Tomographic Reconstruction Research Articles

Dual-Domain Collaborative Diffusion Sampling for Multi-Source Stationary Computed Tomography Reconstruction.

The multi-source stationary CT, where both the detector and X-ray source are fixed, represents a novel imaging system with high temporal resolution that has garnered significant interest. Limited space within the system restricts the number of X-ray sources, leading to sparse-view CT imaging challenges. Recent diffusion models for reconstructing sparse-view CT have generally focused separately on sinogram or image domains. Sinogram-centric models effectively estimate missing projections but may introduce artifacts, lacking mechanisms to ensure image correctness. Conversely, image-domain models, while capturing detailed image features, often struggle with complex data distribution, leading to inaccuracies in projections. Addressing these issues, the Dual-domain Collaborative Diffusion Sampling (DCDS) model integrates sinogram and image domain diffusion processes for enhanced sparse-view reconstruction. This model combines the strengths of both domains in an optimized mathematical framework. A collaborative diffusion mechanism underpins this model, improving sinogram recovery and image generative capabilities. This mechanism facilitates feedback-driven image generation from the sinogram domain and uses image domain results to complete missing projections. Optimization of the DCDS model is further achieved through the alternative direction iteration method, focusing on data consistency updates. Extensive testing, including numerical simulations, real phantoms, and clinical cardiac datasets, demonstrates the DCDS model's effectiveness. It consistently outperforms various state-of-the-art benchmarks, delivering exceptional reconstruction quality and precise sinogram.

Vat photopolymerization of ultra-porous bioactive glass foams

The introduction of additive manufacturing technologies in the field of biomaterials science has opened new horizons for regenerative medicine. In this work, we pushed the potential of vat polymerization to the limit for fabricating ultra-porous bioactive SiO2-CaO-MgO-P2O5-CaF2-Na2O glass scaffolds with bone-like architectural characteristics. The tomographic reconstruction of an open-cell foam was used as input file to the printing system and reliably reproduced in all its exquisite details, as assessed by morphological analyses of sintered scaffolds (thickness of single struts 35 μm, exceptionally high porosity around 94 vol%, most pores with size from 500 to 900 μm). Immersion studies in simulated body fluid (SBF) revealed the apatite-forming ability (i.e., in vitro bioactivity) of the scaffolds, the surface of which started being coated by calcium phosphate after just 3 days from the beginning of the experiments. Taken together, these results show great promise for application of such scaffolds in bone defect repair.

Hyper-realistic rendering-assisted laparoscopic adrenalectomy for giant adrenal tumors: a pilot study.

This study aimed to explore the application value of hyperrealistic rendering (HRR) in laparoscopic giant adrenal tumor resection. We retrospectively analyzed 25 patients with giant adrenal tumors from January 2021 to January 2024, with a median age of 56 (40.5, 58.5) years and a tumor median diameter of 7.20 (6.80, 8.50) cm. All patients underwent preoperative medical HRR based on enhanced computed tomography, followed by laparoscopic adrenal tumor resection. HRR was used to initially determine the nature of the tumor and develop a detailed surgical plan, which was completed in 25 patients preoperatively. All 24 cases of tumors were located in the adrenal gland, 1 case was located in the retroperitoneum, and 13 and 12 cases were on the left and right side, respectively. Preoperative HRR 3D imaging was consistent with the intraoperative situation, and 25 cases had successful surgeries. The median operation time was 165 (120.0, 250.0) min, and median bleeding and blood transfusion volume were 200 (150.0, 450.0) and 200.0 (150.0, 450.0) mL, respectively. There were no collateral injuries to important organs and major vessels and no cases of conversion to open surgery. For large retroperitoneal adrenal tumors, HRR for three-dimensional (3D) reconstruction imaging enables the operator to fully understand the relationship between the tumor and surrounding organs and blood vessels preoperatively, which can reduce intraoperative bleeding and collateral injuries, improve the success rate of laparoscopic resection, and safety of the operation.

Imaging of ventilation and lung injury with low-frequency tomographic ultrasound.

Mechanical ventilation in the intensive care unit (ICU) is a life-saving technique for patients with acute respiratory failure, but is also associated with a high incidence of complications in the injured lung. Currently, there is no widely used monitoring technique to guide the ventilator setting to facilitate a precision medicine approach or to provide a real-time alert for developing adverse pulmonary conditions. Conventional ultrasound has been used as a thoracic bedside technology, but the lack of signal penetration into lung tissue results in images that often contain more information in their artifacts than in the images themselves. Perhaps the greatest obstacle to using traditional ultrasound in the ICU is the need for highly skilled technicians to perform the data collection. In contrast, low-frequency ultrasound (50-500 kHz) has been shown to penetrate the lung, and can detect air trapping in patients with chronic obstructive pulmonary disease (COPD). Here, we present a method of collecting low-frequency ultrasound computed tomographic (USCT) data in vivo on a mechanically ventilated porcine model and computing tomographic reconstructions of airflow during tidal breathing and induced lung injuries. We evaluate the ability of the novel low-frequency USCT system to image regional changes in sound speed in the thorax due to changes in airflow during tidal breathing and induced lung injuries. This represents the first study of low-frequency tomographic ultrasound imaging in vivo and the first to produce tomographic images of ventilatory changes invivo. USCT and computed tomography (CT) scan data were collected alternately on a mechanically ventilated Landrace pig weighing approximately 75 kg during tidal breathing, induced pneumothorax, atelectasis, and pleural effusion. The pneumothorax was induced by injecting air through a 5 mm thick intrathoracic tube inserted in the 8th posterior intercostal space. After removing the air, atelectasis was induced by ventilating the animal with a high concentration of oxygen and low tidal volumes. The pleural effusion was induced by injecting a saline solution through the tube. The USCT data were collected at 125 kHz using the USCT low-frequency ultrasound tomography (LUFT) system on a transducer belt placed around the animal's thorax. Tomographic reconstructions were computed from the USCT data using a regularized refraction-corrected Gauss-Newton-based time-of-flight reconstructionalgorithm. Cyclic changes in computed lung area during tidal breathing were demonstrated to agree with the respiratory rate on the mechanical ventilator. Reconstructed images computed at time steps during the procedure demonstrate regional changes consistent with what would be expected during the induced lung injury. No ground truth was available for images during the procedures since CT scans could only be taken before and after each established lunginjury. In this work, we have demonstrated in the first in vivo study using a mechanically ventilated porcine animal model that low-frequency ultrasound tomography has the ability to image regional changes in sound speed in the thorax corresponding to changes in airflow during tidal breathing and induced lung injury. The results show promise for using low-frequency USCT as a bedside imaging technique in the future for patients with acute respiratory distresssyndrome.

A deep learning method for simultaneous denoising and missing wedge reconstruction in cryogenic electron tomography

Cryogenic electron tomography is a technique for imaging biological samples in 3D. A microscope collects a series of 2D projections of the sample, and the goal is to reconstruct the 3D density of the sample called the tomogram. Reconstruction is difficult as the 2D projections are noisy and can not be recorded from all directions, resulting in a missing wedge of information. Tomograms conventionally reconstructed with filtered back-projection suffer from noise and strong artefacts due to the missing wedge. Here, we propose a deep-learning approach for simultaneous denoising and missing wedge reconstruction called DeepDeWedge. The algorithm requires no ground truth data and is based on fitting a neural network to the 2D projections using a self-supervised loss. DeepDeWedge is simpler than current state-of-the-art approaches for denoising and missing wedge reconstruction, performs competitively and produces more denoised tomograms with higher overall contrast.

Impurity behaviour in JET high-current baseline scenario for Deuterium, Tritium and Deuterium-Tritium plasmas

To support future ITER operation, experimental campaigns at the Joint European Torus (JET) with an ITER-like wall (tungsten divertor and beryllium main chamber) in pure deuterium (D), tritium (T) and Deuterium-Tritium (D-T) were performed. One of the most important challenges in recent years was the development of two main scenarios that investigated different approaches to achieve the high fusion power as well as good plasma confinement (Garzotti et al., 2023). The first one, so-called baseline scenario is relying on high plasma current (Ip≈3.5 MA), normalized beta βN < 2 and safety factor q95 ≈ 3 (Garzotti et al., 2023). On the other hand, the second one, so-called Hybrid scenario is operating at lower plasma current (flat-top Ip ≤ 2.6 MA) and density with respect to the baseline, higher normalized beta βN > 2 and safety factor q95 ≈ 4.8 (Hobirk et al., 2023).In this paper we focus on the impurity behaviour analysis for the baseline discharges at Ip = 3.5 MA and BT = 3.3 T with D, T and DT plasmas, in which the gas and power waveform were optimized to achieve the best possible performance. In particular, we study the impact of total heating power (Ptot + Palpha), flat-top gas flow and ELM (edge localized modes) frequency on mid-Z (Nickel (Ni), Copper (Cu)) and high-Z (Tungsten (W)) impurities. In addition, we compared the two best performing pulses of the baseline scenario (Ip = 3.5MA, BT = 3.3 T and Pin ≈ 35 MW) in D and DT in order to identify the causes responsible for the increase in radiation during the DT pulse, which led to an early plasma termination. All presented results rely on the data collected by the VUV as well as the bolometry system. Detailed analysis indicates that in the baseline scenario, higher radiation, which is most likely due to the tungsten (W), is observed for T and DT plasmas in comparison to D. Moreover, for the two best performing baseline pulses, tomographic reconstructions show that the radiated power density is mainly emitted from the low field side (LFS) of the plasma and W does not accumulate in the plasma center (Telesca et al., 2024).

Predicting fatigue life of additively manufactured lattice structures using the image-based Finite Cell Method and average strain energy density

A well-known Achilles' heel of laser powder bed fusion (L-PBF) additive manufactured lattice structures is the difficulty in predicting fatigue properties. The presence of manufacturing-induced defects significantly affects the fatigue resistance of the porous component and must be accurately captured by predictive models. To tackle this challenge, the as-built geometry of the lattice needs to be modeled, which introduces another challenge on the computational front. For the first time, a model based on the computer tomography (μ-CT) reconstruction of the as-built lattice geometry is simulated with the efficient finite cell method and combined with the average strain energy density (ASED) to obtain accurate fatigue predictions. This work presents a workflow for determining the fatigue resistance of lattice metamaterial, followed by a case study for method validation. The validation shows a good agreement between the predicted fatigue life and the experimental results.

Robust quantitative X-ray phase diagnostic for carbon composite characterisation in the context of lightning induced risk

Getting complementary physical information from a single image acquisition is particularly valuable for materials analysis. Grating based X-ray Phase Contrast Imaging (XPCI) methods allow decoupling attenuation, phase and scattering information. However, the phase and scattering extraction processes can easily suffer from artefacts, which is detrimental to implement this imaging technique in societal applications. In this paper, we demonstrate that grating based XPCI can provide a robust phase measurement in complex materials such as damaged composites. The technique allows the phase to be analysed using a self-assessment method that first identifies the artefacts from the imaging setup, and then can be used as an indicator to interpret the signal from a material. We focus on carbon fibre reinforced polymers which we subjected to laboratory-controlled lightning strikes. We evidence that the combined information from phase and attenuation allow identifying the type of defect induced by the lightning current. The phase information is converted into relative mass density variation within the sample and depicts areas with a loss in density up to 40%. We ensure that these results are valid by comparing them with an X-ray attenuation contrast tomographic reconstruction.

Existence, uniqueness, and efficiency of numerically unbiased attenuation pathlength estimators for photon counting detectors at low count rates.

The first step in computed tomography (CT) reconstruction is to estimate attenuation pathlength. Usually, this is done with a logarithm transformation, which is the direct solution to the Beer-Lambert Law. At low signals, however, the logarithm estimator is biased. Bias arises both from the curvature of the logarithm and from the possibility of detecting zero counts, so a data substitution strategy may be employed to avoid the singularity of the logarithm. Recent progress has been made by Li etal. [IEEE Trans Med Img 42:6, 2023] to modify the logarithm estimator to eliminate curvature bias, but the optimal strategy for mitigating bias from the singularity remains unknown. The purpose of this study was to use numerical techniques to construct unbiased attenuation pathlength estimators that are alternatives to the logarithm estimator, and to study the uniqueness and optimality of possible solutions, assuming a photon counting detector. Formally, an attenuation pathlength estimator is a mapping from integer detector counts to real pathlength values. We constrain our focus to only the small signal inputs that are problematic for the logarithm estimator, which we define as inputs of<100 counts, and we consider estimators that use only a single input and that are not informed by adjacent measurements (e.g., adaptive smoothing). The set of all possible pathlength estimators can then be represented as points in a 100-dimensional vector space. Within this vector space, we use optimization to select the estimator that (1) minimizes mean squared error and (2) is unbiased. We define "unbiased" as satisfying the numerical condition that the maximum bias be less than 0.001 across a continuum of 1000 object thicknesses that span the desired operating range. Because the objective function is convex and the constraints are affine, optimization is tractable and guaranteed to converge to the global minimum. We further examine the nullspace of the constraint matrix to understand the uniqueness of possible solutions, and we compare the results to the Cramér-Rao bound of the variance. We first show that an unbiased attenuation pathlength estimator does not exist if very low mean detector signals (equivalently, very thick objects) are permitted. It is necessary to select a minimum mean detector signal for which unbiased behavior is desired. If we select two counts, the optimal estimator is similar to Li's estimator. If we select one count, the optimal estimator becomes non-monotonic. The oscillations cause the unbiased estimator to be noise amplifying. The nullspace of the constraint matrix is high-dimensional, so that unbiased solutions are not unique. The Cramér-Rao bound of the variance matches well with the expected scaling law and cannot be attained. If arbitrarily thick objects are permitted, an unbiased attenuation pathlength estimator does not exist. If the maximum thickness is restricted, an unbiased estimator exists but is not unique. An optimal estimator can be selected that minimizes variance, but a bias-variance tradeoff exists where a larger domain of unbiased behavior requires increased variance.

Three-dimensional magnetization reconstruction from electron optical phase images with physical constraints

The ability to characterize three-dimensional (3D) magnetization distributions in nanoscale magnetic materials and devices is essential to fully understand their static and dynamic magnetic properties. Phase contrast techniques in the transmission electron microscope (TEM), such as electron holography and electron ptychography, can be used to record two-dimensional (2D) projections of the in-plane magnetic induction of 3D nanoscale objects. Although the 3D magnetic induction can in principle be reconstructed from one or more tilt series of such 2D projections, conventional tomographic reconstruction algorithms do not recover the 3D magnetization within a sample directly. Here, we use simulations to describe the basis of an improved model-based algorithm for the tomographic reconstruction of a 3D magnetization distribution from one or more tilt series of electron optical phase images recorded in the TEM. The algorithm allows a wide range of physical constraints, including a priori information about the sample geometry and magnetic parameters, to be specified. It also makes use of minimization of the micromagnetic energy in the loss function. We demonstrate the reconstruction of the 3D magnetization of a localized magnetic soliton — a hopfion ring — and discuss the influence of noise, choice of magnetic constants, maximum tilt angle and number of tilt axes on the result. The algorithm can in principle be adapted for other magnetic contrast imaging techniques in the TEM, as well as for other magnetic characterization techniques, such as those based on X-rays or neutrons.

Effect of hybrid lead-PVA fibers on microstructure and radiation shielding properties of high-performance concrete

With the widespread application of nuclear technology in the medical and energy fields, the demand for efficient radiation shielding materials is increasing. Employing only lead or polyvinyl alcohol (PVA) fiber reinforcement cannot achieve improvements in the brittleness of high-performance concrete (HPC) while also enhancing radiation shielding efficiency and mechanical properties. This study develops a novel HPC for radiation attenuation, incorporating magnetite as the aggregate and reinforced with hybrid lead-PVA fibers (RSHPC). The synergistic effects of various proportions of hybrid lead-PVA fibers on the mechanical properties, three-dimensional microstructure, pore structure, and interfacial transition zone (ITZ) were investigated. In addition, the radiation attenuation capacities of RSHPC with different hybrid lead-PVA fiber proportions were evaluated through experimental and simulation analysis. The results show that hybrid lead-PVA fibers notably improve the air-void structure of RSHPC. The incorporation of PVA fibers, by improving the ITZ of matrix, effectively mitigates the negative impact of lead fibers on mechanical performance. The refinement of pore structure and the introduction of light and heavy nuclides lead to a significant enhancement in the gamma-ray and neutron radiation attenuation capabilities of RSHPC. Utilizing X-ray computed tomography (X-CT) for three-dimensional reconstruction further indicates the optimization impact of hybrid lead-PVA fibers on the microstructure, notably in the improvement of pore distribution and fiber dispersion, thus enhancing the overall properties and radiation attenuation performance of RSHPC.

Determination of five-parameter grain boundary characteristics in nanocrystalline Ni-W by scanning precession electron diffraction tomography

Determining the full five-parameter grain boundary characteristics from experiments is essential for understanding grain boundaries impact on material properties, improving related models, and designing advanced alloys. However, achieving this is generally challenging, in particular at nanoscale, due to their 3D nature. In our study, we successfully determined the grain boundary characteristics of an annealed nickel-tungsten alloy (NiW) nanocrystalline needle-shaped specimen (tip) containing twins using Scanning Precession Electron Diffraction (SPED) Tomography. The presence of annealing twins in this face-centered cubic (fcc) material gives rise to common reflections in the SPED diffraction patterns, which challenges the reconstruction of orientation-specific virtual dark field (VDF) images required for tomographic reconstruction of the 3D grain shapes. To address this, an automated post-processing step identifies and deselects these shared reflections prior to the reconstruction of the VDF images. Combined with appropriate intensity normalization and projection alignment procedures, this approach enables high-fidelity 3D reconstruction of the individual grains contained in the needle-shaped sample volume. To probe the accuracy of the resulting boundary characteristics, the twin boundary surface normal directions were extracted from the 3D voxelated grain boundary map using a 3D Hough transform. For the sub-set of coherent Σ3 boundaries, the expected {111} grain boundary plane normals were obtained with an angular error of <3° for boundary sizes down to 400 nm². This work advances our ability to precisely characterize and understand the complex grain boundaries that govern material properties.

Calibration-free temperature monitoring of pulverized coal powder based on the dual-mode acoustic tomography

Accurate temperature measurement of the pulverized coal powder is crucial in detecting and preventing coal spontaneous combustion. Compared to other temperature measurement techniques, acoustic tomography using low frequency sound waves has the advantage of high penetration ability for non-intrusive temperature measurement inside the pulverized coal powder. Additionally, by employing an array of acoustic transducers mounted around the coal powder, the entire sensing area can be covered, enabling a comprehensive temperature distribution analysis through tomographic reconstructions. However, the main drawback of the acoustic temperature measurement is the model complexity in describing the dependence of acoustic sound speed on temperature in the porous pulverized coal powder. Accurate calibration is required to determine the model parameters for temperature retrieval, however, which is difficult for in situ industrial application. To solve this problem, we proposed a calibration-free temperature monitoring method based on the dual-mode acoustic tomography for pulverized coal. The three-parameter JCAL model is used to characterize the acoustic propagation in coal powder. The temperature and micro-structure parameters can be jointly retrieved from the reconstructed sound slowness and attenuation distributions at different frequencies. The feasibility and effectiveness of the proposed method are validated in the simulation study. Results show that the dual-mode acoustic tomography can provide calibration-free simplicity for pulverized coal temperature measurement, which has a great potential for industrial applications.

Refining penalty parameter selection in whole-body PET image reconstruction for lung cancer patients using the cross-validation log-likelihood method

Objective.Penalty parameters in penalized likelihood positron emission tomography (PET) reconstruction are typically determined empirically. The cross-validation log-likelihood (CVLL) method has been introduced to optimize these parameters by maximizing a CVLL function, which assesses the likelihood of reconstructed images using one subset of a list-mode dataset based on another subset. This study aims to validate the efficacy of the CVLL method in whole-body imaging for cancer patients using a conventional clinical PET scanner.Approach.Fifteen lung cancer patients were injected with 243.7 ± 23.8 MBq of [18F]FDG and underwent a 22 min PET scan on a Biograph mCT PET/CT scanner, starting at 60 ± 5 min post-injection. The PET list-mode data were partitioned by subsampling without replacement, with 20 minutes of data for image reconstruction using an in-house ordered subset expectation maximization algorithm and the remaining 2 minutes of data for cross-validation. Two penalty parameters, penalty strengthβand Fair penalty function parameterδ, were subjected to optimization. Whole-body images were reconstructed, and CVLL values were computed across various penalty parameter combinations. The optimal image corresponding to the maximum CVLL value was selected by a grid search for each patient.Main results.Theδvalue required to maximize the CVLL value was notably small (⩽10-6in this study). The influences of voxel size and scan duration on image optimization were investigated. A correlation analysis revealed a significant inverse relationship between optimalβand scan count level, with a correlation coefficient of -0.68 (p-value = 3.5 × 10-5). The optimal images selected by the CVLL method were compared with those chosen by two radiologists based on their diagnostic preferences. Differences were observed in the selection of optimal images.Significance.This study demonstrates the feasibility of incorporating the CVLL method into routine imaging protocols, potentially allowing for a wide range of combinations of injected radioactivity amounts and scan durations in modern PET imaging.

Applying Super-Resolution and Tomography Concepts to Identify Receptive Field Subunits in the Retina.

Spatially nonlinear stimulus integration by retinal ganglion cells lies at the heart of various computations performed by the retina. It arises from the nonlinear transmission of signals that ganglion cells receive from bipolar cells, which thereby constitute functional subunits within a ganglion cell's receptive field. Inferring these subunits from recorded ganglion cell activity promises a new avenue for studying the functional architecture of the retina. This calls for efficient methods, which leave sufficient experimental time to leverage the acquired knowledge for further investigating identified subunits. Here, we combine concepts from super-resolution microscopy and computed tomography and introduce super-resolved tomographic reconstruction (STR) as a technique to efficiently stimulate and locate receptive field subunits. Simulations demonstrate that this approach can reliably identify subunits across a wide range of model variations, and application in recordings of primate parasol ganglion cells validates the experimental feasibility. STR can potentially reveal comprehensive subunit layouts within only a few tens of minutes of recording time, making it ideal for online analysis and closed-loop investigations of receptive field substructure in retina recordings.

Omni-directional wavenumber dictionary modified imaging method for orthotropic composites damage detection using ultrasonic guided waves

The sparse reconstruction imaging method is promising for detecting damage in isotropic and quasi-isotropic materials. However, its effectiveness relies on an accurate dictionary. When applied to orthotropic materials without accounting for wavenumber directionality, the accuracy of the dictionary degrades, leading to inaccurate damage localization. To address this limitation, this paper presents an omnidirectional wavenumber dictionary modified imaging method for orthotropic composites damage detection. The omni-directional wavenumber dispersion curve of ultrasonic guided waves is calculated numerically by converting of elastic stiffness coefficient of orthotropic composites at various propagation angles. A modified propagation model, informed by the omni-directional wavenumber dispersion curve, enhances the precision of the acoustic field dictionary. Utilizing a coherent matched field processor, the method compares the scattered signal's envelope with the acoustic field dictionary's envelope to generate an ambiguity function, which in turn forms a damage image. Simulation and experimental results confirm the method's superiority in resolving damage localization inaccuracies and artifacts typically associated with orthotropic composites. The proposed method exhibits enhanced signal-to-noise ratio and lower positioning error, facilitating precise multi-damage detection in orthotropic composites compared to existing methods.

Obtaining parallax-free X-ray powder diffraction computed tomography data with a self-supervised neural network

In this study, we introduce a method designed to eliminate parallax artefacts present in X-ray powder diffraction computed tomography data acquired from large samples. These parallax artefacts manifest as artificial peak shifting, broadening and splitting, leading to inaccurate physicochemical information, such as lattice parameters and crystallite sizes. Our approach integrates a 3D artificial neural network architecture with a forward projector that accounts for the experimental geometry and sample thickness. It is a self-supervised tomographic volume reconstruction approach designed to be chemistry-agnostic, eliminating the need for prior knowledge of the sample’s chemical composition. We showcase the efficacy of this method through its application on both simulated and experimental X-ray powder diffraction tomography data, acquired from a phantom sample and an NMC532 cylindrical lithium-ion battery.

Three-dimensional flame chemiluminescence tomography reconstruction based on outer contour pre-reconstruction

The computed tomography of chemiluminescence (CTC) can be used to reconstruct a three-dimensional (3D) flame chemiluminescence field to obtain information about the spatial characteristics of the flame. However, additional information is needed to solve the ill-posed inverse problem of the CTC due to the constraints such as economy of CTC system and the number of views. In this study, a PR-SART algorithm is proposed for 3D flame reconstruction by combining the flame outer contour pre-reconstruction model with the simultaneous algebraic reconstruction technique (SART). The influence of the number of pre-reconstruction iterations is analyzed in numerical studies. The reconstruction performance of the SART algorithm is compared with the PR-SART algorithm for two flame structures under various numbers of views and noise conditions. Finally, an OH* chemiluminescence imaging system consisting of 8 ultraviolet (UV) cameras is developed, and evaluated through use of reconstructing the 3D structure of low-swirl flames. Numerical and experimental studies indicate that the proposed algorithm and CTC system are effectively capable of removing the reconstruction error in the flame-free region, improving the reconstruction quality, and reducing the computational cost.

Skeletal computed tomography findings of upper extremities in middle-aged persons with thalidomide embryopathy.

Individuals with thalidomide embryopathy are now approximately 60 years old. For years, they have been compensating for their hypoplastic limbs in various aspects of daily living, and they face secondary problems such as limb and back pain. Imaging analysis is beneficial for understanding the pathogenesis of these problems. However, previous studies on skeletal imaging were mainly radiographic studies conducted at young ages, and there are few studies on skeletal imaging after aging, with most of them being case reports. In this study, detailed analyses of the skeletons of the upper extremities were performed using three-dimensional computed tomography and multiplanar reconstruction images in five individuals with thalidomide embryopathy aged approximately 60 years. Each individual frequently complained of neck, shoulder, and/or back pain. Dislocation, subluxation, and osteoarthritis were observed in the shoulder joints in some individuals. Hypoplasia of the trochlea and/or capitulum of the humerus, coronoid fossa, olecranon, and coronoid processes was observed in the elbow joints. Fusion and hypoplasia of the carpal bones were frequently observed in wrist joints. Radiocarpal and ulnocarpal synostoses were also observed. The joint instability and osteoarthritis found in this study may have contribute to upper limb pain in individuals with thalidomide embryopathy.

A convergence enhancement approach for tomographic reconstruction in ultrasonic tomography

A convergence enhancement approach for tomographic reconstruction in ultrasonic tomography