...

This paper describes a statistical image reconstruction method for X-ray computed tomography (CT) that is based on a physical model that accounts for the polyenergetic X-ray source spectrum and the measurement nonlinearities caused by energy-dependent attenuation. We assume that the object consists of a given number of nonoverlapping materials, such as soft tissue and bone. The attenuation coefficient of each voxel is the product of its unknown density and a known energy-dependent mass attenuation coefficient. We formulate a penalized-likelihood function for this polyenergetic model and develop an ordered-subsets iterative algorithm for estimating the unknown densities in each voxel. The algorithm monotonically decreases the cost function at each iteration when one subset is used. Applying this method to simulated X-ray CT measurements of objects containing both bone and soft tissue yields images with significantly reduced beam hardening artifacts.

本文描述了一种针对X射线计算机断层扫描（CT）的统计图像重建方法，该方法基于物理模型，考虑了多能X射线源光谱以及由能量依赖衰减引起的测量非线性。我们假设物体由给定数量的非重叠材料组成，例如软组织和骨骼。每个体素的衰减系数是其未知密度与已知能量依赖质量衰减系数的乘积。我们为此多能模型制定了惩罚似然函数，并开发了一种有序子集迭代算法来估计每个体素中的未知密度。当使用一个子集时，该算法在每次迭代中单调地降低成本函数。将此方法应用于包含骨骼和软组织的物体的模拟X射线CT测量，生成的图像显著减少了射束硬化伪影。

The watershed transform has interesting properties that make it useful for many different image segmentation applications: it is simple and intuitive, can be parallelized, and always produces a complete division of the image. However, when applied to medical image analysis, it has important drawbacks (oversegmentation, sensitivity to noise, poor detection of thin or low signal to noise ratio structures). We present an improvement to the watershed transform that enables the introduction of prior information in its calculation. We propose to introduce this information via the use of a previous probability calculation. Furthermore, we introduce a method to combine the watershed transform and atlas registration, through the use of markers. We have applied our new algorithm to two challenging applications: knee cartilage and gray matter/white matter segmentation in MR images. Numerical validation of the results is provided, demonstrating the strength of the algorithm for medical image segmentation.

分水岭变换具有有趣的特性，使其在许多不同的图像分割应用中非常有用：它简单直观，可以并行化，并且总是产生图像的完整分割。然而，当应用于医学图像分析时，它具有重要的缺点（过度分割、对噪声敏感、对薄结构或低信噪比结构的检测能力差...）

The authors developed a simple method for determining the presampling modulation transfer function (MTF). which includes the unsharpness of the detector and the effect of the sampling aperture, in digital radiographic (DR) systems. With this method, the presampling MTF is determined by the Fourier transform of a 'finely sampled' line spread function (LSF) obtained with a slightly angulated slit in a single exposure. Since the effective sampling distance becomes much smaller than the original sampling distance of the DR system, the effect of aliasing on the MTF calculations can be eliminated. The authors applied this method to the measurement of the presampling MTF of a compound radiographic system and examined the directional dependence, the effect of exponential extrapolation, and the effect of different sampling distances. It is shown that the technique of multiple slit exposure and exponential extrapolation of the LSF tail, which has been commonly used in analog seven-film systems, can be employed in DR systems. The authors determined the glare fraction in order to estimate the component of low-frequency drop mainly due to 'glare'. >

中文摘要翻译待生成

We present a deep convolutional neural network for breast cancer screening exam classification, trained, and evaluated on over 200000 exams (over 1000000 images). Our network achieves an AUC of 0.895 in predicting the presence of cancer in the breast, when tested on the screening population. We attribute the high accuracy to a few technical advances. 1) Our network’s novel two-stage architecture and training procedure, which allows us to use a high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels. 2) A custom ResNet-based network used as a building block of our model, whose balance of depth and width is optimized for high-resolution medical images. 3) Pretraining the network on screening BI-RADS classification, a related task with more noisy labels. 4) Combining multiple input views in an optimal way among a number of possible choices. To validate our model, we conducted a reader study with 14 readers, each reading 720 screening mammogram exams, and show that our model is as accurate as experienced radiologists when presented with the same data. We also show that a hybrid model, averaging the probability of malignancy predicted by a radiologist with a prediction of our neural network, is more accurate than either of the two separately. To further understand our results, we conduct a thorough analysis of our network’s performance on different subpopulations of the screening population, the model’s design, training procedure, errors, and properties of its internal representations. Our best models are publicly available at https://github.com/nyukat/breast_cancer_classifier .

中文摘要翻译待生成

The solution of impedance distribution in electrical impedance tomography is a nonlinear inverse problem that requires the use of a regularization method. The generalized Tikhonov regularization methods have been popular in the solution of many inverse problems. The regularization matrices that are usually used with the Tikhonov method are more or less ad hoc and the implicit prior assumptions are, thus, in many cases inappropriate. In this paper, the authors propose an approach to the construction of the regularization matrix that conforms to the prior assumptions on the impedance distribution. The approach is based on the construction of an approximating subspace for the expected impedance distributions. It is shown by simulations that the reconstructions obtained with the proposed method are better than with two other schemes of the same type when the prior is compatible with the true object. On the other hand, when the prior is incompatible with the true object, the method will still give reasonable estimates.

电阻抗成像中阻抗分布的求解是一个非线性逆问题，需要使用正则化方法。广义Tikhonov正则化方法在许多逆问题的求解中很受欢迎。通常与Tikhonov方法一起使用的正则化矩阵或多或少是特设的，因此隐含的先验假设在许多情况下是不合适的。在本文中，作者提出了一种构建与阻抗分布先验假设相一致的正则化矩阵的方法。该方法基于为预期的阻抗分布构建近似子空间。模拟结果表明，当先验与真实对象一致时，所提出的方法获得的重建结果优于其他两种同类方案。另一方面，当先验与真实对象不一致时，该方法仍能给出合理的估计。

The high complexity of cortical convolutions in humans is very challenging both for engineers to measure and compare it, and for biologists and physicians to understand it. In this paper, we propose a surface-based method for the quantification of cortical gyrification. Our method uses accurate 3-D cortical reconstruction and computes local measurements of gyrification at thousands of points over the whole cortical surface. The potential of our method to identify and localize precisely gyral abnormalities is illustrated by a clinical study on a group of children affected by 22q11 Deletion Syndrome, compared to control individuals.

人类皮层卷积的高度复杂性对工程师测量和比较它，以及对生物学家和医生理解它都极具挑战性。在本文中，我们提出了一种基于表面的皮层回旋量化方法。我们的方法使用精确的三维皮层重建，并在整个皮层表面的数千个点上计算回旋的局部测量。通过对一组患有22q11缺失综合征的儿童与对照个体的临床研究，展示了我们的方法在精确定位和识别脑回异常方面的潜力。

Acquiring images of the same anatomy with multiple different contrasts increases the diversity of diagnostic information available in an MR exam. Yet, the scan time limitations may prohibit the acquisition of certain contrasts, and some contrasts may be corrupted by noise and artifacts. In such cases, the ability to synthesize unacquired or corrupted contrasts can improve diagnostic utility. For m...

中文摘要翻译待生成

Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-a...

中文摘要翻译待生成

Accurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learning-based model for automatic COVID-19 diagnosis on chest CT is helpful to counter the outbreak of SARS-CoV-2. A weakly-supervised deep learning framework was developed using 3D CT volumes for COVID-19 classification and lesion localization. For each pati...

中文摘要翻译待生成

Automated skin lesion classification in dermoscopy images is an essential way to improve the diagnostic performance and reduce melanoma deaths. Although deep convolutional neural networks (DCNNs) have made dramatic breakthroughs in many image classification tasks, accurate classification of skin lesions remains challenging due to the insufficiency of training data, inter-class similarity, intra-cl...

在皮肤镜图像中自动进行皮肤病变分类是提高诊断性能和减少黑色素瘤死亡的重要方法。尽管深度卷积神经网络（DCNN）在许多图像分类任务中取得了突破性进展，但由于训练数据不足、类间相似性和类内差异等原因，皮肤病变的准确分类仍然具有挑战性。

When one integrates a function of two variables x,y - a point function f(P) in the plane - subject to suitable regularity conditions along an arbitrary straight line g then one obtains in the integral values F(g), a line function. In Part A of the present paper the problem which is solved is the inversion of this linear functional transformation, that is the following questions are answered: can every line function satisfying suitable regularity conditions be regarded as constructed in this way? If so, is f uniquely known from F and how can f be calculated? In Part B a solution of the dual problem of calculating a line function F(g) from its point mean values f(P) is solved in a certain sense. Finally, in Part C certain generalizations are discussed, prompted by consideration of non-Euclidean manifolds as well as higher dimensional spaces. The treatment of these problems, themselves of interest, gains enhanced importance through the numerous relationships that exist between this topic and the theory of logarithmic and Newtonian potentials. These are mentioned at appropriate places in the text.

中文摘要翻译待生成

Compressed sensing magnetic resonance imaging (CS-MRI) has provided theoretical foundations upon which the time-consuming MRI acquisition process can be accelerated. However, it primarily relies on iterative numerical solvers, which still hinders their adaptation in time-critical applications. In addition, recent advances in deep neural networks have shown their potential in computer vision and im...

压缩感知磁共振成像（CS-MRI）为加速耗时的MRI采集过程提供了理论基础。然而，它主要依赖于迭代数值求解器，这仍然阻碍了其在时间关键应用中的适应。此外，深度神经网络的最新进展显示了它们在计算机视觉和图像处理中的潜力。

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.

中文摘要翻译待生成

A system for the computerized analysis of images obtained from epiluminescence microscopy (ELM) has been developed to enhance the early recognition of malignant melanoma. As an initial step, the binary mask of the skin lesion is determined by several basic segmentation algorithms together with a fusion strategy. A set of features containing shape and radiometric features as well as local and global parameters is calculated to describe the malignancy of a lesion. Significant features are then selected from this set by application of statistical feature subset selection methods. The final kNN classification delivers a sensitivity of 87% with a specificity of 92%.

开发了一套用于计算机分析表面发光显微镜（ELM）图像的系统，以增强恶性黑色素瘤的早期识别。作为初始步骤，通过几种基本分割算法和融合策略确定皮肤病变的二进制掩模。计算包含形状和辐射特征以及局部和全局参数的一组特征，以描述病变的恶性程度。然后通过应用统计特征子集选择方法从该组中选择显著特征。最终的kNN分类实现了87%的灵敏度和92%的特异性。

The Hausdorff Distance (HD) is widely used in evaluating medical image segmentation methods. However, the existing segmentation methods do not attempt to reduce HD directly. In this paper, we present novel loss functions for training convolutional neural network (CNN)-based segmentation methods with the goal of reducing HD directly. We propose three methods to estimate HD from the segmentation probability map produced by a CNN. One method makes use of the distance transform of the segmentation boundary. Another method is based on applying morphological erosion on the difference between the true and estimated segmentation maps. The third method works by applying circular/spherical convolution kernels of different radii on the segmentation probability maps. Based on these three methods for estimating HD, we suggest three loss functions that can be used for training to reduce HD. We use these loss functions to train CNNs for segmentation of the prostate, liver, and pancreas in ultrasound, magnetic resonance, and computed tomography images and compare the results with commonly-used loss functions. Our results show that the proposed loss functions can lead to approximately 18-45% reduction in HD without degrading other segmentation performance criteria such as the Dice similarity coefficient. The proposed loss functions can be used for training medical image segmentation methods in order to reduce the large segmentation errors.

中文摘要翻译待生成

Accelerating the data acquisition of dynamic magnetic resonance imaging leads to a challenging ill-posed inverse problem, which has received great interest from both the signal processing and machine learning communities over the last decades. The key ingredient to the problem is how to exploit the temporal correlations of the MR sequence to resolve aliasing artifacts. Traditionally, such observation led to a formulation of an optimization problem, which was solved using iterative algorithms. Recently, however, deep learning-based approaches have gained significant popularity due to their ability to solve general inverse problems. In this paper, we propose a unique, novel convolutional recurrent neural network architecture which reconstructs high quality cardiac MR images from highly undersampled k-space data by jointly exploiting the dependencies of the temporal sequences as well as the iterative nature of the traditional optimization algorithms. In particular, the proposed architecture embeds the structure of the traditional iterative algorithms, efficiently modeling the recurrence of the iterative reconstruction stages by using recurrent hidden connections over such iterations. In addition, spatio–temporal dependencies are simultaneously learnt by exploiting bidirectional recurrent hidden connections across time sequences. The proposed method is able to learn both the temporal dependence and the iterative reconstruction process effectively with only a very small number of parameters, while outperforming current MR reconstruction methods in terms of reconstruction accuracy and speed.

加速动态磁共振成像的数据采集导致了一个具有挑战性的病态逆问题，这在过去几十年中引起了信号处理和机器学习社区的极大兴趣。该问题的关键是如何利用MR序列的时间相关性来消除混叠伪影。传统上，这种观察导致了优化问题的公式化，并通过迭代算法求解。然而，近年来，基于深度学习的方法因其解决一般逆问题的能力而获得了显著的普及。在本文中，我们提出了一种独特的、新颖的卷积递归神经网络架构，该架构通过联合利用时间序列的依赖性和传统优化算法的迭代性质，从高度欠采样的k空间数据中重建高质量的心脏MR图像。特别地，所提出的架构嵌入了传统迭代算法的结构，通过在这些迭代中使用递归隐藏连接有效地模拟了迭代重建阶段的循环性。此外，通过利用跨时间序列的双向递归隐藏连接同时学习时空依赖性。所提出的方法能够以非常少的参数有效学习时间依赖性和迭代重建过程，同时在重建精度和速度方面优于当前的MR重建方法。

Automatic whole-brain extraction from magnetic resonance images (MRI), also known as skull stripping, is a key component in most neuroimage pipelines. As the first element in the chain, its robustness is critical for the overall performance of the system. Many skull stripping methods have been proposed, but the problem is not considered to be completely solved yet. Many systems in the literature have good performance on certain datasets (mostly the datasets they were trained/tuned on), but fail to produce satisfactory results when the acquisition conditions or study populations are different. In this paper we introduce a robust, learning-based brain extraction system (ROBEX). The method combines a discriminative and a generative model to achieve the final result. The discriminative model is a Random Forest classifier trained to detect the brain boundary; the generative model is a point distribution model that ensures that the result is plausible. When a new image is presented to the system, the generative model is explored to find the contour with highest likelihood according to the discriminative model. Because the target shape is in general not perfectly represented by the generative model, the contour is refined using graph cuts to obtain the final segmentation. Both models were trained using 92 scans from a proprietary dataset but they achieve a high degree of robustness on a variety of other datasets. ROBEX was compared with six other popular, publicly available methods (BET, BSE, FreeSurfer, AFNI, BridgeBurner, and GCUT) on three publicly available datasets (IBSR, LPBA40, and OASIS, 137 scans in total) that include a wide range of acquisition hardware and a highly variable population (different age groups, healthy/diseased). The results show that ROBEX provides significantly improved performance measures for almost every method/dataset combination.

从磁共振图像（MRI）中进行全自动全脑提取（也称为颅骨剥离）是大多数神经影像处理流程中的关键组成部分。作为流程中的第一步，其鲁棒性对整个系统的性能至关重要。尽管已有许多颅骨剥离方法被提出，但该问题尚未被认为得到完全解决。文献中的许多系统在特定数据集（通常是它们训练/调整所用的数据集）上表现良好，但当采集条件或研究对象群体不同时，却无法产生令人满意的结果。本文介绍了一种鲁棒的、基于学习的脑提取系统（ROBEX）。该方法结合了判别模型和生成模型以获得最终结果。判别模型是一个随机森林分类器，用于检测脑边界；生成模型是一个点分布模型，确保结果的合理性。当新图像输入系统时，生成模型被探索以找到根据判别模型具有最高可能性的轮廓。由于生成模型通常不能完美表示目标形状，因此使用图割对轮廓进行细化以获得最终分割。两个模型均使用来自专有数据集的92次扫描进行训练，但在各种其他数据集上实现了高度的鲁棒性。将ROBEX与六种其他流行的公开可用方法（BET、BSE、FreeSurfer、AFNI、BridgeBurner和GCUT）在三个公开数据集（IBSR、LPBA40和OASIS，共137次扫描）上进行了比较，这些数据集包含广泛的采集硬件和高度可变的人群（不同年龄组、健康/患病）。结果表明，对于几乎每个方法/数据集组合，ROBEX都提供了显著改进的性能指标。

Most of ultrasound medical imaging systems currently on the market implement standard Delay and Sum (DAS) beamforming to form B-mode images. However, image resolution and contrast achievable with DAS are limited by the aperture size and by the operating frequency. For this reason, different beamformers have been presented in the literature that are mainly based on adaptive algorithms, which allow achieving higher performance at the cost of an increased computational complexity. In this paper, we propose the use of an alternative nonlinear beamforming algorithm for medical ultrasound imaging, which is called Delay Multiply and Sum (DMAS) and that was originally conceived for a RADAR microwave system for breast cancer detection. We modify the DMAS beamformer and test its performance on both simulated and experimentally collected linear-scan data, by comparing the Point Spread Functions, beampatterns, synthetic phantom and in vivo carotid artery images obtained with standard DAS and with the proposed algorithm. Results show that the DMAS beamformer outperforms DAS in both simulated and experimental trials and that the main improvement brought about by this new method is a significantly higher contrast resolution (i.e., narrower main lobe and lower side lobes), which turns out into an increased dynamic range and better quality of B-mode images.

目前市场上大多数超声医学成像系统采用标准的延迟与和（DAS）波束形成来生成B模式图像。然而，DAS的图像分辨率和对比度受限于孔径大小和工作频率。因此，文献中提出了不同的波束形成器，主要基于自适应算法，这些算法以增加计算复杂度为代价实现了更高的性能。在本文中，我们提出将一种替代的非线性波束形成算法用于医学超声成像，该算法称为延迟相乘与和（DMAS），最初是为用于乳腺癌检测的雷达成像微波系统而设计的。我们修改了DMAS波束形成器，并通过比较使用标准DAS和所提算法获得的点扩散函数、波束图、合成体模和体内颈动脉图像，在模拟和实验采集的线性扫描数据上测试其性能。结果表明，DMAS波束形成器在模拟和实验试验中均优于DAS，并且这种新方法带来的主要改进是显著更高的对比度分辨率（即更窄的主瓣和更低的旁瓣），从而提高了动态范围和B模式图像的质量。

A new method is proposed to subtract the count of scattered photons from that acquired with a photopeak window at each pixel in each planar image of single-photon emission computed tomography (SPECT). The subtraction is carried out using two sets of data: one set is acquired with a main window centered at photopeak energy and the other is acquired with two subwindows on both sides of the main window. The scattered photons included in the main window are estimated from the counts acquired with the subwindows and then they are subtracted from the count acquired with the main windows. Since the subtraction is performed at each pixel in each planar image, the proposed method has the potential to be more precise than conventional methods. For three different activity distributions in cylinder phantoms, simulation tests gave good agreement between the activity distributions reconstructed from unscattered photons and those from the corrected data. >

中文摘要翻译待生成

X-ray computed tomography (CT) using sparse projection views is a recent approach to reduce the radiation dose. However, due to the insufficient projection views, an analytic reconstruction approach using the filtered back projection (FBP) produces severe streaking artifacts. Recently, deep learning approaches using large receptive field neural networks such as U-Net have demonstrated impressive p...

X射线计算机断层扫描（CT）采用稀疏投影视角是一种降低辐射剂量的近期方法。然而，由于投影视角不足，使用滤波反投影（FBP）的分析重建方法会产生严重的条纹伪影。最近，使用大感受野神经网络（如U-Net）的深度学习方法已展现出令人印象深刻的...