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An improved echo planar high-speed imaging technique using spiral scan is presented and experimental advantages are discussed. This proposed spiral-scan echo planar imaging (SEPI) technique employs two linearly increasing sinusoidal gradient fields, which results in a spiral trajectory in the spatial frequency domain (k-domain) that covers the entire frequency domain uniformly. The advantages of the method are: 1) circularly symmetric T2 weighting, resulting in a circularly symmetric point spread function in the image domain; 2) elimination of discontinuities in gradient waveforms which in turn will reduce initial transient as well as steady-state distortions; and 3) effective rapid spiral-scan from dc to high frequency in a continuous fashion, which ensures multiple pulsing with interlacing for further resolution improvement without T2 decay image degradation. Some preliminary experimental results will be presented and further possible improvements suggested.

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One of the major drawbacks of magnetic resonance imaging (MRI) has been the lack of a standard and quantifiable interpretation of image intensities. Unlike in other modalities, such as X-ray computerized tomography, MR images taken for the same patient on the same scanner at different times may appear different from each other due to a variety of scanner-dependent variations and, therefore, the absolute intensity values do not have a fixed meaning. The authors have devised a two-step method wherein all images (independent of patients and the specific brand of the MR scanner used) can be transformed in such a may that for the same protocol and body region, in the transformed images similar intensities will have similar tissue meaning. Standardized images can be displayed with fixed windows without the need of per-case adjustment. More importantly, extraction of quantitative information about healthy organs or about abnormalities can be considerably simplified. This paper introduces and compares new variants of this standardizing method that can help to overcome some of the problems with the original method.

磁共振成像的主要缺点之一是缺乏对图像强度的标准和可量化解释。与其他模态（如X射线计算机断层扫描）不同，同一患者在同一扫描仪上不同时间拍摄的MR图像可能因多种扫描仪相关变化而彼此不同，因此，上述问题...

A projection operator onto a closed convex set in Hilbert space is one of the few examples of a nonlinear map that can be defined in simple abstract terms. Moreover, it minimizes distance and is nonexpansive, and therefore shares two of the more important properties of ordinary linear orthogonal projections onto closed linear manifolds. In this paper, we exploit the properties of these operators t...

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A method is presented to interpolate between neighboring slices in a grey-scale tomographic data set. Spatial correspondence between adjacent slices is established using a nonrigid registration algorithm based on B-splines which optimizes the normalized mutual information similarity measure. Linear interpolation of the image intensities is then carried out along the directions calculated by the re...

提出了一种在灰度断层数据集中对相邻切片进行插值的方法。利用基于B样条的非刚体配准算法建立相邻切片之间的空间对应关系，该算法优化了归一化互信息相似性度量。然后沿着由配准计算出的方向对图像强度进行线性插值...

In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the imaging performance, we apply a parallel ${1}\times {1}$ CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. The quantitative and qualitative evaluative results demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.

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Guidance systems designed for neurosurgery, hip surgery, and spine surgery, and for approaches to other anatomy that is relatively rigid can use rigid-body transformations to accomplish image registration. These systems often rely on point-based registration to determine the transformation, and many such systems use attached fiducial markers to establish accurate fiducial points for the registration, the points being established by some fiducial localization process. Accuracy is important to these systems, as is knowledge of the level of that accuracy. An advantage of marker-based systems, particularly those in which the markers are bone-implanted, is that registration error depends only on the fiducial localization error (FLE) and is thus to a large extent independent of the particular object being registered. Thus, it should be possible to predict the clinical accuracy of marker-based systems on the basis of experimental measurements made with phantoms or previous patients. This paper presents two new expressions for estimating registration accuracy of such systems and points out a danger in using a traditional measure of registration accuracy. The new expressions represent fundamental theoretical results with regard to the relationship between localization error and registration error in rigid-body, point-based registration. Rigid-body, point-based registration is achieved by finding the rigid transformation that minimizes "fiducial registration error" (FRE), which is the root mean square distance between homologous fiducials after registration. Closed form solutions have been known since 1966. The expected value (FRE/sup 2/) depends on the number N of fiducials and expected squared value of FLE, (FLE/sup 2/), but in 1979 it was shown that (FRE/sup 2/) is approximately independent of the fiducial configuration C. The importance of this surprising result seems not yet to have been appreciated by the registration community: Poor registrations caused by poor fiducial configurations may appear to be good due to a small FRE value. A more critical and direct measure of registration error is the "target registration error" (TRE), which is the distance between homologous points other than the centroids of fiducials. Efforts to characterize its behavior have been made since 1989. Published numerical simulations have shown that (TRE/sup 2/) is roughly proportional to (FLE/sup 2/)/N and, unlike (FRE/sup 2/), does depend in some way on C. Thus, FRE, which is often used as feedback to the surgeon using a point-based guidance system, is in fact an unreliable indicator of registration-accuracy. In this work the authors derive approximate expressions for (TRE/sup 2/), and for the expected squared alignment error of an individual fiducial. They validate both approximations through numerical simulations. The former expression can be used to provide reliable feedback to the surgeon during surgery and to guide the placement of markers before surgery, or at least to warn the surgeon of potentially dangerous fiducial placements; the latter expression leads to a surprising conclusion: Expected registration accuracy (TRE) is worst near the fiducials that are most closely aligned! This revelation should be of particular concern to surgeons who may at present be relying on fiducial alignment as an indicator of the accuracy of their point-based guidance systems.

设计用于神经外科、髋关节手术和脊柱手术以及其它相对刚体解剖结构的引导系统可以采用刚体变换实现图像配准。这些系统通常依赖点配准来确定变换，许多此类系统使用附着的基准标记来建立精确的配准基准点，这些点通过某种基准定位过程确定。准确性对这些系统至关重要，同样重要的是对准确性水平的了解。基于标记的系统（特别是那些标记植入骨中的系统）的一个优势是配准误差仅取决于基准定位误差（FLE），因此在很大程度上独立于被配准的特定对象。因此，应该能够根据使用模体或既往患者进行的实验测量来预测基于标记的系统的临床准确性。本文提出了两个新表达式来估计此类系统的配准准确性，并指出了使用传统配准准确性度量的危险性。新表达式代表了关于刚体点配准中定位误差与配准误差之间关系的基本理论结果。刚体点配准通过找到使“基准配准误差”（FRE）最小化的刚体变换来实现，该误差是配准后对应基准点之间的均方根距离。闭式解自1966年以来已知。期望值（FRE/sup 2/）取决于基准数量N和FLE的期望平方值（FLE/sup 2/），但在1979年发现（FRE/sup 2/）近似独立于基准配置C。这个令人惊讶的结果的重要性似乎尚未被配准学界充分认识到：由不良基准配置导致的差配准可能因为小的FRE值而显得良好。一个更关键和直接的配准误差度量是“目标配准误差”（TRE），即非基准质心的对应点之间的距离。自1989年以来一直努力描述其行为。已发表的数值模拟表明，（TRE/sup 2/）大致与（FLE/sup 2/）/N成正比，并且与（FRE/sup 2/）不同，它在某种程度上依赖于C。因此，经常用作基于点的引导系统外科医生反馈的FRE实际上是一个不可靠的配准准确性指标。在这项工作中，作者推导了（TRE/sup 2/）和单个基准的预期平方对准误差的近似表达式。他们通过数值模拟验证了这两个近似。前者表达式可用于在手术期间向外科医生提供可靠的反馈，并在术前指导标记放置，或至少警告外科医生潜在危险的基准放置；后者表达式得出一个令人惊讶的结论：预期配准准确性（TRE）在最接近对准的基准附近最差！这一发现对于目前可能依赖基准对准作为基于点的引导系统准确性指标的外科医生尤其值得关注。

A method for the automatic measurement of femur length in fetal ultrasound images is presented. Fetal femur length measurements are used to estimate gestational age by comparing the measurement to a typical growth chart. Using a real-time ultrasound system, sonographers currently indicate the femur endpoints on the ultrasound display station with a mouse-like device. The measurements are subjectiv...

中文摘要翻译待生成

The new spiral multislice computed tomography (CT) scanners and the significant increase in rotation speed offer great potential for cardiac imaging with X-ray CT. The authors have therefore developed the dedicated cardiac reconstruction algorithms 180/spl deg/ multislice cardio interpolation (MCI) and 180/spl deg/ multislice cardio delta (MCD) and here offer further details and validation. The al...

新型螺旋多层计算机断层扫描（CT）扫描仪以及旋转速度的显著提高为X射线CT心脏成像提供了巨大潜力。因此，作者开发了专门的心脏重建算法180°多层心脏插值（MCI）和180°多层心脏delta（MCD），并在此提供进一步的细节和验证。该算...

Glaucoma is a chronic eye disease that leads to irreversible vision loss. The cup to disc ratio (CDR) plays an important role in the screening and diagnosis of glaucoma. Thus, the accurate and automatic segmentation of optic disc (OD) and optic cup (OC) from fundus images is a fundamental task. Most existing methods segment them separately, and rely on hand-crafted visual feature from fundus images. In this paper, we propose a deep learning architecture, named M-Net, which solves the OD and OC segmentation jointly in a one-stage multi-label system. The proposed M-Net mainly consists of multi-scale input layer, U-shape convolutional network, side-output layer, and multi-label loss function. The multi-scale input layer constructs an image pyramid to achieve multiple level receptive field sizes. The U-shape convolutional network is employed as the main body network structure to learn the rich hierarchical representation, while the side-output layer acts as an early classifier that produces a companion local prediction map for different scale layers. Finally, a multi-label loss function is proposed to generate the final segmentation map. For improving the segmentation performance further, we also introduce the polar transformation, which provides the representation of the original image in the polar coordinate system. The experiments show that our M-Net system achieves state-of-the-art OD and OC segmentation result on ORIGA data set. Simultaneously, the proposed method also obtains the satisfactory glaucoma screening performances with calculated CDR value on both ORIGA and SCES datasets.

青光眼是一种导致不可逆视力丧失的慢性眼病。杯盘比（CDR）在青光眼的筛查和诊断中起着重要作用。因此，从眼底图像中准确自动地分割视盘（OD）和视杯（OC）是一项基本任务。现有的大多数方法分别分割它们，并依赖于从眼底图像中手工设计的视觉特征。在本文中，我们提出了一种深度学习架构，名为M-Net，它在单阶段多标签系统中联合解决OD和OC分割问题。所提出的M-Net主要包括多尺度输入层、U形卷积网络、侧输出层和多标签损失函数。多尺度输入层构建图像金字塔以实现多个水平的感受野尺寸。U形卷积网络被用作主体网络结构来学习丰富的层次表示，而侧输出层作为一个早期分类器，为不同尺度层产生伴随的局部预测图。最后，提出多标签损失函数以生成最终分割图。为了进一步提高分割性能，我们还引入了极坐标变换，它提供了原始图像在极坐标系中的表示。实验表明，我们的M-Net系统在ORIGA数据集上达到了最先进的OD和OC分割结果。同时，所提出的方法在ORIGA和SCES数据集上计算出的CDR值也获得了令人满意的青光眼筛查性能。

Current computed tomography (CT) technology allows for near isotropic, submillimeter resolution acquisition of the complete chest in a single breath hold. These thin-slice chest scans have become indispensable in thoracic radiology, but have also substantially increased the data load for radiologists. Automating the analysis of such data is, therefore, a necessity and this has created a rapidly developing research area in medical imaging. This paper presents a review of the literature on computer analysis of the lungs in CT scans and addresses segmentation of various pulmonary structures, registration of chest scans, and applications aimed at detection, classification and quantification of chest abnormalities. In addition, research trends and challenges are identified and directions for future research are discussed.

当前的计算机断层扫描（CT）技术允许在单次屏气内获取接近各向同性、亚毫米分辨率的完整胸部图像。这些薄层胸部扫描在胸部放射学中变得不可或缺，但也大大增加了放射科医生的数据负担。因此，自动化分析此类数据成为必要，这催生了一个快速发展的医学影像研究领域。本文综述了CT扫描中肺部计算机分析的文献，涵盖了各种肺部结构的分割、胸部扫描的配准，以及旨在检测、分类和量化胸部异常的应用。此外，还指出了研究趋势和挑战，并讨论了未来的研究方向。

The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to variable size of vessels, relatively low contrast, and potential presence of pathologies like microaneurysms and hemorrhages. Many algorithms, both unsupervised and supervised, have been proposed for this purpose in the past. We propose a supervised segmentation technique that uses a deep neural network trained on a large (up to 400 \thinspace000) sample of examples preprocessed with global contrast normalization, zero-phase whitening, and augmented using geometric transformations and gamma corrections. Several variants of the method are considered, including structured prediction, where a network classifies multiple pixels simultaneously. When applied to standard benchmarks of fundus imaging, the DRIVE, STARE, and CHASE databases, the networks significantly outperform the previous algorithms on the area under ROC curve measure (up to > 0.99) and accuracy of classification (up to > 0.97). The method is also resistant to the phenomenon of central vessel reflex, sensitive in detection of fine vessels ( sensitivity > 0.87), and fares well on pathological cases.

人眼血管网络的状态是眼科诊断中的一个重要因素。由于血管大小不一、对比度相对较低以及可能存在微动脉瘤和出血等病理特征，眼底成像中的血管分割是一项具有挑战性的任务。过去已提出许多算法，包括无监督和有监督方法。我们提出了一种有监督分割技术，该技术使用在大量（多达400000个）样本上训练的深度神经网络，这些样本经过全局对比度归一化、零相位白化预处理，并通过几何变换和伽马校正进行数据增强。考虑了该方法的几种变体，包括结构化预测，其中网络同时分类多个像素。当应用于眼底成像的标准基准（DRIVE、STARE和CHASE数据库）时，该网络在ROC曲线下面积（高达>0.99）和分类准确性（高达>0.97）上显著优于先前的算法。该方法还对中心血管反射现象具有鲁棒性，对细小血管检测敏感（灵敏度>0.87），并且在病理情况下表现良好。

Artifacts can result when reconstructing a dynamic image sequence from inconsistent, as well as insufficient and truncated, cone beam single photon emission computed tomography (SPECT) projection data acquired by a slowly rotating gantry. The artifacts can lead to biases in kinetic model parameters estimated from time-activity curves generated by overlaying volumes of interest on the images. Howev...

当从由慢速旋转机架采集的不一致、不充分且截断的锥束单光子发射计算机断层扫描（SPECT）投影数据重建动态图像序列时，可能会产生伪影。这些伪影可能导致通过将感兴趣区域叠加在图像上生成的时间-活动曲线估计的动力学模型参数出现偏差。

We investigate an approach based on support vector machines (SVMs) for detection of microcalcification (MC) clusters in digital mammograms, and propose a successive enhancement learning scheme for improved performance. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. We formulate MC detection as a supervised-learning problem and apply SVM to develop the detection algorithm. We use the SVM to detect at each location in the image whether an MC is present or not. We tested the proposed method using a database of 76 clinical mammograms containing 1120 MCs. We use free-response receiver operating characteristic curves to evaluate detection performance, and compare the proposed algorithm with several existing methods. In our experiments, the proposed SVM framework outperformed all the other methods tested. In particular, a sensitivity as high as 94% was achieved by the SVM method at an error rate of one false-positive cluster per image. The ability of SVM to outperform several well-known methods developed for the widely studied problem of MC detection suggests that SVM is a promising technique for object detection in a medical imaging application.

我们研究了一种基于支持向量机（SVM）的方法用于数字乳腺X线图像中微钙化（MC）簇的检测，并提出了一种连续增强学习方案以提高性能。SVM是一种基于结构风险最小化原理的机器学习方法，在处理训练集之外的数据时表现良好。我们将MC检测表述为一个监督学习问题，并应用SVM开发检测算法。我们使用SVM来检测图像中每个位置是否存在MC。我们使用包含1120个MC的76例临床乳腺X线图像数据库测试了所提出的方法。我们使用自由响应接收者操作特征曲线来评估检测性能，并将所提出的算法与几种现有方法进行比较。在我们的实验中，所提出的SVM框架优于所有其他测试方法。特别是，SVM方法在每个图像一个假阳性簇的错误率下达到了高达94%的灵敏度。SVM能够优于几种为广泛研究的MC检测问题而开发的知名方法，表明SVM是医学成像应用中物体检测的一种有前景的技术。

Describes a fully automated method for model-based tissue classification of magnetic resonance (MR) images of the brain. The method interleaves classification with estimation of the model parameters, improving the classification at each iteration. The algorithm is able to segment single- and multi-spectral MR images, corrects for MR signal inhomogeneities, and incorporates contextual information by means of Markov random Fields (MRF's). A digital brain atlas containing prior expectations about the spatial location of tissue classes is used to initialize the algorithm. This makes the method fully automated and therefore it provides objective and reproducible segmentations. The authors have validated the technique on simulated as well as on real MR images of the brain.

描述了一种完全自动化的方法，用于对脑部磁共振（MR）图像进行基于模型的组织分类。该方法将分类与模型参数估计交错进行，每次迭代均改进分类。该算法能够分割单谱和多谱MR图像，校正MR信号不均匀性，并利用马尔可夫随机场（MRF）纳入上下文信息。使用包含组织类别空间位置先验知识的数字脑图谱来初始化算法。这使得该方法完全自动化，从而提供客观且可重复的分割结果。作者已在模拟和真实脑部MR图像上验证了该技术。

Over past several years, machine learning, or more generally artificial intelligence, has generated overwhelming research interest and attracted unprecedented public attention. As tomographic imaging researchers, we share the excitement from our imaging perspective [item 1) in the Appendix], and organized this special issue dedicated to the theme of “Machine learning for image reconstruction.” Thi...

在过去几年中，机器学习（或更广义的人工智能）引发了压倒性的研究兴趣，并吸引了前所未有的公众关注。作为断层成像研究人员，我们从成像角度分享了这一兴奋（见附录第1项），并组织了这期特刊，专注于“机器学习在图像重建中的应用”主题。本文...

Gastrointestinal (GI) tract examination with spiral/helical computed tomography (CT) is currently performed by slice-based inspection of axial images. CT colography is a recent advance which allows an intraluminal visualization of the colon, similar to endoscopy. Various rendering algorithms have been developed with promising results, however navigation through the complex, tortuous anatomy of the...

目前，螺旋/螺旋计算机断层扫描（CT）检查胃肠道是通过逐层检查轴向图像进行的。CT结肠成像是一项最新进展，它允许像内窥镜检查一样对结肠进行腔内可视化。已经开发了各种渲染算法，并取得了有希望的结果，然而在复杂、曲折的解剖结构中导航……

Examines the use of the algebraic reconstruction technique (ART) and related techniques to reconstruct 3-D objects from a relatively sparse set of cone-beam projections. Although ART has been widely used for cone-beam reconstruction of high-contrast objects, e.g., in computed angiography, the work presented here explores the more challenging low-contrast case which represents a little-investigated...

研究了使用代数重建技术（ART）及相关技术从相对稀疏的锥束投影中重建三维物体。尽管ART已广泛应用于高对比度物体的锥束重建，例如在计算机断层血管造影中，但本文探讨了更具挑战性的低对比度情况，这是一个研究较少的领域。

The detection of microaneurysms in digital color fundus photographs is a critical first step in automated screening for diabetic retinopathy (DR), a common complication of diabetes. To accomplish this detection numerous methods have been published in the past but none of these was compared with each other on the same data. In this work we present the results of the first international microaneurys...

数字彩色眼底照片中微动脉瘤的检测是糖尿病视网膜病变（DR）自动化筛查的第一步，DR是糖尿病的常见并发症。过去已经发表了许多方法来实现这一检测，但没有一种方法在相同数据上进行比较。在这项工作中，我们展示了第一次国际微动脉瘤...

Staining and scanning of tissue samples for microscopic examination is fraught with undesirable color variations arising from differences in raw materials and manufacturing techniques of stain vendors, staining protocols of labs, and color responses of digital scanners. When comparing tissue samples, color normalization and stain separation of the tissue images can be helpful for both pathologists and software. Techniques that are used for natural images fail to utilize structural properties of stained tissue samples and produce undesirable color distortions. The stain concentration cannot be negative. Tissue samples are stained with only a few stains and most tissue regions are characterized by at most one effective stain. We model these physical phenomena that define the tissue structure by first decomposing images in an unsupervised manner into stain density maps that are sparse and non-negative. For a given image, we combine its stain density maps with stain color basis of a pathologist-preferred target image, thus altering only its color while preserving its structure described by the maps. Stain density correlation with ground truth and preference by pathologists were higher for images normalized using our method when compared to other alternatives. We also propose a computationally faster extension of this technique for large whole-slide images that selects an appropriate patch sample instead of using the entire image to compute the stain color basis.

组织样本的染色和扫描用于显微镜检查时，由于染色剂原材料和生产工艺、实验室染色方案以及数字扫描仪颜色响应的差异，常常会出现不理想的颜色变化。在比较组织样本时，颜色归一化和染色分离对病理学家和软件都有帮助。用于自然图像的技术未能利用染色组织样本的结构特性，从而产生不理想的颜色失真。染色浓度不能为负。组织样本仅用少数几种染色剂染色，且大多数组织区域至多由一种有效染色剂表征。我们通过首先以无监督方式将图像分解为稀疏且非负的染色密度图，来模拟这些定义组织结构的物理现象。对于给定图像，我们将其染色密度图与病理学家偏好的目标图像的染色颜色基相结合，从而仅改变其颜色，同时保留由密度图描述的结构。与其他方法相比，使用我们的方法归一化的图像在染色密度与真实值的相关性以及病理学家的偏好方面均更高。我们还提出了一种计算上更快的扩展技术，用于大型全切片图像，该技术选择合适的斑块样本而非使用整个图像来计算染色颜色基。

Mixture models are commonly used in the statistical segmentation of images. For example, they can be used for the segmentation of structural medical images into different matter types, or of statistical parametric maps into activating and nonactivating brain regions in functional imaging. Spatial mixture models have been developed to augment histogram information with spatial regularization using ...

混合模型常用于图像的统计分割。例如，它们可用于将结构医学图像分割成不同的物质类型，或将统计参数图分割为功能成像中的激活和非激活脑区域。空间混合模型已被开发出来，通过空间正则化增强直方图信息，使用...

Segmentation of pulmonary X-ray computed tomography (CT) images is a precursor to most pulmonary image analysis applications. This paper presents a fully automatic method for identifying the lungs in three-dimensional (3-D) pulmonary X-ray CT images. The method has three main steps. First, the lung region is extracted from the CT images by gray-level thresholding. Then, the left and right lungs are separated by identifying the anterior and posterior junctions by dynamic programming. Finally, a sequence of morphological operations is used to smooth the irregular boundary along the mediastinum in order to obtain results consistent with these obtained by manual analysis, in which only the most central pulmonary arteries are excluded from the lung region. The method has been tested by processing 3-D CT data sets from eight normal subjects, each imaged three times at biweekly intervals with lungs at 90% vital capacity. The authors present results by comparing their automatic method to manually traced borders from two image analysts. Averaged over all volumes, the root mean square difference between the computer and human analysis is 0.8 pixels (0.54 mm). The mean intrasubject change in tissue content over the three scans was 2.75%/spl plusmn/2.29% (mean/spl plusmn/standard deviation).

肺X射线计算机断层扫描(CT)图像的分割是大多数肺部图像分析应用的前置步骤。本文提出了一种全自动方法，用于在三维（3D）肺部X射线CT图像中识别肺。该方法包括三个主要步骤。首先，通过灰度阈值从CT图像中提取肺区域。然后，通过动态规划识别前后连接处，将左肺和右肺分离。最后，使用一系列形态学操作沿着纵隔平滑不规则边界，以获得与手动分析一致的结果，其中只有最中心的肺动脉被排除在肺区域之外。该方法通过处理来自八名正常受试者的三维CT数据集进行了测试，每名受试者每隔两周以90%肺活量成像三次。作者通过将其自动方法与两位图像分析师手动追踪的边界进行比较来展示结果。在所有体积上平均，计算机与人工分析之间的均方根差为0.8像素（0.54毫米）。三次扫描中组织含量的平均受试者内变化为2.75%±2.29%（均值±标准差）。

The structure of microvasculature cannot be resolved using standard clinical ultrasound (US) imaging frequencies due to the fundamental diffraction limit of US waves. In this work, we use a standard clinical US system to perform in vivo sub-diffraction imaging on a CD1, female mouse aged eight weeks by localizing isolated US signals from microbubbles flowing within the ear microvasculature, and compare our results to optical microscopy. Furthermore, we develop a new technique to map blood velocity at super-resolution by tracking individual bubbles through the vasculature. Resolution is improved from a measured lateral and axial resolution of 112 μm and 94 μm respectively in original US data, to super-resolved images of microvasculature where vessel features as fine as 19 μm are clearly visualized. Velocity maps clearly distinguish opposing flow direction and separated speed distributions in adjacent vessels, thereby enabling further differentiation between vessels otherwise not spatially separated in the image. This technique overcomes the diffraction limit to provide a noninvasive means of imaging the microvasculature at super-resolution, to depths of many centimeters. In the future, this method could noninvasively image pathological or therapeutic changes in the microvasculature at centimeter depths in vivo.

由于超声波的固有衍射极限，标准临床超声成像频率无法解析微血管结构。在本工作中，我们使用标准临床超声系统，通过定位来自流经耳微血管的微泡的孤立超声信号，对一只八周龄的CD1雌性小鼠进行体内亚衍射成像，并将我们的结果与光学显微镜进行比较。此外，我们开发了一种新技术，通过追踪血管中的单个气泡来绘制超分辨血流速度。分辨率从原始超声数据中测量的横向112 μm和轴向94 μm提高到微血管超分辨图像，其中可清晰观察到细至19 μm的血管特征。速度图清晰区分相邻血管中的相反流动方向和分离的速度分布，从而进一步区分图像中原本未空间分离的血管。该技术突破了衍射极限，提供了一种非侵入性的超分辨微血管成像方法，深度可达数厘米。未来，该方法可无创地成像体内厘米深度处微血管的病理或治疗变化。

Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e., segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi-structure ultrasound segmentation dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder-based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer’s ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images.

从二维超声心动图像中勾画心脏结构是临床诊断中常见任务。过去几十年，该任务的自动化一直是研究热点。本文评估了最先进的编码器-解码器深度卷积神经网络方法在二维超声心动图像评估上的表现，即分割心脏结构和估计临床指标，使用一个专门为此目标设计的数据集。因此，我们引入了心脏多结构超声分割数据集，这是目前最大且完全标注的公开数据集，用于超声心动评估。该数据集包含来自500名患者的双腔和四腔采集，由一位心脏病专家对整个数据集进行参考测量，并由三位心脏病专家对50名患者的子集进行测量。结果表明，基于编码器-解码器的架构优于最先进的非深度学习方法，并忠实再现了专家对左心室舒张末期和收缩末期容积的分析，平均相关性达0.95，绝对平均误差为9.5毫升。关于左心室的射血分数，结果较为对比，平均相关系数为0.80，绝对平均误差为5.6%。尽管这些结果低于观察者间评分，但略优于观察者内评分。基于这一观察，定义了改进方向，为二维超声心动图像的准确和全自动分析打开了大门。

A new class of acquisition schemes for helical cone-beam computed tomography (CB-CT) scanning is introduced, and their effect on the reconstruction methods is analyzed. These acquisition schemes are based on a new detector shape that is bounded by the helix. It will be shown that the data acquired with these schemes are compatible with exact reconstruction methods, and the adaptation of exact reco...

介绍了一类用于螺旋锥束计算机断层扫描（CB-CT）的新型采集方案，并分析了它们对重建方法的影响。这些采集方案基于一种由螺旋线界定的新型探测器形状。将证明这些方案采集的数据与精确重建方法兼容，并且精确重建方法的适应...