results: 提出了一种基于非几何最小化的图像恢复算法，可以有效地提高多光子镜 microscope 图像质量，并且可以适应不同的背景噪声。Abstract
In multi-photon microscopy (MPM), a recent in-vivo fluorescence microscopy system, the task of image restoration can be decomposed into two interlinked inverse problems: firstly, the characterization of the Point Spread Function (PSF) and subsequently, the deconvolution (i.e., deblurring) to remove the PSF effect, and reduce noise. The acquired MPM image quality is critically affected by PSF blurring and intense noise. The PSF in MPM is highly spread in 3D and is not well characterized, presenting high variability with respect to the observed objects. This makes the restoration of MPM images challenging. Common PSF estimation methods in fluorescence microscopy, including MPM, involve capturing images of sub-resolution beads, followed by quantifying the resulting ellipsoidal 3D spot. In this work, we revisit this approach, coping with its inherent limitations in terms of accuracy and practicality. We estimate the PSF from the observation of relatively large beads (approximately 1$\mu$m in diameter). This goes through the formulation and resolution of an original non-convex minimization problem, for which we propose a proximal alternating method along with convergence guarantees. Following the PSF estimation step, we then introduce an innovative strategy to deal with the high level multiplicative noise degrading the acquisitions. We rely on a heteroscedastic noise model for which we estimate the parameters. We then solve a constrained optimization problem to restore the image, accounting for the estimated PSF and noise, while allowing a minimal hyper-parameter tuning. Theoretical guarantees are given for the restoration algorithm. These algorithmic contributions lead to an end-to-end pipeline for 3D image restoration in MPM, that we share as a publicly available Python software. We demonstrate its effectiveness through several experiments on both simulated and real data.
摘要
multi-photon microscopy (MPM) 是一种最近的生物学内穿镜系统，其图像修复问题可以分解为两个相互关联的反问题：首先， Characterization of the Point Spread Function (PSF) ，然后，使用这个 PSF 来消除锐化的效果和噪声，提高图像质量。 MPM 图像质量受到 PSF 的抖振和强噪声的影响，PSF 在 MPM 中是非常扩散的，对观察对象的变化具有高度不确定性，这使得 MPM 图像修复变得困难。通常在激发谱镜中，PSF 估计方法包括通过捕捉小于分辨率的凝胶小球的图像，然后根据得到的三维椭球来估计 PSF。在这种方法中，我们改进了这种方法，解决其内在的精度和实用性问题。我们通过观察大约1微米的凝胶小球来估计 PSF。这个过程通过非几何最小化问题的形式ulation和解决，我们提议了一种距离逼近法，并提供了收敛保证。在 PSF 估计步骤后，我们引入了一种创新的噪声处理策略，利用一种不同噪声模型，其中我们估计噪声参数。然后，我们解决了一个受限制的优化问题，以修复图像，考虑到估计的 PSF 和噪声，同时允许最小化hyperparameter tuning。我们提供了对修复算法的理论保证。这些算法贡献导致了一个端到端的图像修复管道，我们将其作为一个公共可用的 Python 软件分享。我们通过一些实验，证明了它的有效性，包括both simulated 和实际数据。
Material decomposition for dual-energy propagation-based phase-contrast CT
methods: 这篇论文提出了一种新的迭代方法，通过将phaserecovery、重建和物质分解合并到一起进行，从intensity数据中直接获取物质分解结果，而不需要先进行重建和 then material decomposition。这种方法使用干涉减噪来实现高精度的物质分解和噪声减少。
results: 实验结果表明，相比于传统的两步方法，提出的方法在物质分解和噪声减少方面具有明显的优势。Abstract
Material decomposition refers to using the energy dependence of material physical properties to differentiate materials in a sample, which is a very important application in computed tomography(CT). In propagation-based X-ray phase-contrast CT, the phase retrieval and Reconstruction are always independent. Moreover, like in conventional CT, the material decomposition methods in this technique can be classified into two types based on pre-reconstruction and post-reconstruction (two-step). The CT images often suffer from noise and artifacts in those methods because of no feedback and correction from the intensity data. This work investigates an iterative method to obtain material decomposition directly from the intensity data in different energies, which means that we perform phase retrieval, reconstruction and material decomposition in a one step. Fresnel diffraction is applied to forward propagation and CT images interact with this intensity data throughout the iterative process. Experiments results demonstrate that compared with two-step methods, the proposed method is superior in accurate material decomposition and noise reduction.
摘要
材料分解指的是使用材料物理性能的能量依赖性来 отлича物料在样本中，这是计算机 Tomatoes（CT）中非常重要的应用。在传播基于X射线相对contrast CT中，phaserecovery和重建总是独立的。此外，与传统CT方法一样，这些方法可以根据预重建和后重建（两步）进行分类。CT图像经常受到这些方法中的噪声和artefacts的影响，因为没有反馈和修正来自Intensity数据。这种工作investigates一种迭代法，以直接从Intensity数据中获取材料分解，这意味着我们在一步中完成phaserecovery、重建和材料分解。在迭代过程中，Fresnel diffraction被应用于前向传播，CT图像与这些Intensity数据相互作用。实验结果表明，相比两步方法，我们的方法在精度的材料分解和噪声减少方面表现更优。