results: 实验表明，使用不同的扩散方法不会提高重建质量，而是数据适应性更重要。此外，这个论文还提出了一些基本的理论和估计结果，包括在1-D情况下的等价关系。Abstract
The filling-in effect of diffusion processes is a powerful tool for various image analysis tasks such as inpainting-based compression and dense optic flow computation. For noisy data, an interesting side effect occurs: The interpolated data have higher confidence, since they average information from many noisy sources. This observation forms the basis of our denoising by inpainting (DbI) framework. It averages multiple inpainting results from different noisy subsets. Our goal is to obtain fundamental insights into key properties of DbI and its connections to existing methods. Like in inpainting-based image compression, we choose homogeneous diffusion as a very simple inpainting operator that performs well for highly optimized data. We propose several strategies to choose the location of the selected pixels. Moreover, to improve the global approximation quality further, we also allow to change the function values of the noisy pixels. In contrast to traditional denoising methods that adapt the operator to the data, our approach adapts the data to the operator. Experimentally we show that replacing homogeneous diffusion inpainting by biharmonic inpainting does not improve the reconstruction quality. This again emphasizes the importance of data adaptivity over operator adaptivity. On the foundational side, we establish deterministic and probabilistic theories with convergence estimates. In the non-adaptive 1-D case, we derive equivalence results between DbI on shifted regular grids and classical homogeneous diffusion filtering via an explicit relation between the density and the diffusion time.
摘要
Diffusion 过程中的填充效果是许多图像分析任务的有力工具，如填充基于压缩和稠密光流计算。对于噪声污染的数据，有一个 interessante 的侧效： interpolated 数据具有更高的信任度，因为它们平均了许多噪声来源的信息。这个观察成为我们denoising by inpainting（DbI）框架的基础。DbI 平均了不同噪声子集的多个填充结果。我们的目标是获得基本的洞察和现有方法的连接。与填充基于图像压缩类似，我们选择了高度一致的扩散作为非常简单的填充算子，它在高度优化的数据上表现良好。我们还提出了多种选择选择的像素位置策略，以及改进全局approximation质量的方法。与传统的噪声除法方法不同，我们的方法将数据适应到算子而不是适应到数据。实验表明，将Homogeneous替换为Biharmonic不会提高重建质量。这再次强调了数据适应性的重要性，而不是算子适应性。在基础方面，我们建立了deterministic和probabilistic 理论，并提供了收敛估计。在非适应的1-D情况下，我们 derivation 了DbI 在偏移的正规网格上和经典扩散滤波器之间的等价关系，这种关系可以用来描述density 和扩散时间之间的直接关系。
Design of Novel Loss Functions for Deep Learning in X-ray CT
paper_authors: Obaidullah Rahman, Ken D. Sauer, Madhuri Nagare, Charles A. Bouman, Roman Melnyk, Jie Tang, Brian Nett
for: 提高透射计算机断层（CT）图像质量
methods: 使用深度学习（DL）方法，包括在数据频谱域和重建图像域中进行训练
results: 提出创新的损失函数方法，以更好地衡量图像质量和频谱内容的损失，以提高CT图像重建的精度Abstract
Deep learning (DL) shows promise of advantages over conventional signal processing techniques in a variety of imaging applications. The networks' being trained from examples of data rather than explicitly designed allows them to learn signal and noise characteristics to most effectively construct a mapping from corrupted data to higher quality representations. In inverse problems, one has options of applying DL in the domain of the originally captured data, in the transformed domain of the desired final representation, or both. X-ray computed tomography (CT), one of the most valuable tools in medical diagnostics, is already being improved by DL methods. Whether for removal of common quantum noise resulting from the Poisson-distributed photon counts, or for reduction of the ill effects of metal implants on image quality, researchers have begun employing DL widely in CT. The selection of training data is driven quite directly by the corruption on which the focus lies. However, the way in which differences between the target signal and measured data is penalized in training generally follows conventional, pointwise loss functions. This work introduces a creative technique for favoring reconstruction characteristics that are not well described by norms such as mean-squared or mean-absolute error. Particularly in a field such as X-ray CT, where radiologists' subjective preferences in image characteristics are key to acceptance, it may be desirable to penalize differences in DL more creatively. This penalty may be applied in the data domain, here the CT sinogram, or in the reconstructed image. We design loss functions for both shaping and selectively preserving frequency content of the signal.
摘要
深度学习（DL）在各种成像应用中显示出优势，比如传统的信号处理技术。DL网络从数据示例而不是直接设计，因此可以学习信号和噪声特征，以最有效地构建受损数据到高质量表示的映射。在逆问题中，可以在原始数据频谱中应用DL，在愿望的最终表示频谱中应用DL，或者两者都应用。X射针 Computed Tomography（CT）是医学诊断中最重要的工具，已经由DL方法进行改进。DL可以用于去除常见的量子噪声，或者去除金属implant的影响而导致的图像质量下降。选择训练数据的驱动因素受到受损的影响很直接。然而，在训练中对目标信号和测量数据之间的差别进行惩罚通常采用传统的点均方差或点绝对差惩罚函数。本工作介绍了一种创新的技术，即在DL中不以均方或绝对差惩罚函数来惩罚差别。特别在X射针CT领域， где radiologists的主观偏好在图像特征上对接受性至关重要。在这种情况下，可能需要通过更创新的惩罚方式来惩罚DL。我们设计了在数据频谱中和重建图像中应用的损失函数，以Shape和选择性保留信号的频谱特征。
Statistically Adaptive Filtering for Low Signal Correction in X-ray Computed Tomography
paper_authors: Obaidullah Rahman, Ken D. Sauer, Charles A. Bouman, Roman Melnyk, Brian Nett
for: 实现低X射线剂量CT图像成像，并且维护适当的医学效果。
methods: 使用灵活范围滤波器来缓和低信号领域的残留artefacts。
results: 提高低频率偏好、条状artefacts、本地平均值和标准差、模拟转换函数和杂音功率спектrum等指标。Abstract
Low x-ray dose is desirable in x-ray computed tomographic (CT) imaging due to health concerns. But low dose comes with a cost of low signal artifacts such as streaks and low frequency bias in the reconstruction. As a result, low signal correction is needed to help reduce artifacts while retaining relevant anatomical structures. Low signal can be encountered in cases where sufficient number of photons do not reach the detector to have confidence in the recorded data. % NOTE: SNR is ratio of powers, not std. dev. X-ray photons, assumed to have Poisson distribution, have signal to noise ratio proportional to the dose, with poorer SNR in low signal areas. Electronic noise added by the data acquisition system further reduces the signal quality. In this paper we will demonstrate a technique to combat low signal artifacts through adaptive filtration. It entails statistics-based filtering on the uncorrected data, correcting the lower signal areas more aggressively than the high signal ones. We look at local averages to decide how aggressive the filtering should be, and local standard deviation to decide how much detail preservation to apply. Implementation consists of a pre-correction step i.e. local linear minimum mean-squared error correction, followed by a variance stabilizing transform, and finally adaptive bilateral filtering. The coefficients of the bilateral filter are computed using local statistics. Results show that improvements were made in terms of low frequency bias, streaks, local average and standard deviation, modulation transfer function and noise power spectrum.
摘要
低剂量X射线是在X射线计算机断层成像（CT）中所需的，因为它可以降低健康风险。然而，低剂量也会导致低信号artefacts，如斜线和低频偏好。为了减少这些artefacts，而不失去有关生物结构的信息，需要进行低信号修正。低信号可以在具有不足的X射线 фотоン数据 recording 时出现，这会导致信号质量下降。在这种情况下，X射线 photons 的信号噪声比（SNR）会随剂量的增加。electronic noise 由数据获取系统添加到数据中，进一步减少信号质量。本文将介绍一种用于解决低信号artefacts的技术 - 适应 filters。这种技术基于统计分析，通过对未经修正的数据进行统计分析，更加严格地修正低信号区域。我们根据本地平均值和本地标准差来决定修正的程度，以保留生物结构的细节。实现方式包括先进行本地线性最小二乘均值修正，然后应用变量稳定化变换，最后使用适应二值滤波。适应滤波的系数是根据本地统计来计算的。结果表明，该技术可以提高低频偏好、斜线、本地平均值和标准差、模ulation transfer function 和噪声电力谱的性能。
results: 研究发现，使用这种深度学习模型可以快速获得高品质MBIR图像，并且计算成本远低于传统的MBIR方法。图像的文本特征和噪声功率谱都与MBIR图像相似，表明深度学习模型成功模拟了MBIR操作。Abstract
In computed tomographic imaging, model based iterative reconstruction methods have generally shown better image quality than the more traditional, faster filtered backprojection technique. The cost we have to pay is that MBIR is computationally expensive. In this work we train a 2.5D deep learning (DL) network to mimic MBIR quality image. The network is realized by a modified Unet, and trained using clinical FBP and MBIR image pairs. We achieve the quality of MBIR images faster and with a much smaller computation cost. Visually and in terms of noise power spectrum (NPS), DL-MBIR images have texture similar to that of MBIR, with reduced noise power. Image profile plots, NPS plots, standard deviation, etc. suggest that the DL-MBIR images result from a successful emulation of an MBIR operator.
摘要
在计算tomografic imaging中，基于模型的迭代重建方法通常会提供更好的图像质量，相比较传统的快速滤波后 проекcion技术。然而，MBIR是计算成本高的。在这项工作中，我们使用一个modified U-Net架构来模拟MBIR图像质量。我们使用临床FBP和MBIR图像对的 pairs来训练网络，并在计算成本下降的情况下实现MBIR图像质量。视觉和噪声电磁谱（NPS）等指标表明，DL-MBIR图像具有与MBIR图像相似的 текстура，噪声电磁谱下降。图像profile plot、NPS plot等指标表明，DL-MBIR图像是一个成功地模拟MBIROperator的结果。
Direct Iterative Reconstruction of Multiple Basis Material Images in Photon-counting Spectral CT
results: 在low-concentration scan中，使用了这种方法可以在ROIs中获得volume fractions的 Close to ground truth值。这项研究旨在为将来包含空间含义和/或材料含量regularization的phantoms、动物成像和临床应用铺垫。Abstract
In this work, we perform direct material reconstruction from spectral CT data using a model based iterative reconstruction (MBIR) approach. Material concentrations are measured in volume fractions, whose total is constrained by a maximum of unity. A phantom containing a combination of 4 basis materials (water, iodine, gadolinium, calcium) was scanned using a photon-counting detector. Iodine and gadolinium were chosen because of their common use as contrast agents in CT imaging. Scan data was binned into 5 energy (keV) levels. Each energy bin in a calibration scan was reconstructed, allowing the linear attenuation coefficient of each material for every energy to be estimated by a least-squares fit to ground truth in the image domain. The resulting $5\times 4$ matrix, for $5$ energies and $4$ materials, is incorporated into the forward model in direct reconstruction of the $4$ basis material images with spatial and/or inter-material regularization. In reconstruction from a subsequent low-concentration scan, volume fractions within regions of interest (ROIs) are found to be close to the ground truth. This work is meant to lay the foundation for further work with phantoms including spatially coincident mixtures of contrast materials and/or contrast agents in widely varying concentrations, molecular imaging from animal scans, and eventually clinical applications.
摘要
在这项工作中，我们使用基于模型的迭代重建（MBIR）方法直接重建物质图像从 spectral CT 数据。物质浓度表示为体积分数，总是受限于最大unity。一个包含四种基本材料（水、iodine、gadolinium、 calcium）的phantom在一个photon-counting 探测器上进行了扫描。iodine 和 gadolinium 选择是因为它们在 CT 图像中广泛使用为contrast agent。扫描数据被分割成5个能量（keV）层。每个能量层在一个calibration scan中的每个像素的直线吸收系数可以通过对真实图像中的图像域最小二乘来估算。这个 $5\times 4$ 矩阵，其中有5个能量和4种材料，被 incorporated 到了直接重建的 forward 模型中。在重建从后续的低浓度扫描中，ROIs 中的体积分数几乎与真实值一致。这项工作的目的是为了铺垫将来的灵活材料混合物和高级分子成像、动物扫描和临床应用。
Semantic Communications using Foundation Models: Design Approaches and Open Issues
paper_authors: Peiwen Jiang, Chao-Kai Wen, Xinping Yi, Xiao Li, Shi Jin, Jun Zhang
for: This paper aims to investigate the impact of foundation models (FMs) on different system levels, including computation and memory complexity, and to explore the use of compact models to balance performance and complexity.
methods: The paper uses universal knowledge to profoundly transform system design and employs three separate approaches that employ FMs to balance performance and complexity.
results: The study highlights unresolved issues in the field that need addressing, and provides insights into the effectiveness, semantic, and physical levels of system design.Abstract
Foundation models (FMs), including large language models, have become increasingly popular due to their wide-ranging applicability and ability to understand human-like semantics. While previous research has explored the use of FMs in semantic communications to improve semantic extraction and reconstruction, the impact of these models on different system levels, considering computation and memory complexity, requires further analysis. This study focuses on integrating FMs at the effectiveness, semantic, and physical levels, using universal knowledge to profoundly transform system design. Additionally, it examines the use of compact models to balance performance and complexity, comparing three separate approaches that employ FMs. Ultimately, the study highlights unresolved issues in the field that need addressing.
摘要
基于语言模型（FM）的应用，包括大型语言模型，在各种领域得到广泛应用，这主要归功于它们能够理解人类语言 semantics。 although previous research has explored the use of FMs in semantic communications to improve semantic extraction and reconstruction, the impact of these models on different system levels, considering computation and memory complexity, requires further analysis.本研究强调在效iveness、semantic和physical各级别中集成FMs，使用通用知识进行深度变换系统设计。此外，它还研究了使用压缩模型来平衡性能和复杂性，对三种使用FMs的方法进行比较。最终，研究披露了这个领域中还有待解决的问题。Here's the word-for-word translation:基于语言模型（FM）的应用，包括大型语言模型，在各种领域得到广泛应用，这主要归功于它们能够理解人类语言 semantics。 although previous research has explored the use of FMs in semantic communications to improve semantic extraction and reconstruction, the impact of these models on different system levels, considering computation and memory complexity, requires further analysis.本研究强调在效iveness、semantic和physical各级别中集成FMs，使用通用知识进行深度变换系统设计。此外，它还研究了使用压缩模型来平衡性能和复杂性，对三种使用FMs的方法进行比较。最终，研究披露了这个领域中还有待解决的问题。