paper_authors: Yash Sanghvi, Yiheng Chi, Stanley H. Chan
for: 解压缩扩散照明预测
methods: 使用非 слепо的解压缩方法和 diffusion 方法
results: 达到了实验室和真实扩散数据集上的状态 искусственный智能水平结果Abstract
Blind deconvolution problems are severely ill-posed because neither the underlying signal nor the forward operator are not known exactly. Conventionally, these problems are solved by alternating between estimation of the image and kernel while keeping the other fixed. In this paper, we show that this framework is flawed because of its tendency to get trapped in local minima and, instead, suggest the use of a kernel estimation strategy with a non-blind solver. This framework is employed by a diffusion method which is trained to sample the blur kernel from the conditional distribution with guidance from a pre-trained non-blind solver. The proposed diffusion method leads to state-of-the-art results on both synthetic and real blur datasets.
摘要
盲目减除问题具有严重的不定性，因为原始信号和前进算子都不能准确知道。通常，这些问题是通过 alternate estimation of the image and kernel while keeping the other fixed 来解决的。在这篇论文中，我们表明这种框架存在问题，因为它容易陷入地方最小值中，而不是使用非盲目解决方案。我们建议使用一种扩散方法，该方法通过从含有前进算子的条件分布中采样抖杂kernel。我们的提议的扩散方法可以在both synthetic和实际抖杂数据集上达到状态的the-art的结果。
Deep learning acceleration of iterative model-based light fluence correction for photoacoustic tomography
results: 对比传统iterative方法，使用FNO神经网络可以在Correct Light Fluence（LF）过程中提高速度，并且可以保持相同的Correct LF质量。Abstract
Photoacoustic tomography (PAT) is a promising imaging technique that can visualize the distribution of chromophores within biological tissue. However, the accuracy of PAT imaging is compromised by light fluence (LF), which hinders the quantification of light absorbers. Currently, model-based iterative methods are used for LF correction, but they require significant computational resources due to repeated LF estimation based on differential light transport models. To improve LF correction efficiency, we propose to use Fourier neural operator (FNO), a neural network specially designed for solving differential equations, to learn the forward projection of light transport in PAT. Trained using paired finite-element-based LF simulation data, our FNO model replaces the traditional computational heavy LF estimator during iterative correction, such that the correction procedure is significantly accelerated. Simulation and experimental results demonstrate that our method achieves comparable LF correction quality to traditional iterative methods while reducing the correction time by over 30 times.
摘要
photoacoustic tomography（PAT）是一种有前途的成像技术，可以显示生物组织中色彩体的分布。然而，PAT成像的准确性受到光荷（LF）的限制，这会降低光吸收量的量化。现在，使用模型基于迭代方法进行LF修正，但这需要很大的计算资源，因为需要基于差分光传输模型多次估计LF。为了提高LF修正的效率，我们提议使用傅里曼神经网络（FNO），一种特地设计用于解决差分方程的神经网络，来学习PAT光传输的前向投影。我们的FNO模型通过使用匹配的finite-element-based LF估计数据进行训练，将传统的计算沉重的LF估计器替换为快速的FNO模型，从而大幅减少了修正过程的时间。实验和计算结果表明，我们的方法可以与传统的迭代方法准确地修正LF，而且减少了修正时间超过30倍。
Accelerated Parallel Magnetic Resonance Imaging with Compressed Sensing using Structured Sparsity
results: 这 paper 的结果表明，通过修改 Sparse SENSE 算法以使用 structured sparsity，可以 reconstruction 高质量图像，并且可以降低样本数量并缩短扫描时间。Abstract
Compressed sensing is an imaging paradigm that allows one to invert an underdetermined linear system by imposing the a priori knowledge that the sought after solution is sparse (i.e., mostly zeros). Previous works have shown that if one also knows something about the sparsity pattern (the locations where non-zero entries exist), one can take advantage of this structure to improve the quality of the result. A significant application of compressed sensing is magnetic resonance imaging (MRI), where samples are acquired in the Fourier domain. Compressed sensing allows one to reconstruct a high-quality image with fewer samples which can be collected with a faster scan. This increases the robustness of MRI to patient motion since less motion is possible during the shorter scan. Parallel imaging, where multiple coils are used to gather data, is another an more ubiquitously used method for accelerating MRI. Existing combinations of these acceleration methods, such as Sparse SENSE, yield high quality images with an even shorter scan time than either technique alone. In this work, we show how to modify Sparse SENSE with structured sparsity to reconstruct a high quality image with even fewer samples.
摘要
压缩感知是一种图像模式，允许我们对不充分的线性系统进行逆解，基于先前知道的含义，即寻找的解是稀疏的（即主要是零）。先前的工作表明，如果我们还知道稀疏性模式（非零元素存在的位置），那么可以利用这种结构来改善结果的质量。压缩感知在核磁共振成像（MRI）中有着重要应用，samples在傅里叶空间中被采集。压缩感知可以重建高质量图像，需要 fewer samples，这使得MRI更加抗震，因为 fewer motion是可能 durante shorter scan。多晶磁共振（Parallel imaging）是另一种更广泛使用的加速方法，其中多个antenna被用来收集数据。现有的加速方法的组合，如Sparse SENSE，可以提供高质量图像，scan时间更短。在这项工作中，我们将如何修改Sparse SENSE，以使用结构稀疏来重建高质量图像，并且需要更少的样本。
Coronary Atherosclerotic Plaque Characterization with Photon-counting CT: a Simulation-based Feasibility Study
paper_authors: Mengzhou Li, Mingye Wu, Jed Pack, Pengwei Wu, Bruno De Man, Adam Wang, Koen Nieman, Ge Wang
for: This paper is written to investigate the imaging capabilities of a deep-silicon photon-counting detector (PCCT) for coronary plaque characterization, with a focus on spatial resolution, noise, motion artifacts, radiation dose, and spectral characterization.
methods: The paper uses a systematic simulation study with a clinically-relevant digital plaque phantom to evaluate the performance of the deep-silicon PCCT scanner. The simulation study considers realistic geometrical parameters and chemical compositions of plaques.
results: The simulation results suggest that the deep-silicon PCCT design provides adequate spatial resolution for visualizing a necrotic core and quantitation of key plaque features. Advanced denoising techniques and aggressive bowtie filter designs can keep image noise to acceptable levels at this resolution while keeping radiation dose comparable to that of a conventional CT scan. However, the ultrahigh resolution of PCCT also means an elevated sensitivity to motion artifacts, and accurate motion correction methods are required for best plaque imaging quality.Here is the same information in Simplified Chinese text:
results: simulations 结果表明，深层晶体PCCT设计可以提供足够的空间分辨率 для识别肿瘤中的衰竭核心和关键肿瘤特征的量化。高级排除噪声技术和积极的碎弧筛件设计可以保持图像噪声到可接受水平，同时保持辐射剂量与传统CT扫描相比。但是，PCCT的超高分辨率也意味着运动artefacts 的敏感性增加。Abstract
Recent development of photon-counting CT (PCCT) brings great opportunities for plaque characterization with much-improved spatial resolution and spectral imaging capability. While existing coronary plaque PCCT imaging results are based on detectors made of CZT or CdTe materials, deep-silicon photon-counting detectors have unique performance characteristics and promise distinct imaging capabilities. In this work, we report a systematic simulation study of a deep-silicon PCCT scanner with a new clinically-relevant digital plaque phantom with realistic geometrical parameters and chemical compositions. This work investigates the effects of spatial resolution, noise, motion artifacts, radiation dose, and spectral characterization. Our simulation results suggest that the deep-silicon PCCT design provides adequate spatial resolution for visualizing a necrotic core and quantitation of key plaque features. Advanced denoising techniques and aggressive bowtie filter designs can keep image noise to acceptable levels at this resolution while keeping radiation dose comparable to that of a conventional CT scan. The ultrahigh resolution of PCCT also means an elevated sensitivity to motion artifacts. It is found that a tolerance of less than 0.4 mm residual movement range requires the application of accurate motion correction methods for best plaque imaging quality with PCCT.
摘要
近些年，光子计数 computed tomography（PCCT）的发展带来了对颗粒特征化的巨大机会，以提高空间分辨率和spectral imaging能力。现有的核心粒子PCCT成像结果基于CZT或CdTe材料的探测器，深入的半导体光子计数探测器具有独特的性能特点，承诺着不同的成像能力。在这项工作中，我们进行了一项系统性的PCCT扫描仪设计 simulate study，使用一个新的临床相关的数字颗粒模拟器，模拟了实际的几何参数和化学成分。这项工作研究了PCCT扫描仪的影像质量、噪声、运动 artifacts、辐射剂量和spectral特征。我们的 simulate结果表明，深入PCCT设计可以提供足够的空间分辨率，用于可见颗粒核心和颗粒特征的量化。使用高级滤波器技术和灵活的弯钩滤波器设计可以保持图像噪声在接受度水平，同时保持辐射剂量与传统CT扫描相同。PCCT的超高分辨率也意味着对运动 artifacts的敏感性增加。我们发现，对于最佳颗粒成像质量，运动 artifacts的准确修正方法必须在0.4毫米之间进行。