Fast Tomographic Alignment for Joint Ptychography and Tomography
Azat M. Slyamov, Viktor Nikitin, Doğa Gürsoy, Rajmund Mokso, Jens W. Andreasen
备注：
People who performed the data acquisition are not listed as coauthors. This discussion will require some additional time and the Replacement option is unfortunately not applicable to our case. Immediate change in the author list may result in inappropriate situation and we have to use the Withdrawal option until the consensus is achieved
Joint ptychography and tomography (JPT) is a recently developed framework that enables high-resolution reconstruction of 3D volumes with significantly relaxed constraints on probe overlap at adjacent scan positions. In ptychographic X-ray computed tomography (PXCT) experiment scanning translations are controlled with high-precision piezo-stages, while run-out errors of rotational stages are corrected by aligning phase-retrieved projections. However, such projections are by definition not available for JPT and would thus require precise knowledge of the angular positioning of the sample, which is prohibitively limited by hardware precision. Here, we present a method for correcting the misalignment of ptychographic tomography data with respect to the rotation axis without having to retrieve individual phase projections. This will facilitate development and application of JPT in providing fast data-efficient nanotomography experiments.
In order to determine the 3D structure of a thick sample, researchers have recently combined ptychography (for high resolution) and tomography (for 3D imaging) in a single experiment. 2-step methods are usually adopted for reconstruction, where the ptychography and tomography problems are often solved independently. In this paper, we provide a novel model and ADMM-based algorithm to jointly solve the ptychography-tomography problem iteratively, also employing total variation regularization. The proposed method permits large scan stepsizes for the ptychography experiment, requiring less measure…
Two features desired in a three-dimensional (3D) optical tomographic image reconstruction algorithm are the ability to reduce imaging artifacts and to do fast processing of large data volumes. Traditional iterative inversion algorithms are impractical in this context due to their heavy computational and memory requirements. We propose and experimentally validate a novel scalable iterative mini-batch algorithm (SIMBA) for fast and high-quality optical tomographic imaging. SIMBA enables high-quality imaging by combining two complementary information sources: the physics of the imaging system cha…
Optical tomographic cross-sectional images of biological samples were made possible by interferometric imaging techniques such as Optical Coherence Tomography (OCT). Owing to its unprecedented view of the sample, OCT has become a gold standard, namely for human retinal imaging in the clinical environment. In this Letter, we present Optical Incoherence Tomography (OIT): a completely digital method extending the possibility to generate tomographic retinal cross-sections to non-interferometric imaging systems such as en-face AO-ophthalmoscopes. We demonstrate that OIT can be applied to different …
In tomographic reconstruction, the image quality of the reconstructed images can be significantly degraded by defects in the measured two-dimensional (2D) raw image data. Despite the importance of screening defective 2D images for robust tomographic reconstruction, manual inspection and rule-based automation suffer from low-throughput and insufficient accuracy, respectively. Here, we present deep learning-enabled quality control for holographic data to produce robust and high-throughput optical diffraction tomography (ODT). The key idea is to distill the knowledge of an expert into a deep conv…
Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography has been widely used for in situ combustion diagnostics, yielding images of both species concentration and temperature. The temperature image is generally obtained from the reconstructed absorbance distributions for two spectral transitions, i.e. two-line thermometry. However, the inherently ill-posed nature of tomographic data inversion leads to noise in each of the reconstructed absorbance distributions. These noise effects propagate into the absorbance ratio and generate artefacts in the retrieved temperature image. To address t…
Massachusetts Institute of Technology（麻省理工学院）；qiTech, Inc.（qiTech公司）
We propose a noise-resilient deep reconstruction algorithm for X-ray tomography. Our approach shows strong noise resilience without obtaining noisy training examples. The advantages of our framework may further enable low-photon tomographic imaging.
Image based diagnostics are interpreted in the context of spatial resolution. The same is true for tomographic image reconstruction. Current empirically driven approaches to quantify spatial resolution rely on a deterministic formulation based on point-spread functions which neglect the statistical prior information, that is integral to rank-deficient tomography. We propose a statistical spatial resolution measure based on the covariance of the reconstruction (point estimate) and show that the prior information acts as a lower limit for the spatial resolution. Furthermore, the spatial resoluti…
Gaussian process tomography (GPT) is a method used for obtaining real-time tomographic reconstructions of the plasma emissivity profile in a tokamak, given some model for the underlying physical processes involved. GPT can also be used, thanks to Bayesian formalism, to perform model selection -- i.e., comparing different models and choosing the one with maximum evidence. However, the computations involved in this particular step may become slow for data with high dimensionality, especially when comparing the evidence for many different models. Using measurements collected by the ASDEX Upgrade …
Novosibirsk State University（新西伯利亚国立大学）；International Tomography Center SB RAS（俄罗斯科学院西伯利亚分院国际断层成像中心）；Sobolev Institute of Mathematics SB RAS（俄罗斯科学院西伯利亚分院索博列夫数学研究所）
This paper presents an automatic algorithm for the segmentation of areas affected by an acute stroke on the non-contrast computed tomography brain images. The proposed algorithm is designed for learning in a weakly supervised scenario when some images are labeled accurately, and some images are labeled inaccurately. Wrong labels appear as a result of inaccuracy made by a radiologist in the process of manual annotation of computed tomography images. We propose methods for solving the segmentation problem in the case of inaccurately labeled training data. We use the U-Net neural network architec…
Institute of Optical Materials and Technologies, Bulgarian Academy of Sciences（保加利亚科学院光学材料与技术研究所）；Aligarh Muslim University（阿里格尔穆斯林大学）；Institute of Mechanics, Bulgarian Academy of Sciences（保加利亚科学院力学研究所）；Scientific and Technological Center of Unique Instrumentation, Russian Academy of Sciences（俄罗斯科学院独特仪器科学技术中心）
Deep-learning based segmentation model is proposed for Optical Coherence Tomography images of human varicose vein based on the U-Net model employing atrous convolution with residual blocks, which gives an accuracy of 0.9932.
We present a Machine Learning-based method for tomographic reconstruction of dense layered objects, with range of projection angles limited to $\pm $10$^\circ$. Whereas previous approaches to phase tomography generally require two steps, first to retrieve phase projections from intensity projections and then perform tomographic reconstruction on the retrieved phase projections, in our work a physics-informed pre-processor followed by a Deep Neural Network (DNN) conduct the three-dimensional reconstruction directly from the intensity projections. We demonstrate this single-step method experimen…
AI-driven projection tomography with multicore fibre-optic cell rotation
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；TU Dresden（德累斯顿工业大学）；Max Planck Institute for the Science of Light（马克斯·普朗克光科学研究所）；Max Planck-Zentrum für Physik und Medizin（马克斯·普朗克物理与医学中心）
IIT Bombay（孟买印度理工学院）；Monash University（莫纳什大学）；IITB-Monash Research Academy（IITB-莫纳什研究院）
Low-dose tomography is highly preferred in medical procedures for its reduced radiation risk when compared to standard-dose Computed Tomography (CT). However, the lower the intensity of X-rays, the higher the acquisition noise and hence the reconstructions suffer from artefacts. A large body of work has focussed on improving the algorithms to minimize these artefacts. In this work, we propose two new techniques, rescaled non-linear least squares and Poisson-Gaussian convolution, that reconstruct the underlying image making use of an accurate or near-accurate statistical model of the noise in t…
Centrum Wiskunde & Informatica（数学与计算机科学中心）；Utrecht University（乌得勒支大学）；Leiden University（莱顿大学）
We introduce single-shot X-ray tomography that aims to estimate the target image from a single cone-beam projection measurement. This linear inverse problem is extremely under-determined since the measurements are far fewer than the number of unknowns. Moreover, it is more challenging than conventional tomography where a sufficiently large number of projection angles forms the measurements, allowing for a simple inversion process. However, single-shot tomography becomes less severe if the target image is only composed of known shapes. Hence, the shape prior transforms a linear ill-posed image …
While Computerized Tomography (CT) images can help detect disease such as Covid-19, regular CT machines are large and expensive. Cheaper and more portable machines suffer from errors in geometry acquisition that downgrades CT image quality. The errors in geometry can be represented with parameters in the mathematical model for image reconstruction. To obtain a good image, we formulate a nonlinear least squares problem that simultaneously reconstructs the image and corrects for errors in the geometry parameters. We develop an accelerated alternating minimization scheme to reconstruct the image …
