paper_authors: Thomas H. M. Roos, Edwin Versteeg, Dennis W. J. Klomp, Jeroen C. W. Siero, Jannie P. Wijnen
for: 这个论文的目的是为研究人员开发和分享新的MRI序列提供一个可用的开源平台。
methods: 这个论文使用了修改一些源代码文件来创建一个Pulseq解释器 для Philips MRI系统。验证实验使用了模拟和在7T Achieva MRI系统上进行的phantom扫描。
results: 通过Pulseq实现得到的重建图像与原始实现的图像相当，并且对MRI仪器的资源利用进行了评估，显示了一定的可扩展性。I hope that helps! Let me know if you have any other questions.Abstract
Purpose: This work aims to address the limitations faced by researchers in developing and sharing new MRI sequences by implementing an interpreter for the open-source MRI pulse sequence format, Pulseq, on a Philips MRI scanner. Methods: The implementation involved modifying a few source code files to create a Pulseq interpreter for the Philips MRI system. Validation experiments were conducted using simulations and phantom scans performed on a 7T Achieva MRI system. The observed sequence and waveforms were compared to the intended ones, and the gradient waveforms produced by the scanner were verified using a field camera. Image reconstruction was performed using the raw k-space samples acquired from both the native vendor environment and the Pulseq interpreter. Results: The reconstructed images obtained through the Pulseq implementation were found to be comparable to those obtained through the native implementation. The performance of the Pulseq interpreter was assessed by profiling the CPU utilization of the MRI spectrometer, showing minimal resource utilization for certain sequences. Conclusion: The successful implementation of the Pulseq interpreter on the Philips MRI scanner demonstrates the feasibility of utilizing Pulseq sequences on Philips MRI scanners. This provides an open-source platform for MRI sequence development, facilitating collaboration among researchers and accelerating scientific progress in the field of MRI.
摘要
目的：本研究旨在解决开源MRI序列格式（Pulseq）的开发和共享限制，通过在菲利浦MRI系统上实现Pulseq解释器。方法：实现过程包括修改一些源代码文件，以创建一个Pulseq解释器 для菲利浦MRI系统。验证实验使用了模拟和phantom扫描，在7T Achieva MRI系统上进行。观察的序列和波形与意图的相比，并使用场Camera验证了扫描产生的梯度波形。图像重建使用了原始的k空间样本，从Native vendor环境和Pulseq解释器中获取。结果：通过Pulseq实现的重建图像与Native实现的图像相似。解释器的性能评估通过CPU资源的使用量进行，表明某些序列的资源使用率很低。结论：成功实现Pulseq解释器在菲利浦MRI系统上，证明了使用Pulseq序列在菲利浦MRI系统上的可能性。这提供了一个开源平台 дляMRI序列开发，促进研究人员之间的合作，加速MRI领域科学进步。
Compression Ratio Learning and Semantic Communications for Video Imaging
results: 通过使用政策Gradient方法来实现明确的压缩率与影像歪斜调整 trade-off，提高影像质量。numerical results show the superiority of the proposed methods over existing baselines.Abstract
Camera sensors have been widely used in intelligent robotic systems. Developing camera sensors with high sensing efficiency has always been important to reduce the power, memory, and other related resources. Inspired by recent success on programmable sensors and deep optic methods, we design a novel video compressed sensing system with spatially-variant compression ratios, which achieves higher imaging quality than the existing snapshot compressed imaging methods with the same sensing costs. In this article, we also investigate the data transmission methods for programmable sensors, where the performance of communication systems is evaluated by the reconstructed images or videos rather than the transmission of sensor data itself. Usually, different reconstruction algorithms are designed for applications in high dynamic range imaging, video compressive sensing, or motion debluring. This task-aware property inspires a semantic communication framework for programmable sensors. In this work, a policy-gradient based reinforcement learning method is introduced to achieve the explicit trade-off between the compression (or transmission) rate and the image distortion. Numerical results show the superiority of the proposed methods over existing baselines.
摘要
Camera 感测器在智能 роботи系统中广泛应用。开发高感知效率的 camera 感测器总是非常重要，以降低功能、存储和相关资源。受最近的可编程感测器和深度光学方法的成功启发，我们设计了一种新的视频压缩感测系统，其中具有空间变化的压缩比率，可以实现比现有的单张图像压缩成像方法更高的成像质量。在这篇文章中，我们还研究了用于可编程感测器的数据传输方法，并评估了通信系统的性能基于传输的感测器数据而不是直接传输感测器数据本身。通常，不同的重建算法采用了应用于高动态范围成像、压缩成像或运动锐化等应用场景。这种任务意识的性能 inspirits 一种Semantic Communication Framework for 可编程感测器。在这种工作中，我们引入了一种基于Policy Gradient的强化学习方法，以实现显式地考虑压缩率和图像扭曲之间的平衡。numerical 结果表明我们的方法在现有基线之上具有超越性。
Domain Expansion via Network Adaptation for Solving Inverse Problems
paper_authors: Nebiyou Yismaw, Ulugbek S. Kamilov, M. Salman Asif for:* 这篇论文主要针对的是解决计算成像中的反向问题，提出了一种基于深度学习的方法。methods:* 这种方法可以分为两个类别：一是学习网络将测量转换为信号估计，这种方法容易受到数据分布的变化的影响；二是学习信号的假设，然后使用优化方法来回收信号。results:* 这篇论文通过研究不同类型的域转换的影响，并提出了一种可以灵活地适应不同域的框架，使得已经训练过的网络可以更好地适应不同的数据分布。这种方法在自然图像、MRI和CT重建任务中获得了显著更好的性能和参数效率。Abstract
Deep learning-based methods deliver state-of-the-art performance for solving inverse problems that arise in computational imaging. These methods can be broadly divided into two groups: (1) learn a network to map measurements to the signal estimate, which is known to be fragile; (2) learn a prior for the signal to use in an optimization-based recovery. Despite the impressive results from the latter approach, many of these methods also lack robustness to shifts in data distribution, measurements, and noise levels. Such domain shifts result in a performance gap and in some cases introduce undesired artifacts in the estimated signal. In this paper, we explore the qualitative and quantitative effects of various domain shifts and propose a flexible and parameter efficient framework that adapt pretrained networks to such shifts. We demonstrate the effectiveness of our method for a number of natural image, MRI, and CT reconstructions tasks under domain, measurement model, and noise-level shifts. Our experiments demonstrate that our method provides significantly better performance and parameter efficiency compared to existing domain adaptation techniques.
摘要
深度学习基本方法可以解决计算成像中的逆问题，这些方法可以分为两个组：（1）学习网络来将测量转换为信号估计，这是知道脆弱的；（2）学习信号的先验来在优化基础上进行恢复。尽管后者的方法也具有很好的成果，但是许多这些方法缺乏对数据分布、测量、和噪声水平的鲁棒性，这会导致性能差异和在某些情况下添加不必要的artefacts到估计的信号中。在这篇论文中，我们研究了不同类型的领域变化的影响，并提出了一种灵活和参数高效的框架，可以适应这些变化。我们通过多个自然图像、MRI和CT重建任务的实验，证明了我们的方法可以在不同的领域、测量模型和噪声水平下提供了显著更好的性能和参数高效性，相比之下现有的领域适应技术。