Joint multi-contrast CT for edge illumination X-ray phase contrast imaging using split Barzilai-Borwein steps.

Edge illumination (EI) is an X-ray imaging technique that, in addition to conventional absorption contrast, provides refraction and scatter contrast. It relies on an absorption mask in front of the sample that splits the X-ray beam into beamlets, which hits a second absorption mask positioned in front of the detector. The sample mask is then shifted in multiple steps with respect to the detector mask, thereby measuring an illumination curve per detector element. The width, position, and area of this curve estimated with and without the sample in the beam is then compared, which ultimately provides absorption, refraction, and scatter contrast for each detector pixel. From the obtained contrast sinograms, three contrast tomograms can be computed. In summary, conventional EI relies on a two-stage process comprised of a computational and time intensive contrast retrieval process, followed by tomographic reconstruction. In this work, a novel joint reconstruction method is proposed, which utilizes a combined forward model to reconstruct the three contrasts simultaneously, without the need for an intermediate contrast retrieval step. Compared to the state-of-the-art, this approach reduces reconstruction times, as the retrieval step is skipped and allows a much more flexible acquisition scheme, as there is no need to sample a full illumination curve at each projection angle. The proposed method is shown to improve reconstruction quality on subsampled datasets, enabling the reconstruction of three contrasts from single-shot datasets.

CAD-ASTRA: a versatile and efficient mesh projector for X-ray tomography with the ASTRA-toolbox.

Accurate and fast simulation of X-ray projection data from mesh models has many applications in academia and industry, ranging from 3D X-ray computed tomography (XCT) reconstruction algorithms to radiograph-based object inspection and quality control. While software tools for the simulation of X-ray projection data from mesh models are available, they lack either performance, public availability, flexibility to implement non-standard scanning geometries, or easy integration with existing 3D XCT software. In this paper, we propose CAD-ASTRA, a highly versatile toolbox for fast simulation of X-ray projection data from mesh models. While fully functional as standalone software, it is also compatible with the ASTRA toolbox, an open-source toolbox for flexible tomographic reconstruction. CAD-ASTRA provides three specialized GPU projectors based on state-of-the-art algorithms for 3D rendering, implemented using the NVIDIA CUDA Toolkit and the OptiX engine. First, it enables X-ray phase contrast simulations by modeling refraction through ray tracing. Second, it allows the back-propagation of projective errors to mesh vertices, enabling immediate application in mesh reconstruction, deep learning, and other optimization routines. Finally, CAD-ASTRA allows simulation of polychromatic X-ray projections from heterogeneous objects with a source of finite focal spot size. Use cases on a CAD-based inspection task, a phase contrast experiment, a combined mesh-volumetric data projection, and a mesh reconstruction demonstrate the wide applicability of CAD-ASTRA.

Principal component analysis as a tool for determining optimal tibial baseplate geometry in modern TKA design.

Optimal tibial component fixation in total knee arthroplasty (TKA) requires maximal tibial bone coverage, optimized mediolateral cortical fit as well as component rotation. Failure to achieve an optimal fit may result in component subsidence and loosening in case of undersizing, or overhang with subsequent soft tissue impingement in case of overhang. To date there is no consensus on optimal tibial component shape, and significant variability exists among different design manufacturers. In this study "principal component analysis" was used as a statistical tool in order to determine the ideal tibia baseplate shape, based upon anthropometric CT- scan data defining an average proximal tibial shape and variations. Gender specificity was evaluated and differences in geometry depending on anatomic constitution (varus, neutral, valgus) were analyzed. The results from our study indicate that in the arthritic knee differences in proximal tibial morphology at the resection level were mainly attributed to size and not shape. This is true for both Caucasian men and women, and is independent from the anatomical constitution.

Diffusion kurtosis imaging probes cortical alterations and white matter pathology following cuprizone induced demyelination and spontaneous remyelination.

Although MRI is the gold standard for the diagnosis and monitoring of multiple sclerosis (MS), current conventional MRI techniques often fail to detect cortical alterations and provide little information about gliosis, axonal damage and myelin status of lesioned areas. Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) provide sensitive and complementary measures of the neural tissue microstructure. Additionally, specific white matter tract integrity (WMTI) metrics modelling the diffusion in white matter were recently derived. In the current study we used the well-characterized cuprizone mouse model of central nervous system demyelination to assess the temporal evolution of diffusion tensor (DT), diffusion kurtosis tensor (DK) and WMTI-derived metrics following acute inflammatory demyelination and spontaneous remyelination. While DT-derived metrics were unable to detect cuprizone induced cortical alterations, the mean kurtosis (MK) and radial kurtosis (RK) were found decreased under cuprizone administration, as compared to age-matched controls, in both the motor and somatosensory cortices. The MK remained decreased in the motor cortices at the end of the recovery period, reflecting long lasting impairment of myelination. In white matter, DT, DK and WMTI-derived metrics enabled the detection of cuprizone induced changes differentially according to the stage and the severity of the lesion. More specifically, the MK, the RK and the axonal water fraction (AWF) were the most sensitive for the detection of cuprizone induced changes in the genu of the corpus callosum, a region less affected by cuprizone administration. Additionally, microgliosis was associated with an increase of MK and RK during the acute inflammatory demyelination phase. In regions undergoing severe demyelination, namely the body and splenium of the corpus callosum, DT-derived metrics, notably the mean diffusion (MD) and radial diffusion (RD), were among the best discriminators between cuprizone and control groups, hence highlighting their ability to detect both acute and long lasting changes. Interestingly, WMTI-derived metrics showed the aptitude to distinguish between the different stages of the disease. Both the intra-axonal diffusivity (Da) and the AWF were found to be decreased in the cuprizone treated group, Da specifically decreased during the acute inflammatory demyelinating phase whereas the AWF decrease was associated to the spontaneous remyelination and the recovery period. Altogether our results demonstrate that DKI is sensitive to alterations of cortical areas and provides, along with WMTI metrics, information that is complementary to DT-derived metrics for the characterization of demyelination in both white and grey matter and subsequent inflammatory processes associated with a demyelinating event.

Chronic exposure to haloperidol and olanzapine leads to common and divergent shape changes in the rat hippocampus in the absence of grey-matter volume loss.

BACKGROUND: One of the most consistently reported brain abnormalities in schizophrenia (SCZ) is decreased volume and shape deformation of the hippocampus. However, the potential contribution of chronic antipsychotic medication exposure to these phenomena remains unclear. METHOD: We examined the effect of chronic exposure (8 weeks) to clinically relevant doses of either haloperidol (HAL) or olanzapine (OLZ) on adult rat hippocampal volume and shape using ex vivo structural MRI with the brain retained inside the cranium to prevent distortions due to dissection, followed by tensor-based morphometry (TBM) and elastic surface-based shape deformation analysis. The volume of the hippocampus was also measured post-mortem from brain tissue sections in each group. RESULTS: Chronic exposure to either HAL or OLZ had no effect on the volume of the hippocampus, even at exploratory thresholds, which was confirmed post-mortem. In contrast, shape deformation analysis revealed that chronic HAL and OLZ exposure lead to both common and divergent shape deformations (q = 0.05, FDR-corrected) in the rat hippocampus. In particular, in the dorsal hippocampus, HAL exposure led to inward shape deformation, whereas OLZ exposure led to outward shape deformation. Interestingly, outward shape deformations that were common to both drugs occurred in the ventral hippocampus. These effects remained significant after controlling for hippocampal volume suggesting true shape changes. CONCLUSIONS: Chronic exposure to either HAL or OLZ leads to both common and divergent effects on rat hippocampal shape in the absence of volume change. The implications of these findings for the clinic are discussed.

Evaluation of prominence of straight plates and precontoured clavicle plates using automated plate-to-bone alignment.

Hardware prominence after plate fixation for clavicle fracture is a common complication. The aim of the study was to perform a 3D analysis of the prominence of different types of superior clavicle plates. An automated fitting of 3 straight and 10 precontoured plates was performed on 52 3D-CT-scan reconstructed cadaver clavicles. The mean and maximum bone-plate distance and maximum prominence was significant higher with the straight plates compared to the precontoured plates. The mean and maximum boneplate distance was significant higher with the precontoured DePuy-Synthes plates compared to the precontoured Acumed plates but when evaluating the maximum prominence there was no significant difference between the most commonly used 8-holes plates. To conclude, precontoured plates of the clavicula diminish significantly hardware prominence. There exists a difference in hardware prominence between different brands of precontoured plates but this difference is limited and in most cases not significant.

A Condensed History Approach to X-Ray Dark Field Effects in Edge Illumination Phase Contrast Simulations.

X-ray dark field signals, measurable in many x-ray phase contrast imaging (XPCI) setups, stem from unresolvable microstructures in the scanned sample. This makes them ideally suited for the detection of certain pathologies, which correlate with changes in the microstructure of a sample. Simulations of x-ray dark field signals can aid in the design and optimization of XPCI setups, and the development of new reconstruction techniques. Current simulation tools, however, require explicit modelling of the sample microstructures according to their size and spatial distribution. This process is cumbersome, does not translate well between different samples, and considerably slows down simulations. In this work, a condensed history approach to modelling x-ray dark field effects is presented, under the assumption of an isotropic distribution of microstructures, and applied to edge illumination phase contrast simulations. It substantially simplifies the sample model, can be easily ported between samples, and is two orders of magnitude faster than conventional dark field simulations, while showing equivalent results.Clinical relevance- Dark field signal provides information on the microstructure distribution within the investigated sample, which can be applied in areas such as histology and lung x-ray imaging. Efficient simulation tools for this dark field signal aid in optimizing scanning setups, acquisition schemes and reconstruction techniques.

Performance improvements for iterative electron tomography reconstruction using graphics processing units (GPUs).

Iterative reconstruction algorithms are becoming increasingly important in electron tomography of biological samples. These algorithms, however, impose major computational demands. Parallelization must be employed to maintain acceptable running times. Graphics Processing Units (GPUs) have been demonstrated to be highly cost-effective for carrying out these computations with a high degree of parallelism. In a recent paper by Xu et al. (2010), a GPU implementation strategy was presented that obtains a speedup of an order of magnitude over a previously proposed GPU-based electron tomography implementation. In this technical note, we demonstrate that by making alternative design decisions in the GPU implementation, an additional speedup can be obtained, again of an order of magnitude. By carefully considering memory access locality when dividing the workload among blocks of threads, the GPU's cache is used more efficiently, making more effective use of the available memory bandwidth.

Recurrent inference machines as inverse problem solvers for MR relaxometry.

In this paper, we propose the use of Recurrent Inference Machines (RIMs) to perform T1 and T2 mapping. The RIM is a neural network framework that learns an iterative inference process based on the signal model, similar to conventional statistical methods for quantitative MRI (QMRI), such as the Maximum Likelihood Estimator (MLE). This framework combines the advantages of both data-driven and model-based methods, and, we hypothesize, is a promising tool for QMRI. Previously, RIMs were used to solve linear inverse reconstruction problems. Here, we show that they can also be used to optimize non-linear problems and estimate relaxometry maps with high precision and accuracy. The developed RIM framework is evaluated in terms of accuracy and precision and compared to an MLE method and an implementation of the Residual Neural Network (ResNet). The results show that the RIM improves the quality of estimates compared to the other techniques in Monte Carlo experiments with simulated data, test-retest analysis of a system phantom, and in-vivo scans. Additionally, inference with the RIM is 150 times faster than the MLE, and robustness to (slight) variations of scanning parameters is demonstrated. Hence, the RIM is a promising and flexible method for QMRI. Coupled with an open-source training data generation tool, it presents a compelling alternative to previous methods.

3D imaging of nanomaterials by discrete tomography.

The field of discrete tomography focuses on the reconstruction of samples that consist of only a few different materials. Ideally, a three-dimensional (3D) reconstruction of such a sample should contain only one grey level for each of the compositions in the sample. By exploiting this property in the reconstruction algorithm, either the quality of the reconstruction can be improved significantly, or the number of required projection images can be reduced. The discrete reconstruction typically contains fewer artifacts and does not have to be segmented, as it already contains one grey level for each composition. Recently, a new algorithm, called discrete algebraic reconstruction technique (DART), has been proposed that can be used effectively on experimental electron tomography datasets. In this paper, we propose discrete tomography as a general reconstruction method for electron tomography in materials science. We describe the basic principles of DART and show that it can be applied successfully to three different types of samples, consisting of embedded ErSi(2) nanocrystals, a carbon nanotube grown from a catalyst particle and a single gold nanoparticle, respectively.

Atom-counting in High Resolution Electron Microscopy:TEM or STEM - That's the question.

In this work, a recently developed quantitative approach based on the principles of detection theory is used in order to determine the possibilities and limitations of High Resolution Scanning Transmission Electron Microscopy (HR STEM) and HR TEM for atom-counting. So far, HR STEM has been shown to be an appropriate imaging mode to count the number of atoms in a projected atomic column. Recently, it has been demonstrated that HR TEM, when using negative spherical aberration imaging, is suitable for atom-counting as well. The capabilities of both imaging techniques are investigated and compared using the probability of error as a criterion. It is shown that for the same incoming electron dose, HR STEM outperforms HR TEM under common practice standards, i.e. when the decision is based on the probability function of the peak intensities in HR TEM and of the scattering cross-sections in HR STEM. If the atom-counting decision is based on the joint probability function of the image pixel values, the dependence of all image pixel intensities as a function of thickness should be known accurately. Under this assumption, the probability of error may decrease significantly for atom-counting in HR TEM and may, in theory, become lower as compared to HR STEM under the predicted optimal experimental settings. However, the commonly used standard for atom-counting in HR STEM leads to a high performance and has been shown to work in practice.

Detecting and locating light atoms from high-resolution STEM images: The quest for a single optimal design.

In the present paper, the optimal detector design is investigated for both detecting and locating light atoms from high resolution scanning transmission electron microscopy (HR STEM) images. The principles of detection theory are used to quantify the probability of error for the detection of light atoms from HR STEM images. To determine the optimal experiment design for locating light atoms, use is made of the so-called Cramér-Rao Lower Bound (CRLB). It is investigated if a single optimal design can be found for both the detection and location problem of light atoms. Furthermore, the incoming electron dose is optimised for both research goals and it is shown that picometre range precision is feasible for the estimation of the atom positions when using an appropriate incoming electron dose under the optimal detector settings to detect light atoms.

StatSTEM: An efficient approach for accurate and precise model-based quantification of atomic resolution electron microscopy images.

An efficient model-based estimation algorithm is introduced to quantify the atomic column positions and intensities from atomic resolution (scanning) transmission electron microscopy ((S)TEM) images. This algorithm uses the least squares estimator on image segments containing individual columns fully accounting for overlap between neighbouring columns, enabling the analysis of a large field of view. For this algorithm, the accuracy and precision with which measurements for the atomic column positions and scattering cross-sections from annular dark field (ADF) STEM images can be estimated, has been investigated. The highest attainable precision is reached even for low dose images. Furthermore, the advantages of the model-based approach taking into account overlap between neighbouring columns are highlighted. This is done for the estimation of the distance between two neighbouring columns as a function of their distance and for the estimation of the scattering cross-section which is compared to the integrated intensity from a Voronoi cell. To provide end-users this well-established quantification method, a user friendly program, StatSTEM, is developed which is freely available under a GNU public license.

Generalized likelihood ratio tests for complex fMRI data: a simulation study.

Statistical tests developed for the analysis of (intrinsically complex valued) functional magnetic resonance time series, are generally applied to the data's magnitude components. However, during the past five years, new tests were developed that incorporate the complex nature of fMRI data. In particular, a generalized likelihood ratio test (GLRT) was proposed based on a constant phase model. In this work, we evaluate the sensitivity of GLRTs for complex data to small misspecifications of the phase model by means of simulation experiments. It is argued that, in practical situations, GLRTs based on magnitude data are likely to perform better compared to GLRTs based on complex data in terms of detection rate and constant false alarm rate properties.

Pore REconstruction and Segmentation (PORES) method for improved porosity quantification of nanoporous materials.

Electron tomography is currently a versatile tool to investigate the connection between the structure and properties of nanomaterials. However, a quantitative interpretation of electron tomography results is still far from straightforward. Especially accurate quantification of pore-space is hampered by artifacts introduced in all steps of the processing chain, i.e., acquisition, reconstruction, segmentation and quantification. Furthermore, most common approaches require subjective manual user input. In this paper, the PORES algorithm "POre REconstruction and Segmentation" is introduced; it is a tailor-made, integral approach, for the reconstruction, segmentation, and quantification of porous nanomaterials. The PORES processing chain starts by calculating a reconstruction with a nanoporous-specific reconstruction algorithm: the Simultaneous Update of Pore Pixels by iterative REconstruction and Simple Segmentation algorithm (SUPPRESS). It classifies the interior region to the pores during reconstruction, while reconstructing the remaining region by reducing the error with respect to the acquired electron microscopy data. The SUPPRESS reconstruction can be directly plugged into the remaining processing chain of the PORES algorithm, resulting in accurate individual pore quantification and full sample pore statistics. The proposed approach was extensively validated on both simulated and experimental data, indicating its ability to generate accurate statistics of nanoporous materials.

Neuroanatomy of the fragile X knockout mouse brain studied using in vivo high resolution magnetic resonance imaging.

Magnetic resonance imaging (MRI) of the brain of fragile X patients, the most frequent form of inherited mental retardation, has revealed abnormalities in the size of specific brain structures, including the cerebellar vermis, the hippocampus, and the ventricular system. We intended to quantify the differences observed in the patient studies in the fragile X knockout mouse model, which is a good model for the disease, paralleling the human disorder in having cognitive deficits, macro-orchidism, and immature dendritic spines. Therefore we set up MRI of the mouse brain which allowed us to measure the size of the brain structures reported to be abnormal in human fragile X patients in the mouse model. We did not find evidence for size alterations in various brain regions of the fragile X mouse model, but the method described may find a wide application in the study of mutant mouse models with neurological involvement.

How to optimize the design of a quantitative HREM experiment so as to attain the highest precision.

Using parameter estimation theory, an expression is derived for the maximum precision with which the position of a single atom can be estimated from high resolution transmission electron microscopy images. This expression, being a complicated function of the object as well as of the microscope parameters and the electron dose, can be used to optimize the design of an HREM experiment so as to attain the highest precision.

Maximum-likelihood estimation of Rician distribution parameters.

The problem of parameter estimation from Rician distributed data (e.g., magnitude magnetic resonance images) is addressed. The properties of conventional estimation methods are discussed and compared to maximum-likelihood (ML) estimation which is known to yield optimal results asymptotically. In contrast to previously proposed methods, ML estimation is demonstrated to be unbiased for high signal-to-noise ratio (SNR) and to yield physical relevant results for low SNR.

An energy-based beam hardening model in tomography.

As a consequence of the polychromatic x-ray source, used in micro-computer tomography (microCT) and in medical CT, the attenuation is no longer a linear function of absorber thickness. If this nonlinear beam hardening effect is not compensated, the reconstructed images will be corrupted by cupping artefacts. In this paper, a bimodal energy model for the detected energy spectrum is presented, which can be used for reduction of artefacts caused by beam hardening in well-specified conditions. Based on the combination of the spectrum of the source and the detector efficiency, the assumption is made that there are two dominant energies which can describe the system. The validity of the proposed model is examined by fitting the model to the experimental datapoints obtained on a microtomograph for different materials and source voltages.

Quantification and improvement of the signal-to-noise ratio in a magnetic resonance image acquisition procedure.

A procedure is developed to quantify and improve the signal-to-noise ratio (SNR) of magnetic resonance images. The image SNR is quantified using the correlation function of two independent acquisitions of an image. To test the performance of the quantification, SNR measurement data are fitted to theoretically expected curves. The proposed correlation technique is also used to improve the SNR by estimating the amplitude of the signal spectrum. The technique is applied to a set of MR images, and its performance in terms of gain in SNR, contrast-to-noise ratio (CNR), and resolution loss is compared to that of classical noise filters. The SNR as well as the CNR is improved significantly with minor loss of resolution. Finally, it is shown that the correlation technique can be implemented in a highly efficient way in almost any acquisition procedure of a magnetic resonance imaging system.