Volume 37, Issue 6

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。

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 Learned Primal-Dual algorithm for tomographic reconstruction. The algorithm accounts for a (possibly non-linear) forward operator in a deep neural network by unrolling a proximal primal-dual optimization method, but where the proximal operators have been replaced with convolutional neural networks. The algorithm is trained end-to-end, working directly from raw measured data and it d...

我们提出了用于断层重建的学习型原始-对偶算法。该算法通过展开近端原始-对偶优化方法，将（可能非线性的）前向算子纳入深度神经网络中，其中近端算子被卷积神经网络取代。该算法以端到端方式训练，直接处理原始测量数据，并且...

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。

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...

In the presence of metal implants, metal artifacts are introduced to x-ray computed tomography CT images. Although a large number of metal artifact reduction (MAR) methods have been proposed in the past decades, MAR is still one of the major problems in clinical x-ray CT. In this paper, we develop a convolutional neural network (CNN)-based open MAR framework, which fuses the information from the o...

在金属植入物存在的情况下，X射线计算机断层扫描（CT）图像中会出现金属伪影。尽管过去几十年提出了大量的金属伪影减少（MAR）方法，但MAR仍然是临床X射线CT的主要问题之一。本文开发了一种基于卷积神经网络（CNN）的开放式MAR框架，该框架融合了来自...的信息

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...

Sparse-view computed tomography (CT) holds great promise for speeding up data acquisition and reducing radiation dose in CT scans. Recent advances in reconstruction algorithms for sparse-view CT, such as iterative reconstruction algorithms, obtained high-quality image while requiring advanced computing power. Lately, deep learning (DL) has been widely used in various applications and has obtained ...

稀疏视角计算机断层扫描（CT）在加速数据采集和减少CT扫描辐射剂量方面具有很大前景。最近稀疏视角CT重建算法的进展，如迭代重建算法，在获得高质量图像的同时需要先进的计算能力。近来，深度学习（DL）已广泛应用于各种应用，并取得了...

Low-dose computed tomography (LDCT) has attracted major attention in the medical imaging field, since CT-associated X-ray radiation carries health risks for patients. The reduction of the CT radiation dose, however, compromises the signal-to-noise ratio, which affects image quality and diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in LDCT denoisin...

低剂量计算机断层扫描（LDCT）在医学成像领域引起了广泛关注，因为CT相关的X射线辐射对患者存在健康风险。然而，降低CT辐射剂量会降低信噪比，从而影响图像质量和诊断性能。最近，基于深度学习的算法在LDCT去噪方面取得了令人鼓舞的结果。

Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view computed tomography (CT), tomosynthesis, interior tomography, and so on. To perform sparse-data CT, the iterative reconstruction commonly uses regularizers in the CS framework. Currently, how to choose the parameters a...

压缩感知（CS）已被证明对于从稀疏采集数据或欠采样测量中进行断层重建是有效的，这对于少视图计算机断层扫描（CT）、断层合成、内部断层成像等实际应用非常重要。为了执行稀疏数据CT，迭代重建通常使用CS框架中的正则化器。目前，如何选择参数...

We present a new image reconstruction method that replaces the projector in a projected gradient descent (PGD) with a convolutional neural network (CNN). Recently, CNNs trained as image-to-image regressors have been successfully used to solve inverse problems in imaging. However, unlike existing iterative image reconstruction algorithms, these CNN-based approaches usually lack a feedback mechanism...

我们提出了一种新的图像重建方法，该方法将投影梯度下降（PGD）中的投影器替换为卷积神经网络（CNN）。最近，训练为图像到图像回归器的CNN已成功用于解决成像中的逆问题。然而，与现有的迭代图像重建算法不同，这些基于CNN的方法通常缺乏反馈机制...

Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (CT) are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the textures were not fully recovered. To address this problem, here we propose a n...

基于模型的迭代重建算法用于低剂量X射线计算机断层扫描（CT）计算成本高。为了解决这个问题，我们最近提出了一种用于低剂量X射线CT的深度卷积神经网络（CNN），并在2016年AAPM低剂量CT大挑战中获得第二名。然而，一些纹理并未完全恢复。为了解决这个问题，本文提出了一种...

Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed up. In this paper, we present a deep neural network that is specifically designed to provide high resolution 3-D images from restricted photoacoustic measurements. The network is designed to represent an iterative scheme and incorporates g...

近期深度学习在断层重建方面的进展显示出在显著加速的同时生成准确高质量图像的巨大潜力。本文提出了一种专门设计的深度神经网络，用于从受限的光声测量中提供高分辨率3D图像。该网络设计为表示迭代方案，并结合了...

In this paper, we present a new deep learning framework for 3-D tomographic reconstruction. To this end, we map filtered back-projection-type algorithms to neural networks. However, the back-projection cannot be implemented as a fully connected layer due to its memory requirements. To overcome this problem, we propose a new type of cone-beam back-projection layer, efficiently calculating the forwa...

本文提出了一种用于三维断层重建的深度学习新框架。为此，我们将滤波反投影类算法映射到神经网络。然而，由于内存需求，反投影无法作为全连接层实现。为了解决这个问题，我们提出了一种新型锥束反投影层，能够高效地计算前向...

Interventional applications of photoacoustic imaging typically require visualization of point-like targets, such as the small, circular, cross-sectional tips of needles, catheters, or brachytherapy seeds. When these point-like targets are imaged in the presence of highly echogenic structures, the resulting photoacoustic wave creates a reflection artifact that may appear as a true signal. We propos...

光声成像的介入应用通常需要可视化点状目标，例如针、导管或近距离放疗种子的小圆形横截面尖端。当这些点状目标在存在高回声结构的情况下成像时，产生的光声波会产生反射伪影，可能表现为真实信号。我们提出...

Motivated by the great potential of deep learning in medical imaging, we propose an iterative positron emission tomography reconstruction framework using a deep learning-based prior. We utilized the denoising convolutional neural network (DnCNN) method and trained the network using full-dose images as the ground truth and low dose images reconstructed from downsampled data by Poisson thinning as i...

受深度学习在医学影像中巨大潜力的启发，我们提出了一种基于深度学习先验的迭代正电子发射断层扫描重建框架。我们利用了去噪卷积神经网络（DnCNN）方法，并使用全剂量图像作为真实标签，以及通过泊松细化对下采样数据重建的低剂量图像作为输入进行训练。

A number of image-processing problems can be formulated as optimization problems. The objective function typically contains several terms specifically designed for different purposes. Parameters in front of these terms are used to control the relative importance among them. It is of critical importance to adjust these parameters, as quality of the solution depends on their values. Tuning parameter...

许多图像处理问题可以表述为优化问题。目标函数通常包含几个专门为不同目的设计的项。这些项前面的参数用于控制它们之间的相对重要性。调整这些参数至关重要，因为解的质量取决于它们的值。参数调整...

In the area of magnetic resonance imaging (MRI), an extensive range of non-linear reconstruction algorithms has been proposed which can be used with general Fourier subsampling patterns. However, the design of these subsampling patterns has typically been considered in isolation from the reconstruction rule and the anatomy under consideration. In this paper, we propose a learning-based framework f...

在磁共振成像（MRI）领域，已经提出了大量非线性重建算法，可用于一般的傅里叶欠采样模式。然而，这些欠采样模式的设计通常与重建规则和所考虑的解剖结构相脱离。在本文中，我们提出了一个基于学习的框架...

The development of computed tomography (CT) image reconstruction methods that significantly reduce patient radiation exposure, while maintaining high image quality is an important area of research in low-dose CT imaging. We propose a new penalized weighted least squares (PWLS) reconstruction method that exploits regularization based on an efficient Union of Learned TRAnsforms (PWLS-ULTRA). The uni...

开发能够显著减少患者辐射暴露同时保持高图像质量的计算机断层扫描（CT）图像重建方法是低剂量CT成像研究的重要领域。我们提出了一种新的惩罚加权最小二乘（PWLS）重建方法，该方法利用基于高效学习变换联合（PWLS-ULTRA）的正则化。该...

In positron emission tomography (PET) image reconstruction, the Bayesian framework with various regularization terms has been implemented to constrain the radio tracer distribution. Varying the regularizing weight of a maximum a posteriori (MAP) algorithm specifies a lower bound of the tradeoff between variance and spatial resolution measured from the reconstructed images. The purpose of this pape...

在正电子发射断层扫描（PET）图像重建中，已实现具有各种正则化项的贝叶斯框架来约束放射性示踪剂分布。改变最大后验（MAP）算法的正则化权重，可以指定从重建图像中测量的方差和空间分辨率之间权衡的下限。本文的目的是...

Cone-beam computed tomography (CBCT) plays an important role in radiation therapy. Statistical iterative reconstruction (SIR) algorithms with specially designed penalty terms provide good performance for low-dose CBCT imaging. Among others, the total variation (TV) penalty is the current state-of-the-art in removing noises and preserving edges, but one of its well-known limitations is its staircas...

锥束计算机断层扫描(CBCT)在放射治疗中扮演重要角色。具有专门设计的惩罚项的统计迭代重建算法为低剂量CBCT成像提供了良好的性能。其中，全变差惩罚是当前去除噪声和保留边缘的最先进技术，但其一个众所周知的局限性是阶梯效应...

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