Processing sequence to analyse 3D THz images

In this paper we present a new processing sequence to perform (i) tomographic reconstruction, (ii) automated segmentation, (iii) component labelling and (iv) accurate measurements of an object from its 3D THz acquisition. This sequence, implemented in a new 3D THz imaging software2, is validated through the analysis of several samples

Investigation on Encoder-Decoder Networks for Segmentation of Very Degraded X-Ray CT Tomograms

Field of View (FOV) Nano-CT X-Ray synchrotron imaging is used for acquiring brain neuronal features from Golgi-stained bio-samples. It theoretically requires a large number of acquired data for compensating CT recon struction noise and artefacts (both reinforced by the sparsity of brain features). However reducing the number of radiographs is essential in routine applications but it results to degraded tomograms. In such a case, traditional segmentation techniques are no longer able to distinguish neuronal structures from surrounding noise. Thus, we investigate several deep-learning networks to segment brain features from very degraded tomograms. We focus on encoder-decoder networks and define new ones addressing specifically our application. We demonstrate that some networks wildly outperform traditional segmentation and discuss the superiority of the proposed networks

Bayesian approach to time-resolved tomography

Conventional X-ray micro-computed tomography (μCT) is unable to meet the need for real-time, high-resolution, time-resolved imaging of multi-phase fluid flow. High signal-to-noise-ratio (SNR) data acquisition is too slow and results in motion artefacts in the images, while fast acquisition is too noisy and results in poor image contrast. We present a Bayesian framework for time-resolved tomography that uses priors to drastically reduce the required amount of experiment data. This enables high-quality time-resolved imaging through a data acquisition protocol that is both rapid and high SNR. Here we show that the framework: (i) encompasses our previous, algorithms for imaging two-phase flow as limiting cases; (ii) produces more accurate results from imperfect (i.e. real) data, where it can be compared to our previous work; and (iii) is generalisable to previously intractable systems, such as three-phase flow

Sinogram Restoration Using Confidence Maps to Reduce Metal Artifact in Computed Tomography

International audienceMetal artifact reduction (MAR) is a well-known problem and lots of studies have been performed during the last decades. The common standard methods for MAR consist of synthesizing missing projection data by using an interpolation or in-painting process. However, no method has been yet proposed to solve MAR problem when no sinogram is available. This paper proposes a novel MAR approach using confidence maps to restore an artifacted sinogram computed directly from the reconstructed image

Multi-resolution radiograph alignment for motion correction in x-ray micro-tomography

Achieving sub-micron resolution in lab-based micro-tomography is challenging due to the geometric instability of the imaging hardware (spot drift, stage precision, sample motion). These instabilities manifest themselves as a distortion or motion of the radiographs relative to the expected system geometry. When the hardware instabilities are small (several microns of absolute motion), the radiograph distortions are well approximated by shift and magnification of the image. In this paper we examine the use of re-projection alignment (RA) to estimate per-radiograph motions. Our simulation results evaluate how the convergence properties of RA vary with: motion-type (smooth versus random), trajectory (helical versus space-filling) and resolution. We demonstrate that RA convergence rate and accuracy, for the space-filling trajectory, is invariant with regard to the motion-type. In addition, for the space-filling trajectory, the per-projection motions can be estimated to less than 0.25 pixel mean absolute error by performing a single quarter-resolution RA iteration followed by a single half-resolution RA iteration. The direct impact is that, for the space-filling trajectory, we need only perform one RA iteration per resolution in our iterative multi-grid reconstruction (IMGR). We also give examples of the effectiveness of RA motion correction method applied to real double-helix and space-filling trajectory micro-CT data. For double-helix Katsevich filtered-back-projection reconstruction (≈2500×2500×5000 voxels), we use a multi-resolution RA method as a pre-processing step. For the space-filling iterative reconstruction (≈2000×2000×5400 voxels), RA is applied during the IMGR iterations.This research was also supported under Australian Research Council's Linkage Project funding scheme (project number LP150101040) with partner organisation FEI. Associate Professor Adrian Sheppard is the recipient of an Australian Research Council Future Fellowship (project number FT100100470)

Reprojection Alignment for Trajectory Perturbation Estimation in Microtomography

For standard laboratory microtomography systems, acquired radiographs do not always adhere to the strict geometrical assumptions of the reconstruction algorithm. The consequence of this geometrical inconsistency is that the reconstructed tomogram contains motion artifacts, e.g., blurring, streaking, double-edges. To achieve a motion-artifact-free tomographic reconstruction, one must estimate, and subsequently correct for, the per-radiograph experimental geometry parameters. In this paper, we examine the use of re-projection alignment (RA) to estimate per-radiograph geometry. Our simulations evaluate how the convergence properties of RA vary with: motion-type (smooth versus random), trajectory (helical versus discrete-sampling `space-filling' trajectories) and tomogram resolution. The idealized simulations demonstrate for the space-filling trajectory that RA convergence rate and accuracy is invariant with regard to the motion-type and that the per-projection motions can be estimated to less than 0.25 pixel mean absolute error by performing a single quarter-resolution RA iteration followed by a single half-resolution RA iteration. The direct impact is that, for the space-filling trajectory, one can incorporate RA in an iterative multi-grid reconstruction scheme with only a single RA iteration per multi-grid resolution step. We also find that for either trajectory, slowly varying vertical errors cannot be reliably estimated by employing the RA method alone; such errors are indistinguishable from a trajectory of different pitch. This has minimal effect in practice because RA can be combined with reference frame correction which is effective for correcting low-frequency errors

Building a bone μ-CT images atlas for micro-architecture recognition

Trabecular bone and its micro-architecture are of prime importance for health. Changes of bone micro-architecture are linked to different pathological situations like osteoporosis and begin now to be understood. In a previous paper, we started to investigate the relationships between bone and vessels and we also proposed to build a Bone Atlas. This study describes how to proceed for the elaboration and use of such an atlas. Here, we restricted the Atlas to legs (tibia, femur) of rats in order to work with well known geometry of the bone micro-architecture. From only 6 acquired bone, 132 trabecular bone volumes were generated using simple mathematical morphology tools. The variety and veracity of the created micro-architecture volumes is presented in this paper. Medical application and final goal would be to determinate bone micro-architecture with some angulated radiographs (3 or 4) and to easily diagnose the bone status (healthy, pathological or healing bone⋯)

Connected-Components-based Post-processing for Retinal Vessels Deep-Learning Segmentation

The analysis of fundus images may reflect systemic and cerebral vascular status through a non-invasive, rapid, and cost-effective method. Accurate characterization of the retinal vessels is critical for this status assessment. Medical professionals can perform diagnosis on measurements extracted from the retinal vessels, which are identified through segmentation. Supervised-Learning is used to perform this segmentation task and has been shown to produce higher-quality results compared to traditional methods. However, the Supervised-Learning-based binary method leads to segmentations with multiple Connected Components (CC). Amongst these components, some are disconnected retinal vessels (mentioned as branches), others are artifacts. Artifacts are disconnected miss-classified components resulting from the Supervised-Learning segmentation and that should be removed. Conversely, branches should be kept and further re-connected as they are anatomically supposed to be connected. In this study, we propose a Connected-Components-based post-processing procedure to remove artifacts while preserving the most possible amount of branches. Our methodology involves a relative threshold to cluster the CC based on their areas. We also introduce a useful evaluation metric for the segmentations in the case of measurements extractions on retinal vessels. Over 615 predicted segmentations from six datasets, we improved the dice by a substantial 0.062 leading from 0.782 to 0.844. In conclusion, our method has the potential to significantly enhance the usability and reliability of retinal vessels segmentations, making it a valuable tool for medical professionals in the assessment of systemic and cerebral vascular status. Our work also provides useful insights for future research in this area, especially to address the re-connection of the remaining branches