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In the technique known as gridding, the data samples are weighted for sampling density and convolved with a finite kernel, then resampled on a grid preparatory to a fast Fourier transform. The authors compare the artifact introduced into the image for various convolving functions of different sizes, including the Kaiser-Bessel window and the zero-order prolate spheroidal wave function (PSWF). They also show a convolving function that improves upon the PSWF in some circumstances. >

在称为网格化的技术中，数据样本根据采样密度进行加权，并与有限核进行卷积，然后在网格上重新采样，以准备快速傅里叶变换。作者比较了不同大小的各种卷积函数引入图像的伪影，包括Kaiser-Bessel窗口和零阶扁长球面波函数（PSWF）。他们...

This paper presents a new nonrigid monomodality image registration algorithm based on $B$-splines. The deformation is described by a cubic $B$-spline field and found by minimizing the energy between a reference image and a deformed version of a floating image. To penalize noninvertible transformation, we propose two different constraints on the Jacobian of the transformation and its derivatives. T...

本文提出了一种基于B样条的非刚性单模态图像配准算法。变形由三次B样条场描述，并通过最小化参考图像与浮动图像变形版本之间的能量来求解。为了惩罚不可逆变换，我们提出了两种不同的约束条件，分别作用于变换的雅可比矩阵及其导数。...

The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically di...

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A partial-differential equations (PDE)-based system for detecting the boundary of skin lesions in digital clinical skin images is presented. The image is first preprocessed via contrast-enhancement and anisotropic diffusion. If the lesion is covered by hairs, a PDE-based continuous morphological filter that removes them is used as an additional preprocessing step. Following these steps, the skin l...

提出了一种基于偏微分方程（PDE）的系统，用于检测数字临床皮肤图像中皮肤病变的边界。图像首先通过对比度增强和各向异性扩散进行预处理。如果病变被毛发覆盖，则使用基于PDE的连续形态学滤波器将其移除作为额外的预处理步骤。在这些步骤之后，皮肤病变...

Noise is inherent to low-dose CT acquisition. We propose to train a convolutional neural network (CNN) jointly with an adversarial CNN to estimate routine-dose CT images from low-dose CT images and hence reduce noise. A generator CNN was trained to transform low-dose CT images into routine-dose CT images using voxelwise loss minimization. An adversarial discriminator CNN was simultaneously trained...

噪声是低剂量CT采集固有的。我们提出联合训练卷积神经网络（CNN）和对抗性CNN，从低剂量CT图像估计常规剂量CT图像，从而降低噪声。训练生成器CNN，通过体素级损失最小化将低剂量CT图像转换为常规剂量CT图像。同时训练对抗性判别器CNN...

P.J. Green has defined an OSL (one-step late) algorithm that retains the E-step of the EM algorithm (for image reconstruction in emission tomography) but provides an approximate solution to the M-step. Further modifications of the OSL algorithm guarantee convergence to the unique maximum of the log posterior function. Convergence is proved under a specific set of sufficient conditions. Several of ...

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This paper presents the adaptive axial interpolator (AAI), a novel spiral interpolation approach for multislice spiral computed tomography (CT) implemented in a clinical multislice CT scanner, the SOMATOM Volume Zoom (Siemens Medical Systems, Forchheim, Germany). The method works on parallel-beam data generated from the acquired fan-beam data by azimuthal rebinning. Spiral interpolation is perform...

本文介绍了自适应轴向插值器（AAI），这是一种用于多层螺旋计算机断层扫描（CT）的新型螺旋插值方法，已在临床多层螺旋CT扫描仪SOMATOM Volume Zoom（西门子医疗系统，德国福希海姆）中实现。该方法通过对采集的扇形束数据进行方位角重排，生成平行束数据。螺旋插值执行...

Ultrafast ultrasonic imaging is a rapidly developing field based on the unfocused transmission of plane or diverging ultrasound waves. This recent approach to ultrasound imaging leads to a large increase in raw ultrasound data available per acquisition. Bigger synchronous ultrasound imaging datasets can be exploited in order to strongly improve the discrimination between tissue and blood motion in the field of Doppler imaging. Here we propose a spatiotemporal singular value decomposition clutter rejection of ultrasonic data acquired at ultrafast frame rate. The singular value decomposition (SVD) takes benefits of the different features of tissue and blood motion in terms of spatiotemporal coherence and strongly outperforms conventional clutter rejection filters based on high pass temporal filtering. Whereas classical clutter filters operate on the temporal dimension only, SVD clutter filtering provides up to a four-dimensional approach (3D in space and 1D in time). We demonstrate the performance of SVD clutter filtering with a flow phantom study that showed an increased performance compared to other classical filters (better contrast to noise ratio with tissue motion between 1 and 10mm/s and axial blood flow as low as 2.6 mm/s). SVD clutter filtering revealed previously undetected blood flows such as microvascular networks or blood flows corrupted by significant tissue or probe motion artifacts. We report in vivo applications including small animal fUltrasound brain imaging (blood flow detection limit of 0.5 mm/s) and several clinical imaging cases, such as neonate brain imaging, liver or kidney Doppler imaging.

超快超声成像是一个快速发展的领域，基于平面或发散超声波的非聚焦发射。这种最新的超声成像方法使得每次采集可获得的原始超声数据大幅增加。可以利用更大的同步超声成像数据集来强有力地改善多普勒成像中组织和血液运动的区分。本文提出了一种对超快帧率采集的超声数据进行时空奇异值分解杂波抑制的方法。奇异值分解（SVD）利用了组织和血液运动在时空相干性方面的不同特征，并且比基于高通时间滤波的传统杂波抑制滤波器性能更强。经典杂波滤波器仅在时间维度上操作，而SVD杂波滤波提供了高达四维（三维空间和一维时间）的方法。我们通过流动体模研究展示了SVD杂波滤波的性能，与其他经典滤波器相比，其性能有所提高（在组织运动1-10毫米/秒和轴向血流低至2.6毫米/秒的情况下具有更好的对比度噪声比）。SVD杂波滤波揭示了先前未检测到的血流，例如微血管网络或受到显著组织或探头运动伪影影响的血流。我们报告了体内应用，包括小动物功能性超声脑成像（血流检测极限为0.5毫米/秒）以及几个临床成像案例，如新生儿脑成像、肝脏或肾脏多普勒成像。

Singular value decomposition of ultrafast imaging ultrasonic data sets has recently been shown to build a vector basis far more adapted to the discrimination of tissue and blood flow than the classical Fourier basis, improving by large factor clutter filtering and blood flow estimation. However, the question of optimally estimating the boundary between the tissue subspace and the blood flow subspa...

近期研究表明，超快成像超声数据集的奇异值分解能够构建一个比经典傅里叶基更适用于区分组织和血流的向量基，从而大幅提高杂波滤波和血流估计的效果。然而，如何最优地估计组织子空间与血流子空间之间的边界仍是一个问题...

A general reconstruction algorithm for magnetic resonance imaging (MRI) with gradients having arbitrary time dependence is presented. This method estimates spin density by calculating the weighted correlation of the observed free induction decay signal and the phase modulation function at each point. A theorem which states that this method can be derived from the conditions of linearity and shift ...

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This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

本文对磁共振（MR）图像中无监督统计脑组织分类技术进行了验证研究。评估了几种基于不同假设的图像模型，这些假设涉及强度分布模型、空间模型和类别数量。这些方法在已知分类真实标签的模拟数据上进行测试。添加了不同的噪声和强度不均匀性以模拟真实的成像条件。在分类过程之前或期间不考虑图像质量增强。这样，测试了方法的准确性及其对图像伪影的鲁棒性。还在真实数据上进行了分类，通过定量验证将方法的结果与专家手动分割的估计真实标签进行比较。使用不同的局部和全局度量来评估各种分类方法在图像标记和组织体积方面的有效性。结果表明，依赖强度和空间信息的方法对噪声和场不均匀性更鲁棒。我们还表明，即使考虑混合类别的方法优于仅考虑纯高斯类别的方法，部分体积也没有被完美建模。最后，我们展示了模拟数据的结果也可以推广到真实数据。

In this paper, we focus on the retrospective topology correction of surfaces. We propose a technique to accurately correct the spherical topology of cortical surfaces. Specifically, we construct a mapping from the original surface onto the sphere to detect topological defects as minimal nonhomeomorphic regions. The topology of each defect is then corrected by opening and sealing the surface along a set of nonseparating loops that are selected in a Bayesian framework. The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature. Applied to real data, our method provides topological corrections similar to those made by a trained operator

本文聚焦于表面的回顾性拓扑校正。我们提出了一种技术，用于精确校正皮层表面的球面拓扑。具体而言，我们构建从原始表面到球面的映射，以将拓扑缺陷检测为最小的非同胚区域。然后通过沿着在贝叶斯框架中选择的一组非分离环打开和密封表面来校正每个缺陷的拓扑。所提出的方法是一个完全自包含的拓扑校正算法，它基于磁共振成像（MRI）强度轮廓和预期的局部曲率来确定几何精确、拓扑正确的解。应用于实际数据时，我们的方法提供了与经过训练的操作员所做的相似的拓扑校正。

Fast magnetic resonance imaging slice acquisition techniques such as single shot fast spin echo are routinely used in the presence of uncontrollable motion. These techniques are widely used for fetal magnetic resonance imaging (MRI) and MRI of moving subjects and organs. Although high-quality slices are frequently acquired by these techniques, inter-slice motion leads to severe motion artifacts th...

快速磁共振成像切片采集技术，如单次激发快速自旋回波，常在存在不可控运动的情况下常规使用。这些技术广泛应用于胎儿磁共振成像（MRI）以及移动对象和器官的MRI。尽管这些技术通常能获取高质量切片，但切片间运动会导致严重的运动伪影...

Parallel transmission is a very promising candidate technology to mitigate the inevitable radio-frequency (RF) field inhomogeneity in magnetic resonance imaging at ultra-high field. For the first few years, pulse design utilizing this technique was expressed as a least squares problem with crude power regularizations aimed at controlling the specific absorption rate (SAR), hence the patient safety...

平行传输是一项非常有前景的候选技术，旨在减轻超高场磁共振成像中不可避免的射频场不均匀性。在最初几年，利用该技术的脉冲设计被表述为一个最小二乘问题，并采用粗略的功率正则化来控制特定吸收率（SAR），从而确保患者安全……

Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce motion artefacts and the effect of contrast washout, thus yielding better image quality. Different from parallel imaging-based fast MRI, which utilizes multiple ...

压缩感知磁共振成像(CS-MRI)能够实现快速采集，这对于许多临床应用非常理想。这不仅可以降低扫描成本、减轻患者负担，还可能减少运动伪影和对比剂冲刷效应，从而获得更好的图像质量。与基于并行成像的快速MRI不同，它利用多个...

We propose the boundary shift integral (BSI) as a measure of cerebral volume changes derived from registered repeat three-dimensional (3-D) magnetic resonance (MR) [3D MR] scans. The BSI determines the total volume through which the boundaries of a given cerebral structure have moved and, hence, the volume change, directly from voxel intensities. We found brain and ventricular BSI's correlated tig...

我们提出边界位移积分（BSI）作为一种从配准重复三维磁共振（3D MR）扫描中得出的脑体积变化测量方法。BSI直接通过体素强度确定给定脑结构边界移动的总体积，从而得出体积变化。我们发现脑和脑室的BSI与...

Trabecular bone (TB) is a complex quasi-random network of interconnected plates and rods. TB constantly remodels to adapt to the stresses to which it is subjected (Wolff's Law). In osteoporosis, this dynamic equilibrium between bone formation and resorption is perturbed, leading to bone loss and structural deterioration. Both bone loss and structural deterioration increase fracture risk. Bone's me...

松质骨是一个由相互连接的板状和杆状结构组成的复杂准随机网络。松质骨不断重塑以适应其所承受的应力（沃尔夫定律）。在骨质疏松症中，骨形成和骨吸收之间的动态平衡被打破，导致骨质流失和结构恶化。骨质流失和结构恶化都会增加骨折风险。骨的力学...

In contrast to acquisition-based noise reduction methods a postprocess based on anisotropic diffusion is proposed. Extensions of this technique support 3-D and multiecho magnetic resonance imaging (MRI), incorporating higher spatial and spectral dimensions. The procedure overcomes the major drawbacks of conventional filter methods, namely the blurring of object boundaries and the suppression of fi...

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Nonrigid registration of medical images is important for a number of applications such as the creation of population averages, atlas-based segmentation, or geometric correction of functional magnetic resonance imaging (IMRI) images to name a few. In recent years, a number of methods have been proposed to solve this problem, one class of which involves maximizing a mutual information (Ml)-based objective function over a regular grid of splines. This approach has produced good results but its computational complexity is proportional to the compliance of the transformation required to register the smallest structures in the image. Here, we propose a method that permits the spatial adaptation of the transformation's compliance. This spatial adaptation allows us to reduce the number of degrees of freedom in the overall transformation, thus speeding up the process and improving its convergence properties. To develop this method, we introduce several novelties: 1) we rely on radially symmetric basis functions rather than B-splines traditionally used to model the deformation field; 2) we propose a metric to identify regions that are poorly registered and over which the transformation needs to be improved; 3) we partition the global registration problem into several smaller ones; and 4) we introduce a new constraint scheme that allows us to produce transformations that are topologically correct. We compare the approach we propose to more traditional ones and show that our new algorithm compares favorably to those in current use.

医学图像的非刚性配准对于许多应用非常重要，例如创建人口平均值、基于图谱的分割或功能磁共振成像（fMRI）图像的几何校正等。近年来，已经提出了许多方法来解决这个问题，其中一类方法涉及在规则的样条网格上最大化基于互信息的目标函数。这种方法取得了良好的结果，但其计算复杂度与配准图像中最细微结构所需的变换的顺应性成正比。在这里，我们提出了一种允许空间自适应变换顺应性的方法。这种空间自适应允许我们减少整体变换的自由度，从而加快过程并改善其收敛特性。为了开发这种方法，我们引入了几个新颖之处：1）我们依赖径向对称基函数，而不是传统用于建模变形场的B样条；2）我们提出了一种度量来识别配准不良且需要改进变换的区域；3）我们将全局配准问题分解为几个小问题；4）我们引入了一种新的约束方案，使我们能够产生拓扑正确的变换。我们将我们提出的方法与更传统的方法进行了比较，并表明我们的新算法优于当前使用的方法。

Compressed sensing (CS) utilizes the sparsity of magnetic resonance (MR) images to enable accurate reconstruction from undersampled k-space data. Recent CS methods have employed analytical sparsifying transforms such as wavelets, curvelets, and finite differences. In this paper, we propose a novel framework for adaptively learning the sparsifying transform (dictionary), and reconstructing the image simultaneously from highly undersampled k-space data. The sparsity in this framework is enforced on overlapping image patches emphasizing local structure. Moreover, the dictionary is adapted to the particular image instance thereby favoring better sparsities and consequently much higher undersampling rates. The proposed alternating reconstruction algorithm learns the sparsifying dictionary, and uses it to remove aliasing and noise in one step, and subsequently restores and fills-in the k-space data in the other step. Numerical experiments are conducted on MR images and on real MR data of several anatomies with a variety of sampling schemes. The results demonstrate dramatic improvements on the order of 4-18 dB in reconstruction error and doubling of the acceptable undersampling factor using the proposed adaptive dictionary as compared to previous CS methods. These improvements persist over a wide range of practical data signal-to-noise ratios, without any parameter tuning.

压缩感知(CS)利用磁共振(MR)图像的稀疏性，从欠采样的k空间数据中实现精确重建。最近的CS方法采用了分析性稀疏变换，如小波、曲线波和有限差分。在本文中，我们提出了一种新颖的框架，用于自适应学习稀疏变换（字典），并同时从重度欠采样的k空间数据中重建图像。该框架中的稀疏性施加在重叠的图像块上，强调局部结构。此外，字典适应于特定的图像实例，从而有利于更好的稀疏性，进而实现更高的欠采样率。所提出的交替重建算法学习稀疏字典，并在一步中使用它去除混叠和噪声，然后在另一步中恢复并填充k空间数据。在MR图像和多种解剖结构的真实MR数据上进行了数值实验，采用了多种采样方案。结果表明，与之前的CS方法相比，使用所提出的自适应字典，重建误差显著提高了4-18 dB，可接受的欠采样因子翻倍。这些改进在广泛的实际数据信噪比范围内持续存在，无需任何参数调整。

The authors detail the design and implementation of HANDX, a model-based computer vision system used in the domain of medical image processing. Given a digitized hand radiograph, HANDX segments out specific bones and measures particular parameters of the bones, without requiring specific characterization of noise variations in background contrast and anatomical differences which arise from patient...

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The noise-equivalent count-rate (NEC) performance of a neuro-positron emission tomography (PET) scanner has been determined with and without interplane septa on uniform cylindrical phantoms of differing radii and in human studies to assess the optimum count rate conditions that realize the maximum gain. In the brain, the effective gain in NEC performance for three-dimensions (3-D) ranges from >5 a...

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A new approach is presented for elastic registration of medical images, and is applied to magnetic resonance images of the brain. Experimental results demonstrate very high accuracy in superposition of images from different subjects. There are two major novelties in the proposed algorithm. First, it uses an attribute vector, i.e., a set of geometric moment invariants (GMIs) that are defined on each voxel in an image and are calculated from the tissue maps, to reflect the underlying anatomy at different scales. The attribute vector, if rich enough, can distinguish between different parts of an image, which helps establish anatomical correspondences in the deformation procedure; it also helps reduce local minima, by reducing ambiguity in potential matches. This is a fundamental deviation of our method, referred to as the hierarchical attribute matching mechanism for elastic registration (HAMMER), from other volumetric deformation methods, which are typically based on maximizing image similarity. Second, in order to avoid being trapped by local minima, i.e., suboptimal poor matches, HAMMER uses a successive approximation of the energy function being optimized by lower dimensional smooth energy functions, which are constructed to have significantly fewer local minima. This is achieved by hierarchically selecting the driving features that have distinct attribute vectors, thus, drastically reducing ambiguity in finding correspondence. A number of experiments demonstrate that the proposed algorithm results in accurate superposition of image data from individuals with significant anatomical differences.

提出了一种用于医学图像弹性配准的新方法，并将其应用于脑部磁共振图像。实验结果表明，该方法在来自不同受试者的图像叠加中具有非常高的准确性。所提出的算法有两个主要创新点。首先，它使用属性向量，即一组定义在图像中每个体素上并从组织图中计算得到的几何矩不变量（GMIs），来反映不同尺度下的底层解剖结构。属性向量如果足够丰富，可以区分图像的不同部分，这有助于在变形过程中建立解剖对应关系；它还有助于通过减少潜在匹配中的模糊性来减少局部极小值。这是我们的方法（称为用于弹性配准的层次属性匹配机制，HAMMER）与其他通常基于最大化图像相似性的体积变形方法的根本区别。其次，为了避免陷入局部极小值（即次优的差匹配），HAMMER使用连续逼近被优化的能量函数，通过构造具有显著较少局部极小值的低维光滑能量函数来实现。这是通过层次性地选择具有独特属性向量的驱动特征来实现的，从而大幅减少寻找对应关系时的模糊性。多项实验表明，所提出的算法能够精确叠加具有显著解剖差异的个体的图像数据。

Microwave breast imaging (using electromagnetic waves of frequencies around 1 GHz) has mostly remained at the research level for the past decade, gaining little clinical acceptance. The major hurdles limiting patient use are both at the hardware level (challenges in collecting accurate and noncorrupted data) and software level (often plagued by unrealistic reconstruction times in the tens of hours). In this paper we report improvements that address both issues. First, the hardware is able to measure signals down to levels compatible with sub-centimeter image resolution while keeping an exam time under 2 min. Second, the software overcomes the enormous time burden and produces similarly accurate images in less than 20 min. The combination of the new hardware and software allows us to produce and report here the first clinical 3-D microwave tomographic images of the breast. Two clinical examples are selected out of 400+ exams conducted at the Dartmouth Hitchcock Medical Center (Lebanon, NH). The first example demonstrates the potential usefulness of our system for breast cancer screening while the second example focuses on therapy monitoring.

微波乳腺成像（使用频率约为1 GHz的电磁波）在过去十年中大多停留在研究水平，临床接受度不高。限制患者使用的主要障碍既包括硬件层面（收集准确且无损坏数据的挑战），也包括软件层面（通常受困于数十小时的不切实际的重建时间）。在本文中，我们报告了解决这两个问题的改进。首先，硬件能够测量到与亚厘米图像分辨率兼容的信号水平，同时将检查时间控制在2分钟以内。其次，软件克服了巨大的时间负担，并在不到20分钟内生成同样精确的图像。新硬件和软件的结合使我们能够生成并在此报告首例临床3D微波断层乳腺图像。我们从达特茅斯希区柯克医疗中心（新罕布什尔州黎巴嫩）进行的400多次检查中选取了两个临床实例。第一个实例展示了我们的系统在乳腺癌筛查中的潜在用途，而第二个实例则侧重于治疗监测。

A new method to correct the barrel distortion of an electronic endoscope image is presented. A correction model assuming circularly symmetric distortion is introduced with the following model parameters: the center of distortion and the coefficients of polynomials representing the distortion correction in the radial direction. If the imaging system is distortion-free, straight lines in the object ...

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