ABSTRACT: X-ray 3D tomographic techniques are powerful tools for investigating the morphology and internal structures of specimens. A common strategy for obtaining 3D tomography is to capture a series of 2D projections from different X-ray illumination angles of specimens mounted on a finely calibrated rotational stage. However, the reconstruction quality of 3D tomography relies on the precision and stability of the rotational stage, i.e. the accurate alignment of the 2D projections in the correct three-dimensional positions. This is a crucial problem for nano-tomographic techniques due to the non-negligible mechanical imperfection of the rotational stages at the nanometer level which significantly degrades the spatial resolution of reconstructed 3-D tomography. Even when using an X-ray micro-CT with a highly stabilized rotational stage, thermal effects caused by the CT system are not negligible and may cause sample drift. Here, we propose a markerless image auto-alignment algorithm based on an iterative method. This algorithm reduces the traditional projection matching method into two simplified matching problems and it is much faster and more reliable than traditional methods. This algorithm can greatly decrease hardware requirements for both nano-tomography and data processing and can be easily applied to other tomographic techniques, such as X-ray micro-CT and electron tomography.
Project description:Tomography has made a radical impact on diverse fields ranging from the study of 3D atomic arrangements in matter to the study of human health in medicine. Despite its very diverse applications, the core of tomography remains the same, that is, a mathematical method must be implemented to reconstruct the 3D structure of an object from a number of 2D projections. Here, we present the mathematical implementation of a tomographic algorithm, termed GENeralized Fourier Iterative REconstruction (GENFIRE), for high-resolution 3D reconstruction from a limited number of 2D projections. GENFIRE first assembles a 3D Fourier grid with oversampling and then iterates between real and reciprocal space to search for a global solution that is concurrently consistent with the measured data and general physical constraints. The algorithm requires minimal human intervention and also incorporates angular refinement to reduce the tilt angle error. We demonstrate that GENFIRE can produce superior results relative to several other popular tomographic reconstruction techniques through numerical simulations and by experimentally reconstructing the 3D structure of a porous material and a frozen-hydrated marine cyanobacterium. Equipped with a graphical user interface, GENFIRE is freely available from our website and is expected to find broad applications across different disciplines.
Project description:Gel'fand and Graev performed classical work on the inversion of integral transforms in different spaces [Gel'fand and Graev, Funct. Anal. Appl. 25(1) 1-5 (1991)]. This paper discusses their key results for further research and development.The Gel'fand-Graev inversion formula reveals a fundamental relationship between projection data and the Hilbert transform of an image to be reconstructed. This differential backprojection (DBP)∕backprojection filtration (BPF) approach was rediscovered in the CT field, and applied in important applications such as reconstruction from truncated projections, interior tomography, and limited-angle tomography. Here the authors present the Gel'fand-Graev inversion formula in a 3D setting assuming the 1D x-ray transform.The pseudodifferential operator is a powerful theoretical tool. There is a fundamental mathematical link between the Gel'fand-Graev formula and the DBP (or BPF) approach in the case of the 1D x-ray transform in a 3D real space.This paper shows the power of mathematics for tomographic imaging and the value of a pure theoretical finding, which may appear quite irrelevant to daily healthcare at the first glance.
Project description:The recent advent of tensor tomography techniques has enabled tomographic investigations of the 3D nanostructure organization of biological and material science samples. These techniques extended the concept of conventional X-ray tomography by reconstructing not only a scalar value such as the attenuation coefficient per voxel, but also a set of parameters that capture the local anisotropy of nanostructures within every voxel of the sample. Tensor tomography data sets are intrinsically large as each pixel of a conventional X-ray projection is substituted by a scattering pattern, and projections have to be recorded at different sample angular orientations with several tilts of the rotation axis with respect to the X-ray propagation direction. Currently available reconstruction approaches for such large data sets are computationally expensive. Here, a novel, fast reconstruction algorithm, named iterative reconstruction tensor tomography (IRTT), is presented to simplify and accelerate tensor tomography reconstructions. IRTT is based on a second-rank tensor model to describe the anisotropy of the nanostructure in every voxel and on an iterative error backpropagation reconstruction algorithm to achieve high convergence speed. The feasibility and accuracy of IRTT are demonstrated by reconstructing the nanostructure anisotropy of three samples: a carbon fiber knot, a human bone trabecula specimen and a fixed mouse brain. Results and reconstruction speed were compared with those obtained by the small-angle scattering tensor tomography (SASTT) reconstruction method introduced by Liebi et al. [Nature (2015), 527, 349-352]. The principal orientation of the nanostructure within each voxel revealed a high level of agreement between the two methods. Yet, for identical data sets and computer hardware used, IRTT was shown to be more than an order of magnitude faster. IRTT was found to yield robust results, it does not require prior knowledge of the sample for initializing parameters, and can be used in cases where simple anisotropy metrics are sufficient, i.e. the tensor approximation adequately captures the level of anisotropy and the dominant orientation within a voxel. In addition, by greatly accelerating the reconstruction, IRTT is particularly suitable for handling large tomographic data sets of samples with internal structure or as a real-time analysis tool during the experiment for online feedback during data acquisition. Alternatively, the IRTT results might be used as an initial guess for models capturing a higher complexity of structural anisotropy such as spherical harmonics based SASTT in Liebi et al. (2015), improving both overall convergence speed and robustness of the reconstruction.
Project description:The limited specimen tilting range that is typically available in electron tomography gives rise to a region in the Fourier space of the reconstructed object where experimental data are unavailable - the missing wedge. Since this region is sharply delimited from the area of available data, the reconstructed signal is typically hampered by convolution with its impulse response, which gives rise to the well-known missing wedge artefacts in 3D reconstructions. Despite the recent progress in the field of reconstruction and regularization techniques, the missing wedge artefacts remain untreated in most current reconstruction workflows in structural biology. Therefore we have designed a simple Fourier angular filter that effectively suppresses the ray artefacts in the single-axis tilting projection acquisition scheme, making single-axis tomographic reconstructions easier to interpret in particular at low signal-to-noise ratio in acquired projections. The proposed filter can be easily incorporated into current electron tomographic reconstruction schemes.
Project description:Statistical image reconstruction (SIR) methods are studied extensively for X-ray computed tomography (CT) due to the potential of acquiring CT scans with reduced X-ray dose while maintaining image quality. However, the longer reconstruction time of SIR methods hinders their use in X-ray CT in practice. To accelerate statistical methods, many optimization techniques have been investigated. Over-relaxation is a common technique to speed up convergence of iterative algorithms. For instance, using a relaxation parameter that is close to two in alternating direction method of multipliers (ADMM) has been shown to speed up convergence significantly. This paper proposes a relaxed linearized augmented Lagrangian (AL) method that shows theoretical faster convergence rate with over-relaxation and applies the proposed relaxed linearized AL method to X-ray CT image reconstruction problems. Experimental results with both simulated and real CT scan data show that the proposed relaxed algorithm (with ordered-subsets [OS] acceleration) is about twice as fast as the existing unrelaxed fast algorithms, with negligible computation and memory overhead.
Project description:Synchrotron X-ray fluorescence (SXRF) microtomography has emerged as a powerful technique for the 3D visualization of the elemental distribution in biological samples. The mechanical stability, both of the instrument and the specimen, is paramount when acquiring tomographic projection series. By combining the progressive lowering of temperature method (PLT) with femtosecond laser sectioning, we were able to embed, excise, and preserve a zebrafish embryo at 24 hours post fertilization in an X-ray compatible, transparent resin for tomographic elemental imaging. Based on a data set comprised of 60 projections, acquired with a step size of 2 ?m during 100 hours of beam time, we reconstructed the 3D distribution of zinc, iron, and copper using the iterative maximum likelihood expectation maximization (MLEM) reconstruction algorithm. The volumetric elemental maps, which entail over 124 million individual voxels for each transition metal, revealed distinct elemental distributions that could be correlated with characteristic anatomical features at this stage of embryonic development.
Project description:Accurate knowledge of elemental distributions within biological organisms is critical for understanding their cellular roles. The ability to couple this knowledge with overall cellular architecture in three dimensions (3D) deepens our understanding of cellular chemistry. Using a whole, frozen-hydrated <i>Chlamydomonas reinhardtii</i> cell as an example, we report the development of 3D correlative microscopy through a combination of simultaneous cryogenic x-ray ptychography and x-ray fluorescence microscopy. By taking advantage of a recently developed tomographic reconstruction algorithm, termed GENeralized Fourier Iterative REconstruction (GENFIRE), we produce high-quality 3D maps of the unlabeled alga's cellular ultrastructure and elemental distributions within the cell. We demonstrate GENFIRE's ability to outperform conventional tomography algorithms and to further improve the reconstruction quality by refining the experimentally intended tomographic angles. As this method continues to advance with brighter coherent light sources and more efficient data handling, we expect correlative 3D x-ray fluorescence and ptychographic tomography to be a powerful tool for probing a wide range of frozen-hydrated biological specimens, ranging from small prokaryotes such as bacteria, algae, and parasites to large eukaryotes such as mammalian cells, with applications that include understanding cellular responses to environmental stimuli and cell-to-cell interactions.
Project description:X-ray imaging techniques that capture variations in the x-ray phase can yield higher contrast images with lower x-ray dose than is possible with conventional absorption radiography. However, the extraction of phase information is often more difficult than the extraction of absorption information and requires a more sophisticated experimental arrangement. We here report a method for three-dimensional (3D) X-ray phase contrast computed tomography (CT) which gives quantitative volumetric information on the real part of the refractive index. The method is based on the recently developed X-ray speckle tracking technique in which the displacement of near field speckle is tracked using a digital image correlation algorithm. In addition to differential phase contrast projection images, the method allows the dark-field images to be simultaneously extracted. After reconstruction, compared to conventional absorption CT images, the 3D phase CT images show greatly enhanced contrast. This new imaging method has advantages compared to other X-ray imaging methods in simplicity of experimental arrangement, speed of measurement and relative insensitivity to beam movements. These features make the technique an attractive candidate for material imaging such as in-vivo imaging of biological systems containing soft tissue.
Project description:To develop an algorithm for computing realistic digitally reconstructed radiographs (DRRs) that match real cone-beam CT (CBCT) projections with no artificial adjustments.The authors used measured attenuation data from cone-beam CT projection radiographs of different materials to obtain a function to convert CT number to linear attenuation coefficient (LAC). The effects of scatter, beam hardening, and veiling glare were first removed from the attenuation data. Using this conversion function the authors calculated the line integral of LAC through a CT along rays connecting the radiation source and detector pixels with a ray-tracing algorithm, producing raw DRRs. The effects of scatter, beam hardening, and veiling glare were then included in the DRRs through postprocessing.The authors compared actual CBCT projections to DRRs produced with all corrections (scatter, beam hardening, and veiling glare) and to uncorrected DRRs. Algorithm accuracy was assessed through visual comparison of projections and DRRs, pixel intensity comparisons, intensity histogram comparisons, and correlation plots of DRR-to-projection pixel intensities. In general, the fully corrected algorithm provided a small but nontrivial improvement in accuracy over the uncorrected algorithm. The authors also investigated both measurement- and computation-based methods for determining the beam hardening correction, and found the computation-based method to be superior, as it accounted for nonuniform bowtie filter thickness. The authors benchmarked the algorithm for speed and found that it produced DRRs in about 0.35 s for full detector and CT resolution at a ray step-size of 0.5 mm.The authors have demonstrated a DRR algorithm calculated from first principles that accounts for scatter, beam hardening, and veiling glare in order to produce accurate DRRs. The algorithm is computationally efficient, making it a good candidate for iterative CT reconstruction techniques that require a data fidelity term based on the matching of DRRs and projections.
Project description:Cone-beam computed tomography (CBCT) is an imaging technique that provides computed tomographic (CT) images from a rotational scan acquired with a C-arm equipped with a flat panel detector. Utilizing CBCT images during interventional procedures bridges the gap between the world of diagnostic imaging (typically three-dimensional imaging but performed separately from the procedure) and that of interventional radiology (typically two-dimensional imaging). CBCT is capable of providing more information than standard two-dimensional angiography in localizing and/or visualizing liver tumors ("seeing" the tumor) and targeting tumors though precise microcatheter placement in close proximity to the tumors ("reaching" the tumor). It can also be useful in evaluating treatment success at the time of procedure ("assessing" treatment success). CBCT technology is rapidly evolving along with the development of various contrast material injection protocols and multiphasic CBCT techniques. The purpose of this article is to provide a review of the principles of CBCT imaging, including purpose and clinical evidence of the different techniques, and to introduce a decision-making algorithm as a guide for the routine utilization of CBCT during transarterial chemoembolization of liver cancer.