An improved ring removal procedure for in-line x-ray phase contrast tomography

Deep-learning-based ring artifact correction for tomographic reconstruction

X-ray tomography has been widely used in various research fields thanks to its capability of observing 3D structures with high resolution non-destructively. However, due to the nonlinearity and inconsistency of detector pixels, ring artifacts usually appear in tomographic reconstruction, which may compromise image quality and cause nonuniform bias. This study proposes a new ring artifact correction method based on the residual neural network (ResNet) for X-ray tomography. The artifact correction network uses complementary information of each wavelet coefficient and a residual mechanism of the residual block to obtain high-precision artifacts through low operation costs. In addition, a regularization term is used to accurately extract stripe artifacts in sinograms, so that the network can better preserve image details while accurately separating artifacts. When applied to simulation and experimental data, the proposed method shows a good suppression of ring artifacts. To solve the problem of insufficient training data, ResNet is trained through the transfer learning strategy, which brings advantages of robustness, versatility and low computing cost.

Ring artifact and Poisson noise attenuation via volumetric multiscale nonlocal collaborative filtering of spatially correlated noise

X-ray micro-tomography systems often suffer from high levels of noise. In particular, severe ring artifacts are common in reconstructed images, caused by defects in the detector, calibration errors, and fluctuations producing streak noise in the raw sinogram data. Furthermore, the projections commonly contain high levels of Poissonian noise arising from the photon-counting detector. This work presents a 3-D multiscale framework for streak attenuation through a purposely designed collaborative filtering of correlated noise in volumetric data. A distinct multiscale denoising step for attenuation of the Poissonian noise is further proposed. By utilizing the volumetric structure of the projection data, the proposed fully automatic procedure offers improved feature preservation compared with 2-D denoising and avoids artifacts which arise from individual filtering of sinograms.

Improving reproducibility in synchrotron tomography using implementation-adapted filters

For reconstructing large tomographic datasets fast, filtered backprojection-type or Fourier-based algorithms are still the method of choice, as they have been for decades. These robust and computationally efficient algorithms have been integrated in a broad range of software packages. The continuous mathematical formulas used for image reconstruction in such algorithms are unambiguous. However, variations in discretization and interpolation result in quantitative differences between reconstructed images, and corresponding segmentations, obtained from different software. This hinders reproducibility of experimental results, making it difficult to ensure that results and conclusions from experiments can be reproduced at different facilities or using different software. In this paper, a way to reduce such differences by optimizing the filter used in analytical algorithms is proposed. These filters can be computed using a wrapper routine around a black-box implementation of a reconstruction algorithm, and lead to quantitatively similar reconstructions. Use cases for this approach are demonstrated by computing implementation-adapted filters for several open-source implementations and applying them to simulated phantoms and real-world data acquired at the synchrotron. Our contribution to a reproducible reconstruction step forms a building block towards a fully reproducible synchrotron tomography data processing pipeline.

X-ray Phase Contrast Tomography Serves Preclinical Investigation of Neurodegenerative Diseases

We report a qualitative study on central nervous system (CNS) damage that demonstrates the ability of X-ray phase contrast tomography (XPCT) to confirm data obtained with standard 2D methodology and permits the description of additional features that are not detected with 2D or other 3D techniques. In contrast to magnetic resonance or computed tomography, XPCT makes possible the high-resolution 3D imaging of soft tissues classically considered "invisible" to X-rays without the use of additional contrast agents, or without the need for intense processing of the tissue required by 2D techniques. Most importantly for studies of CNS diseases, XPCT enables a concomitant multi-scale 3D biomedical imaging of neuronal and vascular networks ranging from cells through to the CNS as a whole. In the last years, we have used XPCT to investigate neurodegenerative diseases, such as Alzheimer's disease (AD) and multiple sclerosis (MS), to shed light on brain damage and extend the observations obtained with standard techniques. Here, we show the cutting-edge ability of XPCT to highlight in 3D, concomitantly, vascular occlusions and damages, close associations between plaques and damaged vessels, as well as dramatic changes induced at neuropathological level by treatment in AD mice. We corroborate data on the well-known blood-brain barrier dysfunction in the animal model of MS, experimental autoimmune encephalomyelitis, and further show its extent throughout the CNS axis and at the level of the single vessel/capillary.

Assessment of plaque morphology in Alzheimer's mouse cerebellum using three-dimensional X-ray phase-based virtual histology

Visualization and characterization of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β-amyloid deposits is a fundamental task in pre-clinical study of Alzheimer’s disease (AD) to assess its evolution and monitor the efficiency of new therapeutic strategies. While the cerebellum is one of the brain areas most underestimated in the context of AD, renewed interest in cerebellar lesions has recently arisen as they may link to motor and cognitive alterations. Thus, we quantitatively investigated three-dimensional plaque morphology in the cerebellum in APP/PS1 transgenic mouse, as a model of AD. In order to obtain a complete high-resolution three-dimensional view of the investigated tissue, we exploited synchrotron X-ray phase contrast tomography (XPCT), providing virtual slices with histology-matching resolution. We found the formation of plaques elongated in shape, and with a specific orientation in space depending on the investigated region of the cerebellar cortex. Remarkably, a similar shape is observed in human cerebellum from demented patients. Our findings demonstrate the capability of XPCT in volumetric quantification, supporting the current knowledge about plaque morphology in the cerebellum and the fundamental role of the surrounding tissue in driving their evolution. A good correlation with the human neuropathology is also reported.

X-ray phase contrast tomography for the investigation of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder affecting motor neurons. Pre-clinical studies drive the development of animal models that well mimic ALS disorder and enable both the dissection of disease processes and an early assessment of therapy efficacy. A comprehensive knowledge of neuronal and vascular lesions in the brain and spinal cord is an essential factor to understand the development of the disease. Spatial resolution and bidimensional imaging are important drawbacks limiting current neuroimaging tools, while neuropathology relies on protocols that may alter tissue chemistry and structure. In contrast, recent ex vivo studies in mice demonstrated that X-ray phase-contrast tomography enables study of the 3D distribution of both vasculature and neuronal networks, without sample sectioning or use of staining. Here we present our findings on ex vivo SOD1G93A ALS mice spinal cord at a micrometric scale. An unprecedented direct quantification of neuro-vascular alterations at different stages of the disease is shown.

High resolution 3D visualization of the spinal cord in a post-mortem murine model

A crucial issue in the development of therapies to treat pathologies of the central nervous system is represented by the availability of non-invasive methods to study the three-dimensional morphology of spinal cord, with a resolution able to characterize its complex vascular and neuronal organization. X-ray phase contrast micro-tomography enables a high-quality, 3D visualization of both the vascular and neuronal network simultaneously without the need of contrast agents, destructive sample preparations or sectioning. Until now, high resolution investigations of the post-mortem spinal cord in murine models have mostly been performed in spinal cords removed from the spinal canal. We present here post-mortem phase contrast micro-tomography images reconstructed using advanced computational tools to obtain high-resolution and high-contrast 3D images of the fixed spinal cord without removing the bones and preserving the richness of micro-details available when measuring exposed spinal cords. We believe that it represents a significant step toward the in-vivo application.

Ring artifact suppression in X-ray computed tomography using a simple, pixel-wise response correction

We present a pixel-specific, measurement-driven correction that effectively reduces errors in detector response that give rise to the ring artifacts commonly seen in X-ray computed tomography (CT) scans. This correction is easy to implement, suppresses CT artifacts significantly, and is effective enough for use with both absorption and phase contrast imaging. It can be used as a standalone correction or in conjunction with existing ring artifact removal algorithms to further improve image quality. We validate this method using two X-ray CT data sets acquired using monochromatic sources, showing post-correction signal-to-noise increases of up to 55%, and we define an image quality metric to use specifically for the assessment of ring artifact suppression.