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The current trend in attenuation correction for single photon emission computed tomography (SPECT) is to measure and reconstruct the attenuation coefficient map using a transmission scan, performed either sequentially or simultaneously with the emission scan. This approach requires dedicated hardware and increases the cost (and in some cases the scanning time) required to produce a clinical SPECT ...

当前单光子发射计算机断层扫描（SPECT）衰减校正的趋势是使用透射扫描测量和重建衰减系数图，该扫描可与发射扫描顺序或同时进行。这种方法需要专用硬件，并增加了临床SPECT...

The magnetic particle imaging (MPI) imaging process is a new method of medical imaging with great promise. In this paper we derive the 1-D MPI signal, resolution, bandwidth requirements, signal-to-noise ratio (SNR), specific absorption rate, and slew rate limitations. We conclude with experimental data measuring the point spread function for commercially available SPIO nanoparticles and a demonstr...

磁粒子成像（MPI）成像过程是一种前景广阔的新型医学成像方法。本文推导了一维MPI信号、分辨率、带宽要求、信噪比（SNR）、比吸收率和压摆率限制。最后，我们通过实验数据测量了市售SPIO纳米颗粒的点扩散函数，并展示了一个演示。

We present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.

我们提出了一种新颖的模糊分割算法，用于磁共振成像（MRI）数据的模糊分割和强度不均匀性估计。MRI强度不均匀性可归因于射频线圈的缺陷或与采集序列相关的问题。结果是在图像上产生缓慢变化的阴影伪影，这可能导致基于强度的传统分类产生误差。我们的算法通过修改标准模糊C均值（FCM）算法的目标函数来补偿这种不均匀性，并允许像素（体素）的标记受其邻近邻域标记的影响。邻域效应作为正则化器，使解偏向于分段均匀的标记。这种正则化对于分割受椒盐噪声污染的扫描图像非常有用。给出了合成图像和MR数据的实验结果，以证明所提算法的有效性和效率。

We present a new method for automatic segmentation of heterogeneous image data that takes a step toward bridging the gap between bottom-up affinity-based segmentation methods and top-down generative model based approaches. The main contribution of the paper is a Bayesian formulation for incorporating soft model assignments into the calculation of affinities, which are conventionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm, and apply the technique to the task of detecting and segmenting brain tumor and edema in multichannel magnetic resonance (MR) volumes. The computationally efficient method runs orders of magnitude faster than current state-of-the-art techniques giving comparable or improved results. Our quantitative results indicate the benefit of incorporating model-aware affinities into the segmentation process for the difficult case of glioblastoma multiforme brain tumor.

我们提出了一种新的异质图像数据自动分割方法，该方法旨在弥合基于底层的亲和度分割方法与基于顶层的生成模型方法之间的差距。本文的主要贡献在于提出了一种贝叶斯公式，将软模型分配纳入亲和度的计算中，而传统上亲和度是无模型的。我们将得到的模型感知亲和度集成到基于加权聚合算法的多级分割中，并将该技术应用于多通道磁共振（MR）体积中脑肿瘤和水肿的检测与分割任务。该计算方法高效，比当前最先进的技术快几个数量级，同时结果相当或更优。我们的定量结果表明，在分割过程中纳入模型感知亲和度对处理多形性胶质母细胞瘤脑肿瘤这一困难病例是有益的。

The continuous development and extensive use of computed tomography (CT) in medical practice has raised a public concern over the associated radiation dose to the patient. Reducing the radiation dose may lead to increased noise and artifacts, which can adversely affect the radiologists’ judgment and confidence. Hence, advanced image reconstruction from low-dose CT data is needed to improve the diagnostic performance, which is a challenging problem due to its ill-posed nature. Over the past years, various low-dose CT methods have produced impressive results. However, most of the algorithms developed for this application, including the recently popularized deep learning techniques, aim for minimizing the mean-squared error (MSE) between a denoised CT image and the ground truth under generic penalties. Although the peak signal-to-noise ratio is improved, MSE- or weighted-MSE-based methods can compromise the visibility of important structural details after aggressive denoising. This paper introduces a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein distance is a key concept of the optimal transport theory and promises to improve the performance of GAN. The perceptual loss suppresses noise by comparing the perceptual features of a denoised output against those of the ground truth in an established feature space, while the GAN focuses more on migrating the data noise distribution from strong to weak statistically. Therefore, our proposed method transfers our knowledge of visual perception to the image denoising task and is capable of not only reducing the image noise level but also trying to keep the critical information at the same time. Promising results have been obtained in our experiments with clinical CT images.

计算机断层扫描（CT）在医疗实践中的不断发展和广泛应用引起了公众对患者所受辐射剂量的关注。降低辐射剂量可能导致噪声和伪影增加，进而影响放射科医生的判断和信心。因此，需要从低剂量CT数据中进行先进的图像重建以提高诊断性能，但由于其不适定性，这是一个具有挑战性的问题。在过去几年中，各种低剂量CT方法取得了令人印象深刻的结果。然而，大多数针对此应用开发的算法，包括最近流行的深度学习技术，都旨在最小化去噪CT图像与真值之间的均方误差（MSE），并采用通用惩罚项。尽管峰值信噪比有所提高，但基于MSE或加权MSE的方法在积极去噪后可能会损害重要结构细节的可视性。本文介绍了一种基于生成对抗网络（GAN）的新型CT图像去噪方法，该方法结合了Wasserstein距离和感知相似性。Wasserstein距离是最优传输理论中的一个关键概念，有望提升GAN的性能。感知损失通过在已建立的特征空间中比较去噪输出与真值的感知特征来抑制噪声，而GAN则更多地关注在统计上将数据噪声分布从强迁移到弱。因此，我们提出的方法将视觉感知知识转移到图像去噪任务中，不仅能够降低图像噪声水平，同时还能尝试保留关键信息。在临床CT图像实验中取得了令人满意的结果。

Proliferative diabetic retinopathy is a rare condition likely to lead to severe visual impairment. It is characterized by the development of abnormal new retinal vessels. We describe a method for automatically detecting new vessels on the optic disc using retinal photography. Vessel-like candidate segments are first detected using a method based on watershed lines and ridge strength measurement. F...

增殖性糖尿病视网膜病变是一种罕见的疾病，可能导致严重的视力损伤。其特征是异常新生视网膜血管的生成。我们描述了一种使用视网膜摄影自动检测视盘上新血管的方法。首先，基于分水岭线和脊强度测量的方法检测血管样候选段。

Blood vessels usually have poor local contrast, and the application of existing edge detection algorithms yield results which are not satisfactory. An operator for feature extraction based on the optical and spatial properties of objects to be recognized is introduced. The gray-level profile of the cross section of a blood vessel is approximated by a Gaussian-shaped curve. The concept of matched filter detection of signals is used to detect piecewise linear segments of blood vessels in these images. Twelve different templates that are used to search for vessel segments along all possible directions are constructed. Various issues related to the implementation of these matched filters are discussed. The results are compared to those obtained with other methods. >

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In this paper, we extend a previously reported intensity-based nonrigid registration algorithm by using a novel regularization term to constrain the deformation. Global motion is modeled by a rigid transformation while local motion is described by a free-form deformation based on B-splines. An information theoretic measure, normalized mutual information, is used as an intensity-based image similarity measure. Registration is performed by searching for the deformation that minimizes a cost function consisting of a weighted combination of the image similarity measure and a regularization term. The novel regularization term is a local volume-preservation (incompressibility) constraint, which is motivated by the assumption that soft tissue is incompressible for small deformations and short time periods. The incompressibility constraint is implemented by penalizing deviations of the Jacobian determinant of the deformation from unity. We apply the nonrigid registration algorithm with and without the incompressibility constraint to precontrast and postcontrast magnetic resonance (MR) breast images from 17 patients. Without using a constraint, the volume of contrast-enhancing lesions decreases by 1%-78% (mean 26%). Image improvement (motion artifact reduction) obtained using the new constraint is compared with that obtained using a smoothness constraint based on the bending energy of the coordinate grid by blinded visual assessment of maximum intensity projections of subtraction images. For both constraints, volume preservation improves, and motion artifact correction worsens, as the weight of the constraint penalty term increases. For a given volume change of the contrast-enhancing lesions (2% of the original volume), the incompressibility constraint reduces motion artifacts better than or equal to the smoothness constraint in 13 out of 17 cases (better in 9, equal in 4, worse in 4). The preliminary results suggest that incorporation of the incompressibility regularization term improves intensity-based free-form nonrigid registration of contrast-enhanced MR breast images by greatly reducing the problem of shrinkage of contrast-enhancing structures while simultaneously allowing motion artifacts to be substantially reduced.

在本文中，我们通过使用一种新的正则化项来约束变形，对先前报道的基于强度的非刚性配准算法进行了扩展。全局运动通过刚性变换建模，而局部运动则通过基于B样条的自由形变来描述。使用基于信息论的度量——归一化互信息，作为基于强度的图像相似性度量。配准通过搜索使代价函数最小化的变形来实现，该代价函数由图像相似性度量和正则化项的加权组合构成。新的正则化项是局部体积保持（不可压缩性）约束，其动机是假设软组织在小变形和短时间内是不可压缩的。不可压缩性约束通过惩罚变形雅可比行列式与1的偏差来实现。我们将带有和不带有不可压缩性约束的非刚性配准算法应用于17名患者的增强前和增强后磁共振（MR）乳腺图像。在没有约束的情况下，对比增强病变的体积减少了1%至78%（平均26%）。通过盲法视觉评估减影图像的最大强度投影，将使用新约束获得的图像改善（运动伪影减少）与使用基于坐标网格弯曲能量的平滑约束获得的改善进行比较。对于两种约束，随着约束惩罚项权重的增加，体积保持效果改善，而运动伪影校正效果变差。对于给定的对比增强病变体积变化（原始体积的2%），在17例中有13例中，不可压缩性约束减少运动伪影的效果优于或等于平滑约束（其中9例更优，4例相等，4例更差）。初步结果表明，加入不可压缩性正则化项可以极大地减少对比增强结构的收缩问题，同时允许运动伪影显著减少，从而改善对比增强MR乳腺图像的基于强度的自由形变非刚性配准。

Reconstruction of images in electrical impedance tomography requires the solution of a nonlinear inverse problem on noisy data. This problem is typically ill-conditioned and requires either simplifying assumptions or regularization based on a priori knowledge. The authors present a reconstruction algorithm using neural network techniques which calculates a linear approximation of the inverse probl...

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A new iterative maximum-likelihood reconstruction algorithm for X-ray computed tomography is presented. The algorithm prevents beam hardening artifacts by incorporating a polychromatic acquisition model. The continuous spectrum of the X-ray tube is modeled as a number of discrete energies. The energy dependence of the attenuation is taken into account by decomposing the linear attenuation coefficient into a photoelectric component and a Compton scatter component. The relative weight of these components is constrained based on prior material assumptions. Excellent results are obtained for simulations and for phantom measurements. Beam-hardening artifacts are effectively eliminated. The relation with existing algorithms is discussed. The results confirm that improving the acquisition model assumed by the reconstruction algorithm results in reduced artifacts. Preliminary results indicate that metal artifact reduction is a very promising application for this new algorithm.

提出了一种用于X射线计算机断层扫描的新的迭代最大似然重建算法。该算法通过引入多色采集模型来防止射束硬化伪影。X射线管的连续频谱被建模为多个离散能量。衰减的能量依赖性通过将线性衰减系数分解为光电效应分量和康普顿散射分量来考虑。基于先前的材料假设，这些分量的相对权重受到约束。模拟和体模测量获得了优异的结果。射束硬化伪影被有效消除。讨论了与现有算法的关系。结果证实，改进重建算法假设的采集模型可减少伪影。初步结果表明，金属伪影减少是该新算法的一个非常有前景的应用。

We show an iterative reconstruction framework for diffraction ultrasound tomography. The use of broad-band illumination allows significant reduction of the number of projections compared to straight ray tomography. The proposed algorithm makes use of forward nonuniform fast Fourier transform (NUFFT) for iterative Fourier inversion. Incorporation of total variation regularization allows the reducti...

我们展示了一种用于衍射超声层析成像的迭代重建框架。与直射线层析成像相比，使用宽带照明可以显著减少投影数量。所提出的算法利用前向非均匀快速傅里叶变换（NUFFT）进行迭代傅里叶反演。结合总变差正则化可以减少...

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.

可变形图像配准是医学图像处理中的一项基本任务。其最重要的应用包括：1) 多模态融合，其中不同成像设备或协议获取的信息被融合以促进诊断和治疗规划；2) 纵向研究，其中研究时间上的结构或解剖变化；以及3) 用于研究正常解剖变异的人群建模和统计图谱。本文试图对可变形配准方法进行概述，重点关注该领域的最新进展。此外，还特别强调了应用于医学图像的技术。为了深入研究图像配准方法，本文识别并独立研究了其主要组成部分。以系统的方式介绍了最新的技术。本文的贡献在于以系统的方式提供了配准技术的广泛描述。

We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and two-dimensional Gabor wavelet transform responses taken at multiple scales. The Gabor wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE (Staal et al.,2004) and STARE (Hoover et al.,2000) databases of manually labeled images. On the DRIVE database, it achieves an area under the receiver operating characteristic curve of 0.9614, being slightly superior than that presented by state-of-the-art approaches. We are making our implementation available as open source MATLAB scripts for researchers interested in implementation details, evaluation, or development of methods

我们提出了一种自动分割视网膜图像中血管网络的方法。该方法通过基于像素的特征向量将每个图像像素分类为血管或非血管，从而生成分割结果。特征向量由像素的强度和多尺度二维Gabor小波变换响应组成。Gabor小波能够调谐到特定频率，从而在单一步骤中实现噪声滤波和血管增强。我们使用贝叶斯分类器，其类条件概率密度函数（似然）描述为高斯混合，能够快速分类，同时能够建模复杂的决策表面。概率分布基于从手动分割获得的标记像素训练集进行估计。该方法在公开可用的DRIVE (Staal et al., 2004) 和 STARE (Hoover et al., 2000) 手动标记图像数据库上进行了性能评估。在DRIVE数据库上，它实现了0.9614的受试者工作特征曲线下面积，略优于现有最先进的方法。我们以开源MATLAB脚本的形式提供我们的实现，供有兴趣的实现细节、评估或方法开发的研究人员使用。

Describes a new image processing technique for rapid analysis and visualization of tagged cardiac magnetic resonance (MR) images. The method is based on the use of isolated spectral peaks in spatial modulation of magnetization (SPAMM)-tagged magnetic resonance images. The authors call the calculated angle of the complex image corresponding to one of these peaks a harmonic phase (HARP) image and sh...

描述了一种新的图像处理技术，用于快速分析和可视化标记的心脏磁共振（MR）图像。该方法基于在空间磁化调制（SPAMM）标记的磁共振图像中使用孤立的频谱峰值。作者将对应于这些峰值之一的复图像的计算角度称为谐波相位（HARP）图像，并...

A new statistical approach to phase correction in NMR imaging is proposed. The proposed scheme consists of first-and zero-order phase corrections each by the inverse multiplication of estimated phase error. The first-order error is estimated by the phase of autocorrelation calculated from the complex valued phase distorted image while the zero-order correction factor is extracted from the histogra...

中文摘要翻译待生成

Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2 $\,\times\,$ 2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance ( $\sim 85.5\%$ ) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.

自动组织表征是计算机辅助诊断（CAD）系统用于间质性肺疾病（ILD）的最关键组成部分之一。尽管该领域已经进行了大量研究，但问题仍然具有挑战性。深度学习技术最近在多种计算机视觉问题中取得了令人印象深刻的成果，这提高了人们对其可能应用于此领域的期望。

In magnetic resonance imaging, magnetic field inhomogeneities cause distortions in images that are reconstructed by conventional fast Fourier transform (FFT) methods. Several noniterative image reconstruction methods are used currently to compensate for field inhomogeneities, but these methods assume that the field map that characterizes the off-resonance frequencies is spatially smooth. Recently,...

在磁共振成像中，磁场不均匀性会导致通过常规快速傅里叶变换（FFT）方法重建的图像出现畸变。目前，有几种非迭代图像重建方法用于补偿场不均匀性，但这些方法假设表征偏共振频率的场图在空间上是平滑的。最近，...

Algebraic reconstruction methods, such as the algebraic reconstruction technique (ART) and the related simultaneous ART (SART), reconstruct a two-dimensional (2-D) or three-dimensional (3-D) object from its X-ray projections. The algebraic methods have, in certain scenarios, many advantages over the more popular Filtered Backprojection approaches and have also recently been shown to perform well f...

代数重建方法，如代数重建技术(ART)及其相关的同步ART(SART)，通过X射线投影重建二维或三维物体。在某些情况下，代数方法比更流行的滤波反投影方法具有许多优点，并且最近也被证明在...中表现良好。

The clinical recognition of abnormal vascular tortuosity, or excessive bending, twisting, and winding, is important to the diagnosis of many diseases. Automated detection and quantitation of abnormal vascular tortuosity from three-dimensional (3-D) medical image data would, therefore, be of value. However, previous research has centered primarily upon two-dimensional (2-D) analysis of the special subset of vessels whose paths are normally close to straight. This report provides the first 3-D tortuosity analysis of clusters of vessels within the normally tortuous intracerebral circulation. We define three different clinical patterns of abnormal tortuosity. We extend into 3-D two tortuosity metrics previously reported as useful in analyzing 2-D images and describe a new metric that incorporates counts of minima of total curvature. We extract vessels from MRA data, map corresponding anatomical regions between sets of normal patients and patients with known pathology, and evaluate the three tortuosity metrics for ability to detect each type of abnormality within the region of interest. We conclude that the new tortuosity metric appears to be the most effective in detecting several types of abnormalities. However, one of the other metrics, based on a sum of curvature magnitudes, may be more effective in recognizing tightly coiled, "corkscrew" vessels associated with malignant tumors.

临床上识别异常血管迂曲度，即过度弯曲、扭转和缠绕，对于许多疾病的诊断非常重要。因此，从三维（3-D）医学图像数据中自动检测和量化异常血管迂曲度将具有重要价值。然而，先前的研究主要集中在二维（2-D）分析那些路径通常接近直线的特殊血管子集上。本报告首次对正常迂曲的脑内循环中的血管簇进行了三维迂曲度分析。我们定义了三种不同的异常迂曲度临床模式。我们将先前报道的在分析二维图像时有用的两个迂曲度指标扩展到三维，并描述了一个新指标，该指标包含了总曲率极小值的计数。我们从MRA数据中提取血管，在正常患者和已知病理患者组之间映射相应的解剖区域，并评估这三个迂曲度指标在感兴趣区域内检测每种异常类型的能力。我们得出结论，新的迂曲度指标在检测多种异常类型方面似乎最为有效。然而，基于曲率幅度之和的另一个指标可能在识别与恶性肿瘤相关的紧密盘绕的“开瓶器”状血管方面更为有效。

It is known that when a tissue is subjected to movements such as rotation, shearing, scaling, etc., changes in speckle patterns that result act as a noise source, often responsible for most of the displacement-estimate variance. From a modeling point of view, these changes can be thought of as resulting from two mechanisms: one is the motion of the speckles and the other, the alterations of their ...

已知当组织受到旋转、剪切、缩放等运动时，产生的散斑图案变化会作为噪声源，通常是位移估计方差的主要来源。从建模角度来看，这些变化可归因于两种机制：一是散斑的运动，二是其形状的改变。

Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the data acquisition process. In particular, we address the case where data are acquired using aggressive Cartesian undersampling. First, we show that when each 2-D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2-D compressed sensing approaches, such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed. Second, when reconstructing the frames of the sequences jointly, we demonstrate that CNNs can learn spatio-temporal correlations efficiently by combining convolution and data sharing approaches. We show that the proposed method consistently outperforms state-of-the-art methods and is capable of preserving anatomical structure more faithfully up to 11-fold undersampling. Moreover, reconstruction is very fast: each complete dynamic sequence can be reconstructed in less than 10 s and, for the 2-D case, each image frame can be reconstructed in 23 ms, enabling real-time applications.

受深度学习最新进展的启发，我们提出了一种框架，利用卷积神经网络（CNN）的深度级联，从未完全采样的数据中重建二维心脏磁共振（MR）图像的动态序列，以加速数据采集过程。具体来说，我们处理使用激进笛卡尔欠采样获取数据的情况。首先，我们展示了当每个二维图像帧独立重建时，所提方法在重建误差和重建速度方面优于最先进的二维压缩感知方法，如基于字典学习的MR图像重建。其次，当联合重建序列的帧时，我们证明了CNN可以通过组合卷积和数据共享方法有效地学习时空相关性。我们展示了所提方法始终优于最先进的方法，并且能够在高达11倍的欠采样下更忠实地保留解剖结构。此外，重建速度非常快：每个完整的动态序列可以在不到10秒内重建，对于二维情况，每个图像帧可以在23毫秒内重建，从而实现实时应用。

The mathematical problem for electrical impedance tomography (EIT) is a highly nonlinear ill-posed inverse problem requiring carefully designed reconstruction procedures to ensure reliable image generation. D-bar methods are based on a rigorous mathematical analysis and provide robust direct reconstructions by using a low-pass filtering of the associated nonlinear Fourier data. Similarly to low-pass filtering of linear Fourier data, only using low frequencies in the image recovery process results in blurred images lacking sharp features, such as clear organ boundaries. Convolutional neural networks provide a powerful framework for post-processing such convolved direct reconstructions. In this paper, we demonstrate that these CNN techniques lead to sharp and reliable reconstructions even for the highly nonlinear inverse problem of EIT. The network is trained on data sets of simulated examples and then applied to experimental data without the need to perform an additional transfer training. Results for absolute EIT images are presented using experimental EIT data from the ACT4 and KIT4 EIT systems.

电阻抗断层成像（EIT）的数学问题是一个高度非线性的不适定逆问题，需要精心设计的重建程序以确保可靠的图像生成。D-bar方法基于严谨的数学分析，通过使用相关非线性傅里叶数据的低通滤波，提供鲁棒的直接重建。与线性傅里叶数据的低通滤波类似，在图像恢复过程中仅使用低频会导致图像模糊，缺乏清晰的器官边界等锐利特征。卷积神经网络为后处理此类卷积直接重建提供了强大的框架。在本文中，我们证明了这些CNN技术即使在EIT的高度非线性逆问题中也能产生清晰可靠的重建。网络在模拟示例数据集上训练，然后应用于实验数据，无需进行额外的迁移训练。展示了使用来自ACT4和KIT4 EIT系统的实验EIT数据的绝对EIT图像结果。

A direct (noniterative) reconstruction algorithm for electrical impedance tomography in the two-dimensional (2-D), cross-sectional geometry is reviewed. New results of a reconstruction of a numerically simulated phantom chest are presented. The algorithm is based on the mathematical uniqueness proof by A.I. Nachman [1996] for the 2-D inverse conductivity problem. In this geometry, several of the c...

回顾了一种用于二维（2-D）横截面几何的电阻抗断层成像的直接（非迭代）重建算法。给出了数值模拟的胸部体模重建的新结果。该算法基于A.I. Nachman [1996] 对二维逆电导率问题的数学唯一性证明。在这种几何结构中，几个...

A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. Denoising is a crucial step to increase image quality and to improve the performance of all the tasks needed for quantitative imaging analysis. The method proposed in this paper is based on a 3-D optimized blockwise version of the nonlocal (NL)-means filter (Buades, , 2005). The NL-means filter uses the redundancy of information in the image under study to remove the noise. The performance of the NL-means filter has been already demonstrated for 2-D images, but reducing the computational burden is a critical aspect to extend the method to 3-D images. To overcome this problem, we propose improvements to reduce the computational complexity. These different improvements allow to drastically divide the computational time while preserving the performances of the NL-means filter. A fully automated and optimized version of the NL-means filter is then presented. Our contributions to the NL-means filter are: 1) an automatic tuning of the smoothing parameter; 2) a selection of the most relevant voxels; 3) a blockwise implementation; and 4) a parallelized computation. Quantitative validation was carried out on synthetic datasets generated with BrainWeb (Collins, , 1998). The results show that our optimized NL-means filter outperforms the classical implementation of the NL-means filter, as well as two other classical denoising methods [anisotropic diffusion (Perona and Malik, 1990)] and total variation minimization process (Rudin, , 1992) in terms of accuracy (measured by the peak signal-to-noise ratio) with low computation time. Finally, qualitative results on real data are presented.

图像恢复中的一个关键问题是在保持相关图像信息完整性的同时去除噪声。去噪是提高图像质量以及改善定量成像分析所需所有任务性能的关键步骤。本文提出的方法基于非局部均值（NL-means）滤波器（Buades等人，2005）的三维优化分块版本。NL-means滤波器利用研究图像中信息的冗余性来去除噪声。NL-means滤波器在二维图像上的性能已经得到验证，但减少计算负担是将该方法扩展到三维图像的关键问题。为了解决这一问题，我们提出了降低计算复杂度的改进措施。这些不同的改进措施能够在保持NL-means滤波器性能的同时大幅缩短计算时间。然后，我们提出了一个全自动且优化的NL-means滤波器版本。我们对NL-means滤波器的贡献包括：1）自动调整平滑参数；2）选择最相关的体素；3）分块实现；4）并行计算。定量验证在BrainWeb（Collins等人，1998）生成的合成数据集上进行。结果表明，与经典NL-means滤波器实现以及其他两种经典去噪方法（各向异性扩散（Perona和Malik，1990）和全变分最小化过程（Rudin等人，1992））相比，我们的优化NL-means滤波器在精度（以峰值信噪比衡量）方面表现更优，且计算时间更短。最后，给出了真实数据的定性结果。

Various deep learning methods have been proposed to segment breast lesions from ultrasound images. However, similar intensity distributions, variable tumor morphologies and blurred boundaries present challenges for breast lesions segmentation, especially for malignant tumors with irregular shapes. Considering the complexity of ultrasound images, we develop an adaptive attention U-net (AAU-net) to ...

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