results: 我们的实验表明，RDS填充在对 вектор值数据的填充方面表现比较好，与许多其他的填充模型相当或更好。Abstract
We introduce regularised diffusion--shock (RDS) inpainting as a modification of diffusion--shock inpainting from our SSVM 2023 conference paper. RDS inpainting combines two carefully chosen components: homogeneous diffusion and coherence-enhancing shock filtering. It benefits from the complementary synergy of its building blocks: The shock term propagates edge data with perfect sharpness and directional accuracy over large distances due to its high degree of anisotropy. Homogeneous diffusion fills large areas efficiently. The second order equation underlying RDS inpainting inherits a maximum--minimum principle from its components, which is also fulfilled in the discrete case, in contrast to competing anisotropic methods. The regularisation addresses the largest drawback of the original model: It allows a drastic reduction in model parameters without any loss in quality. Furthermore, we extend RDS inpainting to vector-valued data. Our experiments show a performance that is comparable to or better than many inpainting models, including anisotropic processes of second or fourth order.
摘要
我们介绍了调整过的扩散---击败（RDS）填写法，这是我们2023年SSVM会议论文中所提出的修改。RDS填写法结合了两个精心选择的 ком成分：恒性扩散和凝聚强化的击败范围滤波器。它受到了这两个元件的补偿性联乘：击败范围滤波器将边数据传递到距离很大的位置，拥有高度的不对称性，而恒性扩散则快速填写大面积。在确定方程的二次项下，RDS填写法继承了最大---最小则理论，这也是竞争性的扩散方法不符的。此外，我们将RDS填写法推广到向量值数据。我们的实验显示RDS填写法的表现与许多填写模型相似或更好，包括二次或四次的不对称过程。
Motion rejection and spectral unmixing for accurate estimation of in vivo oxygen saturation using multispectral optoacoustic tomography
paper_authors: Mitradeep Sarkar, Mailyn Pérez-Liva, Gilles Renault, Bertrand Tavitian, Jérôme Gateau for: This paper aims to develop a two-step image processing method for estimating oxygen saturation (SO$_2$) in deep tissues using multispectral optoacoustic tomography (MSOT).methods: The proposed method consists of two steps. The first step is to mitigate motion artifacts by selecting OA images acquired during the respiratory pause of the animal using ultrafast ultrasound images (USIs). The second step is to estimate directly the SO$_2$ value of each pixel and evaluate the amount of noise present in that pixel, thereby eliminating pixels dominated by noise from the final SO$_2$ map.results: The proposed method was validated by in vivo oxygen challenge experiments and shown to outperform conventional methods for SO$_2$ estimation.Abstract
Multispectral Optoacoustic Tomography (MSOT) uniquely enables spatial mapping in high resolution of oxygen saturation (SO$_2$), with potential applications in studying pathological complications and therapy efficacy. MSOT offers seamless integration with ultrasonography, by using a common ultrasound detector array. However, MSOT relies on multiple successive acquisitions of optoacoustic (OA) images at different optical wavelengths and the low frame rate of OA imaging makes the MSOT acquisition sensitive to body/respiratory motion. Moreover, estimation of SO$_2$ is highly sensitive to noise, and artefacts related to the respiratory motion of the animal were identified as the primary source of noise in MSOT.In this work, we propose a two-step image processing method for SO$_2$ estimation in deep tissues. First, to mitigate motion artefacts, we propose a method of selection of OA images acquired only during the respiratory pause of the animal, using ultrafast ultrasound images (USIs) acquired immediately after each OA acquisition (USI acquisition duration of 1.4 ms and a total delay of 7 ms). We show that gating is more effective using USIs than OA images at different optical wavelengths. Secondly, we propose a novel method which can estimate directly the SO$_2$ value of a pixel and at the same time evaluate the amount of noise present in that pixel. Hence, the method can efficiently eliminate the pixels dominated by noise from the final SO$_2$ map. Our post-processing method is shown to outperform conventional methods for SO$_2$ estimation, and the method was validated by in vivo oxygen challenge experiments.
摘要
多спектраль光学听音成像（MSOT）可以在高分辨率内创造空间图像，涉及到维生素浓度（SO2）的可能应用。MSOT通过使用共同的ultrasound detector array与ultrasound imaging（USI）集成，但MSOT需要多个顺序的optoacoustic（OA）图像获取，以及OA成像的低帧率，使得MSOT获取敏感于生物/呼吸动的运动。此外，SO2的估计受到噪声的影响，而动物呼吸动的噪声是主要的噪声来源。在这种情况下，我们提出了一种两步图像处理方法，以提高SO2估计的精度。第一步是避免噪声影响，我们提出了使用动物呼吸动时暂停的OA图像选择方法，使用ultrafast ultrasound images（USIs），USIs的获取时间为1.4毫秒，总延迟为7毫秒。我们显示，使用USIs比使用不同的光学波长的OA图像更有效。第二步是直接计算每个像素的SO2值，同时评估每个像素中的噪声量。因此，该方法可以高效地从SO2地图中除掉受噪声影响的像素。我们的后处理方法比传统的SO2估计方法更高效，并通过在生物体内进行氧气挑战实验进行验证。
paper_authors: Nil Stolt-Ansó, Julian McGinnis, Jiazhen Pan, Kerstin Hammernik, Daniel Rueckert for:* 这种新的图像分割模型是用于自动化医疗测量的重要工具。methods:* 该模型使用了神经网络学习一种叫做神经隐函数的新技术，可以在高维连续空间中分割生物学形态。results:* 在使用UK Biobank数据集进行3D+t短轴心脏分割任务时，该模型达到了0.87±0.045的 dice分数。Here is the same information in Simplified Chinese:for:* 这种新的图像分割模型是用于自动化医疗测量的重要工具。methods:* 该模型使用了神经网络学习一种叫做神经隐函数的新技术，可以在高维连续空间中分割生物学形态。results:* 在使用UK Biobank数据集进行3D+t短轴心脏分割任务时，该模型达到了0.87±0.045的 dice分数。Abstract
Segmentation of anatomical shapes from medical images has taken an important role in the automation of clinical measurements. While typical deep-learning segmentation approaches are performed on discrete voxels, the underlying objects being analysed exist in a real-valued continuous space. Approaches that rely on convolutional neural networks (CNNs) are limited to grid-like inputs and not easily applicable to sparse or partial measurements. We propose a novel family of image segmentation models that tackle many of CNNs' shortcomings: Neural Implicit Segmentation Functions (NISF). Our framework takes inspiration from the field of neural implicit functions where a network learns a mapping from a real-valued coordinate-space to a shape representation. NISFs have the ability to segment anatomical shapes in high-dimensional continuous spaces. Training is not limited to voxelized grids, and covers applications with sparse and partial data. Interpolation between observations is learnt naturally in the training procedure and requires no post-processing. Furthermore, NISFs allow the leveraging of learnt shape priors to make predictions for regions outside of the original image plane. We go on to show the framework achieves dice scores of 0.87 $\pm$ 0.045 on a (3D+t) short-axis cardiac segmentation task using the UK Biobank dataset. We also provide a qualitative analysis on our frameworks ability to perform segmentation and image interpolation on unseen regions of an image volume at arbitrary resolutions.
摘要
Segmentation of anatomical shapes from medical images has become increasingly important in the automation of clinical measurements. While traditional deep-learning segmentation methods operate on discrete voxels, the objects being analyzed exist in a real-valued continuous space. Approaches based on convolutional neural networks (CNNs) are limited to grid-like inputs and are not easily applicable to sparse or partial measurements. We propose a novel family of image segmentation models that address many of the shortcomings of CNNs: Neural Implicit Segmentation Functions (NISF). Our framework takes inspiration from the field of neural implicit functions, where a network learns a mapping from a real-valued coordinate space to a shape representation. NISFs can segment anatomical shapes in high-dimensional continuous spaces, and training is not limited to voxelized grids, covering applications with sparse and partial data. Interpolation between observations is learned naturally during the training process and requires no post-processing. Furthermore, NISFs allow the leveraging of learned shape priors to make predictions for regions outside of the original image plane. We demonstrate the effectiveness of our framework on a 3D+t short-axis cardiac segmentation task using the UK Biobank dataset, achieving dice scores of 0.87 ± 0.045. We also provide a qualitative analysis of our framework's ability to perform segmentation and image interpolation on unseen regions of an image volume at arbitrary resolutions.
results: 实验和数据示出，使用 FCS 可以获得3-5dB的PSNR提升，比1D Cartesian random sampling和radial under-sampling更高。Abstract
This paper introduces a sparse projection matrix composed of discrete (digital) periodic lines that create a pseudo-random (p.frac) sampling scheme. Our approach enables random Cartesian sampling whilst employing deterministic and one-dimensional (1D) trajectories derived from the discrete Radon transform (DRT). Unlike radial trajectories, DRT projections can be back-projected without interpolation. Thus, we also propose a novel reconstruction method based on the exact projections of the DRT called finite Fourier reconstruction (FFR). We term this combined p.frac and FFR strategy Finite Compressive Sensing (FCS), with image recovery demonstrated on experimental and simulated data; image quality comparisons are made with Cartesian random sampling in 1D and two-dimensional (2D), as well as radial under-sampling in a more constrained experiment. Our experiments indicate FCS enables 3-5dB gain in peak signal-to-noise ratio (PSNR) for 2-, 4- and 8-fold under-sampling compared to 1D Cartesian random sampling. This paper aims to: Review common sampling strategies for compressed sensing (CS)-magnetic resonance imaging (MRI) to inform the motivation of a projective and Cartesian sampling scheme. Compare the incoherence of these sampling strategies and the proposed p.frac. Compare reconstruction quality of the sampling schemes under various reconstruction strategies to determine the suitability of p.frac for CS-MRI. It is hypothesised that because p.frac is a highly incoherent sampling scheme, that reconstructions will be of high quality compared to 1D Cartesian phase-encode under-sampling.
摘要
This paper aims to:1. Review common sampling strategies for compressed sensing (CS)-magnetic resonance imaging (MRI) to inform the motivation of a projective and Cartesian sampling scheme.2. Compare the incoherence of these sampling strategies and the proposed p.frac.3. Compare reconstruction quality of the sampling schemes under various reconstruction strategies to determine the suitability of p.frac for CS-MRI.It is hypothesized that because p.frac is a highly incoherent sampling scheme, that reconstructions will be of high quality compared to 1D Cartesian phase-encode under-sampling.