Most Cited Articles

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。

A novel method is introduced for the generation of landmarks for three-dimensional (3-D) shapes and the construction of the corresponding 3-D statistical shape models. Automatic landmarking of a set of manual segmentations from a class of shapes is achieved by 1) construction of an atlas of the class, 2) automatic extraction of the landmarks from the atlas, and 3) subsequent propagation of these l...

介绍了一种用于三维形状地标生成及相应三维统计形状模型构建的新方法。通过1)构建该类形状的图谱，2)从图谱中自动提取地标，3)随后将这些地标传播到...来实现对一类形状的手动分割的自动地标标记。

Fractals have been shown to be useful in characterizing texture in a variety of contexts. Use of this methodology normally involves measurement of a parameter H, which is directly related to fractal dimension. In this work the basic theory of fractional Brownian motion is extended to the discrete case. It is shown that the power spectral density of such a discrete process is only approximately pro...

中文摘要翻译待生成

Accurate definition of the similarity measure is a key component in image registration. Most commonly used intensity-based similarity measures rely on the assumptions of independence and stationarity of the intensities from pixel to pixel. Such measures cannot capture the complex interactions among the pixel intensities, and often result in less satisfactory registration performances, especially i...

相似性度量的精确定义是图像配准中的关键组成部分。最常用的基于强度的相似性度量依赖于像素间强度的独立性和平稳性假设。这些度量无法捕捉像素强度之间的复杂相互作用，常常导致配准性能不理想，尤其是...

Derives approximate analytical expressions for the local impulse response and covariance of images reconstructed from fully three-dimensional (3-D) positron emission tomography (PET) data using maximum a posteriori (MAP) estimation. These expressions explicitly account for the spatially variant detector response and sensitivity of a 3-D tomograph. The resulting spatially variant impulse response and covariance are computed using 3-D Fourier transforms. A truncated Gaussian distribution is used to account for the effect on the variance of the nonnegativity constraint used in MAP reconstruction. Using Monte Carlo simulations and phantom data from the microPET small animal scanner, the authors show that the approximations provide reasonably accurate estimates of contrast recovery and covariance of MAP reconstruction for priors with quadratic energy functions. They also describe how these analytical results can be used to achieve near-uniform contrast recovery throughout the reconstructed volume.

推导了使用最大后验（MAP）估计从全三维（3-D）正电子发射断层扫描（PET）数据重建图像的局部脉冲响应和协方差的近似解析表达式。这些表达式明确考虑了三维断层扫描仪的空间变化探测器响应和灵敏度。使用三维傅里叶变换计算得到的空间变化脉冲响应和协方差。采用截断高斯分布来解释MAP重建中非负性约束对方差的影响。通过蒙特卡罗模拟和来自microPET小动物扫描仪的体模数据，作者表明这些近似方法能够为具有二次能量函数的先验提供对比度恢复和协方差的合理准确估计。他们还描述了如何利用这些分析结果在整个重建体积中实现接近均匀的对比度恢复。

A quantitative method of skin healing assessment using true color image processing is presented. The method was developed during a clinical trial using healthy volunteers, the goal of which was to study a drug for accelerating healing. Photographic images of the skin were sequentially acquired between day 1 and day 12 after pure painless epidermal wounds. The images were digitized in controlled co...

中文摘要翻译待生成

A robust fully automatic method for segmenting the brain from head magnetic resonance (MR) images has been developed, which works even in the presence of radio frequency (RF) inhomogeneities. It has been successful in segmenting the brain in every slice from head images acquired from several different MRI scanners, using different-resolution images and different echo sequences. The method uses an ...

一种鲁棒的全自动方法已经被开发用于从头部磁共振（MR）图像中分割脑部，该方法甚至在存在射频（RF）不均匀性的情况下也能工作。它已成功地从使用不同分辨率的图像和不同回波序列的多个不同MRI扫描仪获取的头部图像的每个切片中分割出脑部。该方法使用一种……

Describes a fully-automatic three-dimensional (3-D)-segmentation technique for brain magnetic resonance (MR) images. By means of Markov random fields (MRF's) the segmentation algorithm captures three features that are of special importance for MR images, i.e., nonparametric distributions of tissue intensities, neighborhood correlations, and signal inhomogeneities. Detailed simulations and real MR images demonstrate the performance of the segmentation algorithm. In particular, the impact of noise, inhomogeneity, smoothing, and structure thickness are analyzed quantitatively. Even single-echo MR images are well classified into gray matter, white matter, cerebrospinal fluid, scalp-bone, and background. A simulated annealing and an iterated conditional modes implementation are presented.

描述了一种用于脑部磁共振（MR）图像的全自动三维（3-D）分割技术。通过马尔可夫随机场（MRF），该分割算法捕捉了MR图像中特别重要的三个特征，即组织强度的非参数分布、邻域相关性和信号不均匀性。详细的模拟和真实MR图像展示了分割算法的性能。特别地，定量分析了噪声、不均匀性、平滑和结构厚度的影响。即使是单回波MR图像也能很好地分类为灰质、白质、脑脊液、头皮骨骼和背景。文中介绍了模拟退火和迭代条件模式两种实现方法。