Method for auto-alignment and determination of parameter space in dual-phase grating interferometry.

X-ray dual-phase grating interferometry provides quantitative micro-structural information beyond the optical resolution through its tunable correlation length. Ensuring optimal performance of the set-up requires accurate correlation length estimation and precise alignment of the gratings. This paper presents an automated procedure for determining the complete geometrical parameters of the interferometer set-up with a high degree of precision. The algorithm's effectiveness is then evaluated through a series of experimental tests, illustrating its accuracy and robustness.

Deep-Learning Based Quantification of Bovine Oocyte Quality From Microscopy Images.

The success rate of bovine in vitro embryo reproduction is low and highly dependent on the oocyte quality. The selection of the oocyte to be fertilized is done by the embryologists' visual examination of oocytes. It is time-consuming, subjective, and inconsistent between specialists in the area. In this paper, a semi-automatic solution is proposed to score the quality of an immature oocyte. It consists of a deep learning model to classify oocyte competence. The model was trained and tested with real data, composed of images of immature oocytes and their label of whether they developed into blastocysts after fertilization. To the best of our knowledge, automated bovine oocyte classification was not attempted before, but experimental results show that our proposed solution is more robust and objective than specialists' visual assessment and comparable with other works on human oocytes.Clinical relevance- This establishes a semi-automatic real-time method to score bovine immature oocytes, based on stereo-microscopy images. Our method will significantly reduce the time of in vitro embryo production and its success.

Pixel-wise beam-hardening correction for dark-field signal in X-ray dual-phase grating interferometry.

The dark-field signal provided by X-ray grating interferometry is an invaluable tool for providing structural information beyond the direct spatial resolution and their variations on a macroscopic scale. However, when using a polychromatic source, the beam-hardening effect in the dark-field signal makes the quantitative sub-resolution structural information inaccessible. Especially, the beam-hardening effect in dual-phase grating interferometry varies with spatial location, inter-grating distance, and diffraction order. In this work, we propose a beam-hardening correction algorithm, taking into account all these factors. The accuracy and robustness of the algorithm are then validated by experimental results. This work contributes a necessary step toward accessing small-angle scattering structural information in dual-phase grating interferometry.

Deep Learning Tone-Mapping and Demosaicing for Automotive Vision Systems.

High dynamic range (HDR) imaging technology is increasingly being used in automated driving systems (ADS) for improving the safety of traffic participants in scenes with strong differences in illumination. Therefore, a combination of HDR video, that is video with details in all illumination regimes, and (HDR) object perception techniques that can deal with this variety in illumination is highly desirable. Although progress has been made in both HDR imaging solutions and object detection algorithms in the recent years, they have progressed independently of each other. This has led to a situation in which object detection algorithms are typically designed and constantly improved to operate on 8 bit per channel content. This makes these algorithms not ideally suited for use in HDR data processing, which natively encodes to a higher bit-depth (12 bits/16 bits per channel). In this paper, we present and evaluate two novel convolutional neural network (CNN) architectures that intelligently convert high bit depth HDR images into 8-bit images. We attempt to optimize reconstruction quality by focusing on ADS object detection quality. The first research novelty is to jointly perform tone-mapping with demosaicing by additionally successfully suppressing noise and demosaicing artifacts. The first CNN performs tone-mapping with noise suppression on a full-color HDR input, while the second performs joint demosaicing and tone-mapping with noise suppression on a raw HDR input. The focus is to increase the detectability of traffic-related objects in the reconstructed 8-bit content, while ensuring that the realism of the standard dynamic range (SDR) content in diverse conditions is preserved. The second research novelty is that for the first time, to the best of our knowledge, a thorough comparative analysis against the state-of-the-art tone-mapping and demosaicing methods is performed with respect to ADS object detection accuracy on traffic-related content that abounds with diverse challenging (i.e., boundary cases) scenes. The evaluation results show that the two proposed networks have better performance in object detection accuracy and image quality, than both SDR content and content obtained with the state-of-the-art tone-mapping and demosaicing algorithms.

High-Dynamic-Range Tone Mapping in Intelligent Automotive Systems.

Intelligent driver assistance systems are becoming increasingly popular in modern passenger vehicles. A crucial component of intelligent vehicles is the ability to detect vulnerable road users (VRUs) for an early and safe response. However, standard imaging sensors perform poorly in conditions of strong illumination contrast, such as approaching a tunnel or at night, due to their dynamic range limitations. In this paper, we focus on the use of high-dynamic-range (HDR) imaging sensors in vehicle perception systems and the subsequent need for tone mapping of the acquired data into a standard 8-bit representation. To our knowledge, no previous studies have evaluated the impact of tone mapping on object detection performance. We investigate the potential for optimizing HDR tone mapping to achieve a natural image appearance while facilitating object detection of state-of-the-art detectors designed for standard dynamic range (SDR) images. Our proposed approach relies on a lightweight convolutional neural network (CNN) that tone maps HDR video frames into a standard 8-bit representation. We introduce a novel training approach called detection-informed tone mapping (DI-TM) and evaluate its performance with respect to its effectiveness and robustness in various scene conditions, as well as its performance relative to an existing state-of-the-art tone mapping method. The results show that the proposed DI-TM method achieves the best results in terms of detection performance metrics in challenging dynamic range conditions, while both methods perform well in typical, non-challenging conditions. In challenging conditions, our method improves the detection F2 score by 13%. Compared to SDR images, the increase in F2 score is 49%.

Detailed analysis of the interference patterns measured in lab-based X-ray dual-phase grating interferometry through wave propagation simulation.

In this work, we analyze the interference patterns measured in lab-based dual-phase grating interferometry and for the first time explain the spatial dependencies of the measured interference patterns and the large visibility deviations between the theoretical prediction and the experimental results. To achieve this, a simulator based on wave propagation is developed. This work proves that the experimental results can be simulated with high accuracy by including the effective grating thickness profile induced by the cone-beam geometry, the measured detector response function and a non-ideal grating shape. With the comprehensive understanding of dual-phase grating interferometry, this provides the foundations for a more efficient and accurate algorithm to retrieve sample's structure information, and the realistic simulator is a useful tool for optimizing the set-up.

Correction: Sinchuk et al. Variational and Deep Learning Segmentation of Very-Low-Contrast X-ray Computed Tomography Images of Carbon/Epoxy Woven Composites. Materials 2020, 13, 936.

We would like to change the authors' affiliation on the recent published paper [...].

Exploration of the X-ray Dark-Field Signal in Mineral Building Materials.

Mineral building materials suffer from weathering processes such as salt efflorescence, freeze-thaw cycling, and microbial colonization. All of these processes are linked to water (liquid and vapor) in the pore space. The degree of damage following these processes is heavily influenced by pore space properties such as porosity, pore size distribution, and pore connectivity. X-ray computed micro-tomography (µCT) has proven to be a valuable tool to non-destructively investigate the pore space of stone samples in 3D. However, a trade-off between the resolution and field-of-view often impedes reliable conclusions on the material's properties. X-ray dark-field imaging (DFI) is based on the scattering of X-rays by sub-voxel-sized features, and as such, provides information on the sample complementary to that obtained using conventional µCT. In this manuscript, we apply X-ray dark-field tomography for the first time on four mineral building materials (quartzite, fired clay brick, fired clay roof tile, and carbonated mineral building material), and investigate which information the dark-field signal entails on the sub-resolution space of the sample. Dark-field tomography at multiple length scale sensitivities was performed at the TOMCAT beamline of the Swiss Light Source (Villigen, Switzerland) using a Talbot grating interferometer. The complementary information of the dark-field modality is most clear in the fired clay brick and roof tile; quartz grains that are almost indistinguishable in the conventional µCT scan are clearly visible in the dark-field owing to their low dark-field signal (homogenous sub-voxel structure), whereas the microporous bulk mass has a high dark-field signal. Large (resolved) pores on the other hand, which are clearly visible in the absorption dataset, are almost invisible in the dark-field modality because they are overprinted with dark-field signal originating from the bulk mass. The experiments also showed how the dark-field signal from a feature depends on the length scale sensitivity, which is set by moving the sample with respect to the grating interferometer.

Dose-volume-based evaluation of convolutional neural network-based auto-segmentation of thoracic organs at risk.

Background and purpose: The geometrical accuracy of auto-segmentation using convolutional neural networks (CNNs) has been demonstrated. This study aimed to investigate the dose-volume impact of differences between automatic and manual OARs for locally advanced (LA) and peripherally located early-stage (ES) non-small cell lung cancer (NSCLC). Material and methods: A single CNN was created for automatic delineation of the heart, lungs, main left and right bronchus, esophagus, spinal cord and trachea using 55/10/40 patients for training/validation/testing. Dice score coefficient (DSC) and 95th percentile Hausdorff distance (HD95) were used for geometrical analysis. A new treatment plan based on the auto-segmented OARs was created for each test patient using 3D for ES-NSCLC (SBRT, 3-8 fractions) and IMRT for LA-NSCLC (24-35 fractions). The correlation between geometrical metrics and dose-volume differences was investigated. Results: The average (±1 SD) DSC and HD95 were 0.82 ± 0.07 and 16.2 ± 22.4 mm, while the average dose-volume differences were 0.5 ± 1.5 Gy (ES) and 1.5 ± 2.8 Gy (LA). The geometrical metrics did not correlate with the observed dose-volume differences (average Pearson for DSC: -0.27 ± 0.18 (ES) and -0.09 ± 0.12 (LA); HD95: 0.1 ± 0.3 mm (ES) and 0.2 ± 0.2 mm (LA)). Conclusions: After post-processing, manual adjustments of automatic contours are only needed for clinically relevant OARs situated close to the tumor or within an entry or exit beam e.g., the heart and the esophagus for LA-NSCLC and the bronchi for ES-NSCLC. The lungs do not need to be checked further in detail.

Tunable X-ray dark-field imaging for sub-resolution feature size quantification in porous media.

X-ray computed micro-tomography typically involves a trade-off between sample size and resolution, complicating the study at a micrometer scale of representative volumes of materials with broad feature size distributions (e.g. natural stones). X-ray dark-field tomography exploits scattering to probe sub-resolution features, promising to overcome this trade-off. In this work, we present a quantification method for sub-resolution feature sizes using dark-field tomograms obtained by tuning the autocorrelation length of a Talbot grating interferometer. Alumina particles with different nominal pore sizes (50 nm and 150 nm) were mixed and imaged at the TOMCAT beamline of the SLS synchrotron (PSI) at eighteen correlation lengths, covering the pore size range. The different particles cannot be distinguished by traditional absorption µCT due to their very similar density and the pores being unresolved at typical image resolutions. Nevertheless, by exploiting the scattering behavior of the samples, the proposed analysis method allowed to quantify the nominal pore sizes of individual particles. The robustness of this quantification was proven by reproducing the experiment with solid samples of alumina, and alumina particles that were kept separated. Our findings demonstrate the possibility to calibrate dark-field image analysis to quantify sub-resolution feature sizes, allowing multi-scale analyses of heterogeneous materials without subsampling.

Accelerating in vivo fast spin echo high angular resolution diffusion imaging with an isotropic resolution in mice through compressed sensing.

PURPOSE: Echo planar imaging (EPI) is commonly used to acquire the many volumes needed for high angular resolution diffusion Imaging (HARDI), posing a higher risk for artifacts, such as distortion and deformation. An alternative to EPI is fast spin echo (FSE) imaging, which has fewer artifacts but is inherently slower. The aim is to accelerate FSE such that a HARDI data set can be acquired in a time comparable to EPI using compressed sensing. METHODS: Compressed sensing was applied in either q-space or simultaneously in k-space and q-space, by undersampling the k-space in the phase-encoding direction or retrospectively eliminating diffusion directions for different degrees of undersampling. To test the replicability of the acquisition and reconstruction, brain data were acquired from six mice, and a numerical phantom experiment was performed. All HARDI data were analyzed individually using constrained spherical deconvolution, and the apparent fiber density and complexity metric were evaluated, together with whole-brain tractography. RESULTS: The apparent fiber density and complexity metric showed relatively minor differences when only q-space undersampling was used, but deteriorate when k-space undersampling was applied. Likewise, the tract density weighted image showed good results when only q-space undersampling was applied using 15 directions or more, but information was lost when fewer volumes or k-space undersampling were used. CONCLUSION: It was found that acquiring 15 to 20 diffusion directions with a full k-space and reconstructed using compressed sensing could suffice for a replicable measurement of quantitative measures in mice, where areas near the sinuses and ear cavities are untainted by signal loss.

An interactive ImageJ plugin for semi-automated image denoising in electron microscopy.

The recent advent of 3D in electron microscopy (EM) has allowed for detection of nanometer resolution structures. This has caused an explosion in dataset size, necessitating the development of automated workflows. Moreover, large 3D EM datasets typically require hours to days to be acquired and accelerated imaging typically results in noisy data. Advanced denoising techniques can alleviate this, but tend to be less accessible to the community due to low-level programming environments, complex parameter tuning or a computational bottleneck. We present DenoisEM: an interactive and GPU accelerated denoising plugin for ImageJ that ensures fast parameter tuning and processing through parallel computing. Experimental results show that DenoisEM is one order of magnitude faster than related software and can accelerate data acquisition by a factor of 4 without significantly affecting data quality. Lastly, we show that image denoising benefits visualization and (semi-)automated segmentation and analysis of ultrastructure in various volume EM datasets.

Variational and Deep Learning Segmentation of Very-low-contrast X-ray Computed Tomography Images of Carbon/Epoxy Woven Composites.

The purpose of this work is to find an effective image segmentation method for lab-based micro-tomography (µ-CT) data of carbon fiber reinforced polymers (CFRP) with insufficient contrast-to-noise ratio. The segmentation is the first step in creating a realistic geometry (based on µ-CT) for finite element modelling of textile composites on meso-scale. Noise in X-ray imaging data of carbon/polymer composites forms a challenge for this segmentation due to the very low X-ray contrast between fiber and polymer and unclear fiber gradients. To the best of our knowledge, segmentation of µ-CT images of carbon/polymer textile composites with low resolution data (voxel size close to the fiber diameter) remains poorly documented. In this paper, we propose and evaluate different approaches for solving the segmentation problem: variational on the one hand and deep-learning-based on the other. In the author's view, both strategies present a novel and reliable ground for the segmentation of µ-CT data of CFRP woven composites. The predictions of both approaches were evaluated against a manual segmentation of the volume, constituting our "ground truth", which provides quantitative data on the segmentation accuracy. The highest segmentation accuracy (about 4.7% in terms of voxel-wise Dice similarity) was achieved using the deep learning approach with U-Net neural network.

The Effect of the Color Filter Array Layout Choice on State-of-the-Art Demosaicing.

Interpolation from a Color Filter Array (CFA) is the most common method for obtaining full color image data. Its success relies on the smart combination of a CFA and a demosaicing algorithm. Demosaicing on the one hand has been extensively studied. Algorithmic development in the past 20 years ranges from simple linear interpolation to modern neural-network-based (NN) approaches that encode the prior knowledge of millions of training images to fill in missing data in an inconspicious way. CFA design, on the other hand, is less well studied, although still recognized to strongly impact demosaicing performance. This is because demosaicing algorithms are typically limited to one particular CFA pattern, impeding straightforward CFA comparison. This is starting to change with newer classes of demosaicing that may be considered generic or CFA-agnostic. In this study, by comparing performance of two state-of-the-art generic algorithms, we evaluate the potential of modern CFA-demosaicing. We test the hypothesis that, with the increasing power of NN-based demosaicing, the influence of optimal CFA design on system performance decreases. This hypothesis is supported with the experimental results. Such a finding would herald the possibility of relaxing CFA requirements, providing more freedom in the CFA design choice and producing high-quality cameras.

Reproducibility and intercorrelation of graph theoretical measures in structural brain connectivity networks.

Diffusion-weighted magnetic resonance imaging can be used to non-invasively probe the brain microstructure. In addition, recent advances have enabled the identification of complex fiber configurations present in most of the white matter. This has improved the investigation of structural connectivity with tractography methods. Whole-brain structural connectivity networks, or connectomes, are reconstructed by parcellating the gray matter and performing tractography to determine connectivity between these regions. These complex networks can be analyzed with graph theoretical methods, which measure their global and local properties. However, as these tools have only recently been applied to structural brain networks, there is little information about the reproducibility and intercorrelation of network properties, connectivity weights and fiber tractography reconstruction parameters in the brain. We studied the reproducibility and correlation in structural brain connectivity networks reconstructed with constrained spherical deconvolution based probabilistic streamlines tractography. Diffusion-weighted data from 19 subjects were acquired with b = 2800 s/mm2 and 75 gradient orientations. Intrasubject variability was computed with residual bootstrapping. Our findings indicate that the reproducibility of graph theoretical metrics is generally excellent with the exception of betweenness centrality. A reconstruction density of approximately one million streamlines is necessary for excellent reproducibility, but the reproducibility increases further with higher densities. The reproducibility decreases, but only slightly, when switching to a higher order in constrained spherical deconvolution. Moreover, in binary networks, using sufficiently high threshold values improves the reproducibility. We show that multiple network properties and connectivity weights are highly intercorrelated. The experiments were replicated by using a test-retest dataset of 44 healthy subjects provided by the Human Connectome Project. In conclusion, our results provide guidelines for reproducible investigation of structural brain networks.

Sparse Recovery in Magnetic Resonance Imaging With a Markov Random Field Prior.

Recent research in compressed sensing of magnetic resonance imaging (CS-MRI) emphasizes the importance of modeling structured sparsity, either in the acquisition or in the reconstruction stages. Subband coefficients of typical images show certain structural patterns, which can be viewed in terms of fixed groups (like wavelet trees) or statistically (certain configurations are more likely than others). Wavelet tree models have already demonstrated excellent performance in MRI recovery from partial data. However, much less attention has been given in CS-MRI to modeling statistically spatial clustering of subband data, although the potentials of such models have been indicated. In this paper, we propose a practical CS-MRI reconstruction algorithm making use of a Markov random field prior model for spatial clustering of subband coefficients and an efficient optimization approach based on proximal splitting. The results demonstrate an improved reconstruction performance compared with both the standard CS-MRI methods and the recent related methods.

Image Degradation in Microscopic Images: Avoidance, Artifacts, and Solutions.

The goal of modern microscopy is to acquire high-quality image based data sets. A typical microscopy workflow is set up in order to address a specific biological question and involves different steps. The first step is to precisely define the biological question, in order to properly come to an experimental design for sample preparation and image acquisition. A better object representation allows biological users to draw more reliable scientific conclusions. Image restoration can manipulate the acquired data in an effort to reduce the impact of artifacts (spurious results) due to physical and technical limitations, resulting in a better representation of the object of interest. However, precise usage of these algorithms is necessary so as to avoid further artifacts that might influence the data analysis and bias the conclusions. It is essential to understand image acquisition, and how it introduces artifacts and degradations in the acquired data, so that their effects on subsequent analysis can be minimized. This paper provides an overview of the fundamental artifacts and degradations that affect many micrographs. We describe why artifacts appear, in what sense they impact overall image quality, and how to mitigate them by first improving the acquisition parameters and then applying proper image restoration techniques.

D-BRAIN: Anatomically Accurate Simulated Diffusion MRI Brain Data.

Diffusion Weighted (DW) MRI allows for the non-invasive study of water diffusion inside living tissues. As such, it is useful for the investigation of human brain white matter (WM) connectivity in vivo through fiber tractography (FT) algorithms. Many DW-MRI tailored restoration techniques and FT algorithms have been developed. However, it is not clear how accurately these methods reproduce the WM bundle characteristics in real-world conditions, such as in the presence of noise, partial volume effect, and a limited spatial and angular resolution. The difficulty lies in the lack of a realistic brain phantom on the one hand, and a sufficiently accurate way of modeling the acquisition-related degradation on the other. This paper proposes a software phantom that approximates a human brain to a high degree of realism and that can incorporate complex brain-like structural features. We refer to it as a Diffusion BRAIN (D-BRAIN) phantom. Also, we propose an accurate model of a (DW) MRI acquisition protocol to allow for validation of methods in realistic conditions with data imperfections. The phantom model simulates anatomical and diffusion properties for multiple brain tissue components, and can serve as a ground-truth to evaluate FT algorithms, among others. The simulation of the acquisition process allows one to include noise, partial volume effects, and limited spatial and angular resolution in the images. In this way, the effect of image artifacts on, for instance, fiber tractography can be investigated with great detail. The proposed framework enables reliable and quantitative evaluation of DW-MR image processing and FT algorithms at the level of large-scale WM structures. The effect of noise levels and other data characteristics on cortico-cortical connectivity and tractography-based grey matter parcellation can be investigated as well.

Bayesian deconvolution of scanning electron microscopy images using point-spread function estimation and non-local regularization.

Microscopy is one of the most essential imaging techniques in life sciences. High-quality images are required in order to solve (potentially life-saving) biomedical research problems. Many microscopy techniques do not achieve sufficient resolution for these purposes, being limited by physical diffraction and hardware deficiencies. Electron microscopy addresses optical diffraction by measuring emitted or transmitted electrons instead of photons, yielding nanometer resolution. Despite pushing back the diffraction limit, blur should still be taken into account because of practical hardware imperfections and remaining electron diffraction. Deconvolution algorithms can remove some of the blur in post-processing but they depend on knowledge of the point-spread function (PSF) and should accurately regularize noise. Any errors in the estimated PSF or noise model will reduce their effectiveness. This paper proposes a new procedure to estimate the lateral component of the point spread function of a 3D scanning electron microscope more accurately. We also propose a Bayesian maximum a posteriori deconvolution algorithm with a non-local image prior which employs this PSF estimate and previously developed noise statistics. We demonstrate visual quality improvements and show that applying our method improves the quality of subsequent segmentation steps.

The effect of Gibbs ringing artifacts on measures derived from diffusion MRI.

Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a unique method to investigate microstructural tissue properties noninvasively and is one of the most popular methods for studying the brain white matter in vivo. To obtain reliable statistical inferences with diffusion MRI, however, there are still many challenges, such as acquiring high-quality DW-MRI data (e.g., high SNR and high resolution), careful data preprocessing (e.g., correcting for subject motion and eddy current induced geometric distortions), choosing the appropriate diffusion approach (e.g., diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), or diffusion spectrum MRI), and applying a robust analysis strategy (e.g., tractography based or voxel based analysis). Notwithstanding the numerous efforts to optimize many steps in this complex and lengthy diffusion analysis pipeline, to date, a well-known artifact in MRI--i.e., Gibbs ringing (GR)--has largely gone unnoticed or deemed insignificant as a potential confound in quantitative DW-MRI analysis. Considering the recent explosion of diffusion MRI applications in biomedical and clinical applications, a systematic and comprehensive investigation is necessary to understand the influence of GR on the estimation of diffusion measures. In this work, we demonstrate with simulations and experimental DW-MRI data that diffusion estimates are significantly affected by GR artifacts and we show that an off-the-shelf GR correction procedure based on total variation already can alleviate this issue substantially.