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Due to the incomplete projection data collected by limited-angle computed tomography (CT), severe artifacts are present in the reconstructed image. Classical regularization methods such as total variation (TV) minimization, ℓ0 minimization, are unable to suppress artifacts at the edges perfectly. Most existing regularization methods are single-objective optimization approaches, stemming from scalarization methods for multiobjective optimization problems (MOP).To further suppress the artifacts and effectively preserve the edge structures of the reconstructed image.This study presents a multiobjective optimization model incorporates both data fidelity term and ℓ0-norm of the image gradient as objective functions. It employs an iterative approach different from traditional scalarization methods, using the maximization of structural similarity (SSIM) values to guide optimization rather than minimizing the objective function.The iterative method involves two steps, firstly, simultaneous algebraic reconstruction technique (SART) optimizes the data fidelity term using SSIM and the Simulated Annealing (SA) algorithm for guidance. The degradation solution is accepted in the form of probability, and guided image filtering (GIF) is introduced to further preserve the image edge when the degradation solution is rejected. Secondly, the result from the first step is integrated into the second objective function as a constraint, we use ℓ0 minimization to optimize ℓ0-norm of the image gradient, and the SSIM, SA algorithm and GIF are introduced to guide optimization process by improving SSIM value like the first step.With visual inspection, the peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and SSIM values indicate that our approach outperforms other traditional methods.The experiments demonstrate the effectiveness of our method and its superiority over other classical methods in artifact suppression and edge detail restoration. [ABSTRACT FROM AUTHOR]

This study explores the implementation of computed tomography (CT) reconstruction and simulation techniques for patient-specific valves, aiming to dissect the mechanical attributes of calcified valves within transcatheter heart valve replacement (TAVR) procedures. In order to facilitate this exploration, it derives pertinent formulas for 3D multi-material isogeometric hyperelastic analysis based on Hounsfield unit (HU) values, thereby unlocking foundational capabilities for isogeometric analysis in calcified aortic valves. A series of uniaxial and biaxial tensile tests is executed to obtain an accurate constitutive model for calcified active valves. To mitigate discretization errors, methodologies for reconstructing volumetric parametric models, integrating both geometric and material attributes, are introduced. Applying these analytical formulas, constitutive models, and precise analytical models to isogeometric analyses of calcified valves, the research ascertains their close alignment with experimental results through the close fit in displacement-stress curves, compellingly validating the accuracy and reliability of the method. This study presents a step-by-step approach to analyzing themechanical characteristics of patient-specific valves obtained fromCT images, holding significant clinical implications and assisting in the selection of treatment strategies and surgical intervention approaches in TAVR procedures. [ABSTRACT FROM AUTHOR]

Introduction: Foot health is crucial for elephants, as pathological lesions of the feet are a leading cause of euthanasia in captive elephants, which are endangered species. Proper treatment of the feet, particularly in conditions affecting the digits and the digital cushion, requires a thorough understanding of the underlying anatomy. However, only limited literature exists due to the small population and the epidemiological foot diseases which often precludes many deceased elephants from scientific study. The aim of this study was to provide a detailed anatomical description of the blood supply to the African elephant's hindfoot. Methods: The healthy right hindlimb of a 19-year-old deceased female African savanna elephant was examined using computed tomography. Following a native sequence, 48 mL of barium-based contrast agent was injected into the caudal and cranial tibial arteries, and a subsequent scan was performed. The images were processed with 3D Slicer software. Results: The medial and lateral plantar arteries run in a symmetrical pattern. They each have a dorsal and a plantar branch, which reach the plantar skin before turning toward the axial plane of the sole to reach the digital cushion from the proximal direction. An accurate 3D model of the arteries and the bones of the foot, a set of labeled images and an animation of the blood supply have been created for ease of understanding. Discussion: In contrast to domestic ungulates, the digital cushion of the hindlimb is supplied differently from that of the forelimb. The lack of large vessels in its deeper layers indicates a slow regeneration time. This novel anatomical information may be useful in the planning of surgical interventions and in emergency medical procedures. [ABSTRACT FROM AUTHOR]

Introduction: Foot health is crucial for elephants, as pathological lesions of the feet are a leading cause of euthanasia in captive elephants, which are endangered species. Proper treatment of the feet, particularly in conditions affecting the digits and the digital cushion, requires a thorough understanding of the underlying anatomy. However, only limited literature exists due to the small population and the epidemiological foot diseases which often precludes many deceased elephants from scientific study. The aim of this study was to provide a detailed anatomical description of the blood supply to the African elephant's hindfoot. Methods: The healthy right hindlimb of a 19-year-old deceased female African savanna elephant was examined using computed tomography. Following a native sequence, 48 mL of barium-based contrast agent was injected into the caudal and cranial tibial arteries, and a subsequent scan was performed. The images were processed with 3D Slicer software. Results: The medial and lateral plantar arteries run in a symmetrical pattern. They each have a dorsal and a plantar branch, which reach the plantar skin before turning toward the axial plane of the sole to reach the digital cushion from the proximal direction. An accurate 3D model of the arteries and the bones of the foot, a set of labeled images and an animation of the blood supply have been created for ease of understanding. Discussion: In contrast to domestic ungulates, the digital cushion of the hindlimb is supplied differently from that of the forelimb. The lack of large vessels in its deeper layers indicates a slow regeneration time. This novel anatomical information may be useful in the planning of surgical interventions and in emergency medical procedures. [ABSTRACT FROM AUTHOR]

We present a deep-learning-based software pipeline for reconstructing and segmenting large sets of time-resolved micro-computed tomography (µCT) image data. We construct and train a convolutional neural network (CNN) to consistently, rapidly, and autonomously segment the time-resolved tomography data. The preceding CT reconstruction steps are parametrized for optimal image quality for segmentation. We demonstrate how to discriminate materials with similar radiographic densities in the presence of different media, such as air and water. Our approach can be used out of the box for similar µCT data or adapted to any similarly challenging 3D image data by retraining the neural network.

The complex anatomy of the avian respiratory system makes it necessary to broaden our knowledge using modern imaging and reconstructional possibilities. The visualization of these structures can be used for clinical situations, in research or as teaching aids in veterinary education. For this we generated 3D models from diagnostic imaging data (computed tomography [CT] scans) of birds. We describe in detail a repeatable method of animal preparation for scanning, data handling and image analysis. CT scans with varying slice thickness and resolution were obtained in prone and supine body positions to analyse air sac morphology and volume changes relative to posture or sexual dimorphism in birds. The resulting data were prepared and analysed using a reconstructional software (3D Slicer) based on manual and semi‐automatic labelling and subsequent 3D models of the air sac system were created. The terminology employed has been referenced from the Nomina Anatomica Avium, Second Ed. [ABSTRACT FROM AUTHOR]

IntroductionFoot health is crucial for elephants, as pathological lesions of the feet are a leading cause of euthanasia in captive elephants, which are endangered species. Proper treatment of the feet, particularly in conditions affecting the digits and the digital cushion, requires a thorough understanding of the underlying anatomy. However, only limited literature exists due to the small population and the epidemiological foot diseases which often precludes many deceased elephants from scientific study. The aim of this study was to provide a detailed anatomical description of the blood supply to the African elephant’s hindfoot.MethodsThe healthy right hindlimb of a 19-year-old deceased female African savanna elephant was examined using computed tomography. Following a native sequence, 48 mL of barium-based contrast agent was injected into the caudal and cranial tibial arteries, and a subsequent scan was performed. The images were processed with 3D Slicer software.ResultsThe medial and lateral plantar arteries run in a symmetrical pattern. They each have a dorsal and a plantar branch, which reach the plantar skin before turning toward the axial plane of the sole to reach the digital cushion from the proximal direction. An accurate 3D model of the arteries and the bones of the foot, a set of labeled images and an animation of the blood supply have been created for ease of understanding.DiscussionIn contrast to domestic ungulates, the digital cushion of the hindlimb is supplied differently from that of the forelimb. The lack of large vessels in its deeper layers indicates a slow regeneration time. This novel anatomical information may be useful in the planning of surgical interventions and in emergency medical procedures.

Sparse view computed tomography (SVCT) aims to reduce the number of X-ray projection views required for reconstructing the cross-sectional image of an object. While SVCT significantly reduces X-ray radiation dose and speeds up scanning, insufficient projection data give rise to issues such as severe streak artifacts and blurring in reconstructed images, thereby impacting the diagnostic accuracy of CT detection. To address this challenge, a dual-domain reconstruction network incorporating multi-level wavelet transform and recurrent convolution is proposed in this paper. The dual-domain network is composed of a sinogram domain network (SDN) and an image domain network (IDN). Multi-level wavelet transform is employed in both IDN and SDN to decompose sinograms and CT images into distinct frequency components, which are then processed through separate network branches to recover detailed information within their respective frequency bands. To capture global textures, artifacts, and shallow features in sinograms and CT images, a recurrent convolution unit (RCU) based on convolutional long and short-term memory (Conv-LSTM) is designed, which can model their long-range dependencies through recurrent calculation. Additionally, a self-attention-based multi-level frequency feature normalization fusion (MFNF) block is proposed to assist in recovering high-frequency components by aggregating low-frequency components. Finally, an edge loss function based on the Laplacian of Gaussian (LoG) is designed as the regularization term for enhancing the recovery of high-frequency edge structures. The experimental results demonstrate the effectiveness of our approach in reducing artifacts and enhancing the reconstruction of intricate structural details across various sparse views and noise levels. Our method excels in both performance and robustness, as evidenced by its superior outcomes in numerous qualitative and quantitative assessments, surpassing contemporary state-of-the-art CNNs or Transformer-based reconstruction methods.

To address this issue of streak artifacts and noise caused by faster scanning in this paper, this paper presents a prunable U-NET++ with hybrid attention and loss function (HAL-UNET++) deep learning network, which is a U-shaped framework-based network for industrial CT image reconstruction. In this reconstruction network, the prior knowledge that CT images are sparse in the gradient domain is utilized, and a hybrid loss function is designed to combine the L0 norm of gradient image and the image signal-to-noise ratio. This ensures that the network’s learning process places greater emphasis on the noise content and sparsity of the image gradient domain. Subsequently, an adaptable convolutional block attention module has been incorporated into the shallow network layers to ensure the preservation of finer details in the CT images that are reconstructed. Finally, experimental objects comprising cables and rock samples with internal defects were used. The results indicate that the present reconstruction network, through pruning techniques, achieves rapid image reconstruction. Under the constraint of maintaining reconstruction accuracy, the pruned network demonstrates a reduction of approximately fourfold in network parameter volume compared to the unpruned network, resulting in a time saving of 0.24 seconds for the reconstruction of a single CT image. [ABSTRACT FROM AUTHOR]

Computed Tomography (CT) is an essential imaging tool for medical inspection, diagnosis and prevention. While X-rays CT is a consolidated technology, there is nowadays a strong drive for innovation in this field. Between the emerging topics, Diffuse Optical Tomography (DOT) is an instance of Diffuse Optical Imaging which uses non-ionizing light in the near-infrared (NIR) band as investigating signal. Non-trivial challenges accompany DOT reconstruction, which is a severely ill-conditioned inverse problem due to the highly scattering nature of the propagation of light in biological tissues. Correspondingly, the solution of this problem is far from being trivial. In this review paper, we first recall the theoretical basis of NIR light propagation, the relevant mathematical models with their derivation in the perspective of a hierarchy of modeling approaches and the analytical results on the uniqueness issue and stability estimates. Then we describe the state-of-the-art in analytic theory and in computational and algorithmic methods. We present a survey of the few contributions regarding DOT reconstruction aided by machine learning approaches and we conclude providing perspectives in the mathematical treatment of this highly challenging problem. [ABSTRACT FROM AUTHOR]

Sparse view computed tomography (SVCT) aims to reduce the number of X-ray projection views required for reconstructing the cross-sectional image of an object. While SVCT significantly reduces X-ray radiation dose and speeds up scanning, insufficient projection data give rise to issues such as severe streak artifacts and blurring in reconstructed images, thereby impacting the diagnostic accuracy of CT detection. To address this challenge, a dual-domain reconstruction network incorporating multi-level wavelet transform and recurrent convolution is proposed in this paper. The dual-domain network is composed of a sinogram domain network (SDN) and an image domain network (IDN). Multi-level wavelet transform is employed in both IDN and SDN to decompose sinograms and CT images into distinct frequency components, which are then processed through separate network branches to recover detailed information within their respective frequency bands. To capture global textures, artifacts, and shallow features in sinograms and CT images, a recurrent convolution unit (RCU) based on convolutional long and short-term memory (Conv-LSTM) is designed, which can model their long-range dependencies through recurrent calculation. Additionally, a self-attention-based multi-level frequency feature normalization fusion (MFNF) block is proposed to assist in recovering high-frequency components by aggregating low-frequency components. Finally, an edge loss function based on the Laplacian of Gaussian (LoG) is designed as the regularization term for enhancing the recovery of high-frequency edge structures. The experimental results demonstrate the effectiveness of our approach in reducing artifacts and enhancing the reconstruction of intricate structural details across various sparse views and noise levels. Our method excels in both performance and robustness, as evidenced by its superior outcomes in numerous qualitative and quantitative assessments, surpassing contemporary state-of-the-art CNNs or Transformer-based reconstruction methods. [ABSTRACT FROM AUTHOR]

This paper investigates the application of unsupervised learning methods for computed tomography reconstruction. To motivate our work, we review several existing priors, namely the truncated Gaussian prior, the l1 prior, the total variation prior, and the deep image prior (DIP). We find that DIP outperforms the other three priors in terms of representational capability and visual performance. However, the performance of DIP deteriorates when the number of iterations exceeds a certain threshold due to overfitting. To address this issue, we propose a novel method (MCDIP‐ADMM) based on multi‐code deep image prior (MCDIP) and plug‐and‐play alternative direction method of multipliers (ADMM). Specifically, MCDIP utilizes multiple latent codes to generate a series of feature maps at an intermediate layer within a generator model. These maps are then composed with trainable weights, representing the complete image prior. Experimental results demonstrate the superior performance of the proposed MCDIP‐ADMM compared to three existing competitors. In the case of parallel beam projection with Gaussian noise, MCDIP‐ADMM achieves an average improvement of 4.3 dB over DIP, 1.7 dB over ADMM DIP‐weighted total variation (WTV) and 1.2 dB over PnP‐DIP in terms of peak‐signal‐to‐noise ratio (PSNR). Similarly, for fan‐beam projection with Poisson noise, MCDIP‐ADMM achieves an average improvement of 3.09 dB over DIP, 1.86 dB over ADMM DIP‐WTV and 0.84 dB over PnP‐DIP in terms of PSNR. [ABSTRACT FROM AUTHOR]

Key clinical information We report a case of traumatic scalp and dural arteriovenous fistula with significant vascular malformation, which is important for our understanding of this disease.

Key clinical information: We report a case of traumatic scalp and dural arteriovenous fistula with significant vascular malformation, which is important for our understanding of this disease. [ABSTRACT FROM AUTHOR]

Some birds intentionally ingest stones to facilitate digestion of hard foodstuffs, a behaviour inherited from non‐avian dinosaurs and present in some of the earliest birds, as evidenced by clusters of gastroliths preserved within the abdominal cavity of a wide range of dinosaurs and Cretaceous birds. For the first time, high‐resolution computed laminographic and computed tomographic scans were used to reconstruct the gastral mass in two species of non‐neornithine ornithuromorph birds from the Lower Cretaceous Jehol Group. Four specimens of each taxon were analysed. Preservation of the gastral mass in most of these specimens is in situ and regarded as complete or nearly so. The number of gastroliths, their total volume, and their total mass relative to the estimated body mass were calculated for each specimen. The resultant gastral mass to body mass ratios fall within the range observed in extant birds, supporting previous inferences that the digestive system in non‐neornithine ornithuromorphs was comparable to that of extant taxa. Compared to available data for non‐volant non‐avian theropods, the gastral mass is proportionately smaller in birds suggesting that the evolution of flight constrained gastral mass size in the theropod lineage. Currently available data on gastral mass characteristics suggests that Iteravis ate larger food particles compared to Archaeorhynchus but cannot be used to determine diet more precisely. Better understanding of the relationship between gastral mass characteristics and food items across a broader range of extant taxa may provide an indirect but important method through which to infer diet and digestive function in archosaurs. [ABSTRACT FROM AUTHOR]

Objective. Dual-energy computed tomography (DECT) has been widely used to reconstruct numerous types of images due its ability to better discriminate tissue properties. Sequential scanning is a popular dual-energy data acquisition method as it requires no specialized hardware. However, patient motion between two sequential scans may lead to severe motion artifacts in DECT statistical iterative reconstructions (SIR) images. The objective is to reduce the motion artifacts in such reconstructions. Approach. We propose a motion-compensation scheme that incorporates a deformation vector field into any DECT SIR. The deformation vector field is estimated via the multi-modality symmetric deformable registration method. The precalculated registration mapping and its inverse or adjoint are then embedded into each iteration of the iterative DECT algorithm. Main results. Results from a simulated and clinical case show that the proposed framework is capable of reducing motion artifacts in DECT SIRs. Percentage mean square errors in regions of interest in the simulated and clinical cases were reduced from 4.6% to 0.5% and 6.8% to 0.8%, respectively. A perturbation analysis was then performed to determine errors in approximating the continuous deformation by using the deformation field and interpolation. Our findings show that errors in our method are mostly propagated through the target image and amplified by the inverse matrix of the combination of the Fisher information and Hessian of the penalty term. Significance. We have proposed a novel motion-compensation scheme to incorporate a 3D registration method into the joint statistical iterative DECT algorithm in order to reduce motion artifacts caused by inter-scan motion, and successfully demonstrate that interscan motion corrections can be integrated into the DECT SIR process, enabling accurate imaging of radiological quantities on conventional SECT scanners, without significant loss of either computational efficiency or accuracy. [ABSTRACT FROM AUTHOR]

Objective Edelson classification is a 3D classification of proximal humeral fractures, but there is a scarcity of application of this classification in large samples, and the accuracy of classification was also not testified. The objective of this research was to verify whether a revised Edelson classification produces satisfactory agreement for proximal humeral fracture classification in adult patients. Methods A total of 827 proximal humeral fractures (304 male and 520 female patients, 58.0 ± 16.2 years) were found retrospectively from January 2014 to December 2019, and classified according to the traditional and newly proposed Edelson classification. The three‐dimensional CT images were processed, rotated and visualized within software. Five shoulder surgeons classified each fracture. After data collection, radiographic classifications results were compared by inter‐ and intraobserver analysis with the method of weighted kappa coefficients. Fracture classification based on Edelson and revised Edelson classification was presented and compared. Results The mean k value for the interobserver reliability was 0.748 (range, 0.583 to 0.958) compared with Edelson classification (0.548, range, 0.48 to 0.635), indicating satisfactory agreement. The mean k value for intraobserver reliability was 0.906 (range, 0.823 to 0.943) compared with Edelson classification (0.762, range, 0.666 to 0.808), indicating excellent agreement when using the newly revised Edelson classification. The mechanism was categorized as the shoulder being in a position of forward flexion, abduction, and internal rotation in Edelson I‐IV and bicipital fractures. For the greater tuberosity fracture, the mechanism was classified into two mechanisms based on the presence of a combined dislocation. Bicipital groove fracture is a commonly observed fracture pattern, and included in the revised Edelson classification. Conclusions The revised Edelson classification proposed was more in line with the injury mechanism of the fracture, was beneficial in identifying more fracture types such as bicipital groove fracture, and verified to be a good proximal humeral fracture classification with good reliability compared with the traditional Edelson classification.

The total variation (TV) method favors solutions with the piece-wise constant assumption of the desired image from sparse-view sampling, for example, simple geometric images with flat intensity. When the phantoms become more complex and contain complicated textures, for example, high-resolution phantom and lung CT images, the images reconstructed by TV regularization may lose their contrast and fine structures. One of the optimally sparse transforms for images, the shearlet transform, has C2 without discontinuities on C2 curves, giving excellent sensitive directional information as compared with other wavelet transform approaches. Here, we developed a Shearlet-Sparse Regularization (SSR) algorithm solved with the Alternating Direction Method of Multipliers (ADMM) to overcome this limitation. With the strengthened characteristics of SSR, we performed one simulation experiment and two real experiments using a NeuViz 64 X-ray CT scanning system to measure the performance and properties of proposed algorithm. The results demonstrate that the SSR method exhibits the advantage of providing high-quality directional information and contrast as compared with TV.

Most optimization problems arising in imaging science involve high-dimensional linear operators and their adjoints. In the implementations of these operators, changes may be introduced for various practical considerations (e.g., memory limitation, computational cost, convergence speed), leading to an adjoint mismatch. This occurs for the X-ray tomographic inverse problems found in computed tomography (CT), where a surrogate operator often replaces the adjoint of the measurement operator (called the projector). The resulting adjoint mismatch can jeopardize the convergence properties of iterative schemes used for image recovery. In this paper, we study the theoretical behavior of a panel of primal-dual proximal algorithms, which rely on forward-backward-(forward) splitting schemes when an adjoint mismatch occurs. We analyze these algorithms by focusing on the resolution of possibly nonsmooth convex penalized minimization problems in an infinite-dimensional setting. Using tools from fixed point theory, we show that they can solve monotone inclusions beyond minimization problems. Such findings indicate that these algorithms can be seen as a generalization of classical primal-dual formulations. The applicability of our findings is also demonstrated through two numerical experiments in the context of CT image reconstruction. [ABSTRACT FROM AUTHOR]

Sparse view (SV) computed tomography (CT) is a clinical diagnostic technique aimed at reducing radiation dose to the human body from X-rays. However, SVCT reconstructed using conventional filtered back-projection (FBP) algorithms will produce severe streak artifacts. In recent years, convolutional neural networks (CNNs) have demonstrated considerable success in addressing the inverse problem of SVCT reconstruction. However, the local convolutional operations of CNNs ignores long-range dependencies of contextual feature information, thereby limiting their capability in feature extraction. To address this limitation, we propose a dual-domain end-to-end network (DdeNet) combining Pale-Transformer and Laplacian convolution for SVCT reconstruction. Initially, in the projection domain, a UNet is employed to restore the interpolated sinogram. Subsequently, in the image domain, we introduce Pale-Transformer and Laplacian convolution to design a dual-stream feature fusion network for finely restoring the CT images reconstructed by FBP. The dual-stream feature fusion network comprises an image restoration network (IRN) module and an edge enhancement network (EEN) module. The depthwise separable convolution based modified pale-shaped self-attention mechanism in Pale-Transformer captures global feature information of the CT image in the IRN, while the Laplacian convolution and multi-scale convolution block with a squeeze and excitation mechanism in the EEN further extract and restore edge feature information of the CT image. Finally, the feature information output from IRN and EEN is fused along the channel dimension and then convolved to obtain the final reconstructed CT image. Experimental results demonstrate that DdeNet exhibits superior generalization and reconstruction performance compared to the previous CNN or Transformer-based reconstruction methods. [ABSTRACT FROM AUTHOR]

• A method for preparing brittle samples with a hidden spherical hole using an EPS sphere is proposed. • RFPA3D is used to simulate the deformation and fracture processes of samples containing holes. • The failure modes of the samples are analyzed based on CT reconstruction results. • The fracture behavior of the sample with a hidden spherical hole exhibits 3D characteristics. The preparation processes and fracture behaviors of samples with hidden spherical holes are challenges in the field of brittle materials. In this study, a new method was proposed for preparing cement mortar samples with hidden spherical holes by exploiting the high temperature decomposition of an expanded polystyrene (EPS) ball. Based on uniaxial compression tests, numerical simulations, computed tomography (CT) imaging and three-dimensional (3D) reconstruction technology, the effects of a spherical hole on the mechanical properties, stress environment, fracture development, and final fractured surfaces of samples were investigated from multiple perspectives. The results were compared with those of samples containing through-going holes. The macroscopic mechanical parameters and fractured surfaces obtained from rock failure process analysis (RFPA3D) matched well with the test results, and the different types of holes affected the deterioration of the mechanical properties of brittle materials to different degrees. The peak strength and elastic modulus of the samples with hidden spherical holes decreased by 25.12% and 20.18%, respectively, relative to those of the samples with through-going holes. The stress concentrations of the samples with hidden spherical holes were slightly lower than those with through-going holes. The numerical simulation and CT reconstruction results showed that the fracture behaviors of samples with hidden spherical holes exhibited significant 3D characteristics. Conversely, samples with through-going holes mainly exhibited two-dimensional (2D) failure characteristics. [ABSTRACT FROM AUTHOR]

Objective: Edelson classification is a 3D classification of proximal humeral fractures, but there is a scarcity of application of this classification in large samples, and the accuracy of classification was also not testified. The objective of this research was to verify whether a revised Edelson classification produces satisfactory agreement for proximal humeral fracture classification in adult patients. Methods: A total of 827 proximal humeral fractures (304 male and 520 female patients, 58.0 ± 16.2 years) were found retrospectively from January 2014 to December 2019, and classified according to the traditional and newly proposed Edelson classification. The three‐dimensional CT images were processed, rotated and visualized within software. Five shoulder surgeons classified each fracture. After data collection, radiographic classifications results were compared by inter‐ and intraobserver analysis with the method of weighted kappa coefficients. Fracture classification based on Edelson and revised Edelson classification was presented and compared. Results: The mean k value for the interobserver reliability was 0.748 (range, 0.583 to 0.958) compared with Edelson classification (0.548, range, 0.48 to 0.635), indicating satisfactory agreement. The mean k value for intraobserver reliability was 0.906 (range, 0.823 to 0.943) compared with Edelson classification (0.762, range, 0.666 to 0.808), indicating excellent agreement when using the newly revised Edelson classification. The mechanism was categorized as the shoulder being in a position of forward flexion, abduction, and internal rotation in Edelson I‐IV and bicipital fractures. For the greater tuberosity fracture, the mechanism was classified into two mechanisms based on the presence of a combined dislocation. Bicipital groove fracture is a commonly observed fracture pattern, and included in the revised Edelson classification. Conclusions: The revised Edelson classification proposed was more in line with the injury mechanism of the fracture, was beneficial in identifying more fracture types such as bicipital groove fracture, and verified to be a good proximal humeral fracture classification with good reliability compared with the traditional Edelson classification. [ABSTRACT FROM AUTHOR]

Background: Remarkable progress has been made for low‐dose computed tomography (CT) reconstruction tasks by applying deep learning techniques. However, establishing an intrinsic link between deep learning techniques and CT texture preservation is still one of the significant challenges for researchers to further improve the effect of low‐dose CT (LDCT) reconstruction. Purpose: Most of the existing deep learning‐based LDCT reconstruction methods are derived from popular frameworks, and most models focus on the image domain. Even few existing methods start with dual domains (sinogram and image) by considering the processing of the data itself, the final performances are limited due to the lack of perception of textures. With this in mind, we propose a method for texture perception on dual domains, so that the reconstruction process can be uniformly driven by visual effects. Methods: The proposed method involves the processing of two domains: the sinogram domain and the image domain. For the sinogram domain, we have designed a novel dilated residual network (S‐DRN) which aims to increase the receptive field to obtain multiscale information. For the image domain, we propose a self‐attention (SA) residual encoder & decoder network (SRED‐Net) as the denoising network for obtaining much acceptable edges and textures. In addition, the composite loss function composed of the feature loss constructed by the proposed boundary and texture feature‐aware network (BTFAN) and the mean square error (MSE) can obtain a higher image quality while retaining more details and fewer artifacts, thereby obtaining better visual image quality. Results: The proposed method was validated using both the American association of physicists in medicine (AAPM)‐Mayo clinic LDCT data sets and a real clinic data. Experimental results demonstrated that the new method has achieved the state‐of‐the‐art performance on objective indicators and visual metrics in terms of denoising and texture restoration. Conclusions: Compared with single‐domain or existing dual‐domain processing strategies, the proposed texture‐aware dual domain mapping network (TADDM‐Net) can much better improve the visual effect of reconstructed CT images. Meantime, we also provide much intuitive evidence in terms of model interpretability. [ABSTRACT FROM AUTHOR]

The reconstruction of sparsely sampled projection data will generate obvious streaking artifacts, resulting in image quality degradation and affecting medical diagnosis results. Wavelet transform can effectively decompose directional components of image, so the artifact features and edge details with high directionality can be better detected in the wavelet domain. Therefore, a hybrid domain method based on wavelet transform is proposed in this paper for the sparse-view CT reconstruction. The reconstruction model combines wavelet, spatial, and radon domains to restore the projection consistency and enhance image details. In addition, the global distribution of artifacts requires the network to have a large receptive field, so that a multi-level wavelet transform network (MWCNN) is applied to the hybrid domain model. Wavelet transform is used in the encoding part of the network to reduce the size of feature maps instead of pooling operation and inverse wavelet transform is deployed in the decoding part to recover image details. The proposed method can achieve PSNR of 41.049 dB and SSIM of 0.958 with 120 projections of three angular intervals, and obtain the highest values in this paper. Through the results of numerical analysis and reconstructed images, it shows that the hybrid domain method is superior to the single-domain methods. At the same time, the multi-level wavelet transform model is more suitable for CT reconstruction than the single-level wavelet transform. [ABSTRACT FROM AUTHOR]

Objective: Involuntary subject motion is the main source of artifacts in weight-bearing cone-beam CT of the knee. To achieve image quality for clinical diagnosis, the motion needs to be compensated. We propose to use inertial measurement units (IMUs) attached to the leg for motion estimation. Methods: We perform a simulation study using real motion recorded with an optical tracking system. Three IMU-based correction approaches are evaluated, namely rigid motion correction, non-rigid 2D projection deformation and non-rigid 3D dynamic reconstruction. We present an initialization process based on the system geometry. With an IMU noise simulation, we investigate the applicability of the proposed methods in real applications. Results: All proposed IMU-based approaches correct motion at least as good as a state-of-the-art marker-based approach. The structural similarity index and the root mean squared error between motion-free and motion corrected volumes are improved by 24-35% and 78-85%, respectively, compared with the uncorrected case. The noise analysis shows that the noise levels of commercially available IMUs need to be improved by a factor of $10^{5}$ which is currently only achieved by specialized hardware not robust enough for the application. Conclusion: Our simulation study confirms the feasibility of this novel approach and defines improvements necessary for a real application. Significance: The presented work lays the foundation for IMU-based motion compensation in cone-beam CT of the knee and creates valuable insights for future developments. [ABSTRACT FROM AUTHOR]

Additive manufacturing has enabled surface-based sheet lattice to emerge as a new mechanical metamaterial with exceptional weight specific strength properties and multifunction. A novel design method of shell offset has been developed here for enhancement of mechanical properties and domain controllability of separated spaces by sheet lattice. Using selective laser melting, Ti6Al4V lattice samples were fabricated for compression experiments, followed by Micro-CT reconstruction. Manufacturing deviations with mean values of statistical analysis were taken into numerical simulations for prediction of mechanical properties. Investigation results show that increasing the shell offset is an effective strategy to improve mechanical properties and energy absorption of sheet lattices by more than 200% competing with net lattices, which is attributed to the higher second moment of area and successive layer-by-layer deformation mechanism of optimized unit type. In addition, a feasible adjustment region of 30–55% has been found, in which spatial volume fraction of the two independent void phases can be controlled for flow regulation of heat exchange without changing their mechanical properties. Those design concepts of sheet lattices will be valuable in future thermal management applications with lightweight protection.

In recent years, the neural networks are frequently adopted to address the issues of cone beam CT imaging. However, most of the research so far has been to build neural networks individually either in the image domain or in the projection domain for specific purposes, while the connections between them are not well exploited. A reconstruction network that focuses on the filtering and backward projection process can fully exploit the potential connections between the projection domain and the image domain, and can be plugged into other learned reconstruction models for high-quality results. However, until now most of the existing reconstruction networks have taken the fully-connected layer as the backbone, which leads to an explosive growth in model parameters, especially in reconstruction of cone-beam CT. In this paper, a sparse-sharing-reconstruction network (SSRNet) with sparse connections and multi-group weight sharing is proposed, which can be regarded as a substitute of the filtering and backward projection process and can significantly reduce the number of network parameters up to 0.4% of that of the previous models. The experimental results show that the reconstruction results of the 2D SSRNet are basically consistent with those of the traditional FBP algorithm. The 3D SSRNet outperforms the traditional FDK algorithm at 50 layers away from the midplane, while still keeping the number of network parameters within a manageable range. [ABSTRACT FROM AUTHOR]

The goal of this study is to develop a new computed tomography (CT) image reconstruction method, aiming at improving the quality of the reconstructed images of existing methods while reducing computational costs. Existing CT reconstruction is modeled by pixel-based piecewise constant approximations of the integral equation that describes the CT projection data acquisition process. Using these approximations imposes a bottleneck model error and results in a discrete system of a large size. We propose to develop a content-adaptive unstructured grid (CAUG) based regularized CT reconstruction method to address these issues. Specifically, we design a CAUG of the image domain to sparsely represent the underlying image, and introduce a CAUG-based piecewise linear approximation of the integral equation by employing a collocation method. We further apply a regularization defined on the CAUG for the resulting ill-posed linear system, which may lead to a sparse linear representation for the underlying solution. The regularized CT reconstruction is formulated as a convex optimization problem, whose objective function consists of a weighted least square norm based fidelity term, a regularization term and a constraint term. Here, the corresponding weighted matrix is derived from the simultaneous algebraic reconstruction technique (SART). We then develop a SART-type preconditioned fixed-point proximity algorithm to solve the optimization problem. Convergence analysis is provided for the resulting iterative algorithm. Numerical experiments demonstrate the superiority of the proposed method over several existing methods in terms of both suppressing noise and reducing computational costs. These methods include the SART without regularization and with the quadratic regularization, the traditional total variation (TV) regularized reconstruction method and the TV superiorized conjugate gradient method on the pixel grid. [ABSTRACT FROM AUTHOR]

Coordinate system definition is a critical element of biomechanical modeling of the knee, and cases of skeletal trauma present major technical challenges. This paper presents a method to define a tibial coordinate system by fitting geometric primitives to surface anatomy requiring minimal user input. The method presented here utilizes a conical fit to both the tibial shaft and femoral condyles to generate independent axes forming the basis of a tibial coordinate system. Definition of the tibial axis showed high accuracy when shape fitting to ≥50 mm of shaft with

Among many of the image reconstruction techniques; the Feldkamp-Davis-Kress (FDK) is considered the most practical algorithm that used in clinical cone-beam Computed Tomography (CT) technology. In this paper, we present a full memory-aware management optimization scheme to reconstruct high-resolution volumes for a large number of X-Ray images as projections based on an efficient GPU acceleration technique. The proposed optimization technique is a scheme for FDK acceleration, that sheds new light on 4 new strategies: (1) Overcome limited device memory by evaluating different four configurations to migrate input and output data in the CPU/GPU system, (2) Reduce data transfer time during the FDK execution by using a Unified Memory (UM) model with data prefetching feature during reconstruction for data migration between host and device, (3) Accelerate the bottleneck algorithm back-projection as a result of computation mitigation, and (4) Optimize projection data layout that led to better device memory access and overcomes memory latency problem before processing and computing on GPU. The experimental results based on the proposed method show that it took 21.17 sec and 101.76 sec to reconstruct high-resolution 10243,20483- voxel volume from 1800 projection of 10242and 20482-pixel respectively. The performance resulted from our optimization technique considered on-the-fly reconstruction (real-time), which means it can process 85 projections per second for high-resolution volumes from a large number of projections to achieve accuracy and resolution for reconstructed volume. In the term of speedup, the proposed method can achieve 1.72 speedup to reconstruct the low-resolution volume5123, 1.82 speedup for 10243 volume and 1.70speedupfor20483 for highest resolution volumes over baseline implementation and optimized over previous reconstruction methods based on FDK.

Multi-energy spectral CT has a broader range of applications with the recent development of photon-counting detectors. However, the photons counted in each energy bin decrease when the number of energy bins increases, which causes a higher statistical noise level of the CT image. In this work, we propose a novel iterative dynamic dual-energy CT algorithm to reduce the statistical noise. In the proposed algorithm, the multi-energy projections are estimated from the dynamic dual-energy CT data during the iterative process. The proposed algorithm is verified on sufficient numerical simulations and a laboratory two-energy-threshold PCD system. By applying the same reconstruction algorithm, the dynamic dual-energy CT’s final reconstruction results have a much lower statistical noise level than the conventional multi-energy CT. Moreover, based on the analysis of the simulation results, we explain why the dynamic dual-energy CT has a lower statistical noise level than the conventional multi-energy CT. The underlying idea is to sample sparse in the energy dimension, which can be done because there is a high correlation between projection data of different energy bins. [ABSTRACT FROM AUTHOR]

Coordinate system definition is a critical element of biomechanical modeling of the knee, and cases of skeletal trauma present major technical challenges. This paper presents a method to define a tibial coordinate system by fitting geometric primitives to surface anatomy requiring minimal user input. The method presented here utilizes a conical fit to both the tibial shaft and femoral condyles to generate independent axes forming the basis of a tibial coordinate system. Definition of the tibial axis showed high accuracy when shape fitting to ≥50 mm of shaft with <3° of angular variation from the axis obtained using the full tibia. Repeatability and reproducibility of the axis was compared using intraclass correlation coefficients which showed excellent intra- and inter-observer agreement across cases. Additionally, shape fitting to the distal femoral condyles showed high accuracy compared to the reference axis established automatically through identifying the medial and lateral epicondyles (<4°). Utilizing geometric primitives to estimate functional axes for the tibia and femur removes reliance on anatomical landmarks that can be displaced by fracture or inaccurately identified by observers. Furthermore, fitting of such primitives provides a more complete understanding of the true bony anatomy, which cannot be done through simple landmark identification. [ABSTRACT FROM AUTHOR]

Background: To date, there is no consensus on when and how to perform acromioplasty during rotator cuff repair (RCR). We aimed to determine the volume of impinging bone removed during acromioplasty and whether it influences postoperative range of motion (ROM) and clinical scores after RCR. Methods: Preoperative and postoperative computed tomography scans of 57 shoulders that underwent RCR were used to reconstruct scapula models to simulate volumes of impinging acromial bone preoperatively and then compare them to the volumes of bone resected postoperatively to calculate the proportions of desired (ideal) vs. unnecessary (excess) resections. All patients were evaluated preoperatively and at 6 months to assess ROM and functional scores. Results: The volume of impinging bone identified was 3.5 ± 2.3 cm3, of which 1.6 ± 1.2 cm3 (50% ± 27%) was removed during acromioplasty. The volume of impinging bone identified was not correlated with preoperative critical shoulder angle (r = 0.025, P = .853), nor with glenoid inclination (r = −0.024, P = .857). The volume of bone removed was 3.7 ± 2.2 cm3, of which 2.1 ± 1.6 cm3 (53% ± 24%) were unnecessary resections. Multivariable analyses revealed that more extensive removal of impinging bone significantly improved internal rotation with the arm at 90° of abduction (beta, 27.5, P = .048) but did not affect other shoulder movements or clinical scores. Conclusions: Acromioplasty removed only 50% of the estimated volume of impinging acromial bone. More extensive removal of impinging bone significantly improved internal rotation with the arm at 90° of abduction.

Purpose: Recently, diffusion posterior sampling (DPS), where score-based diffusion priors are combined with likelihood models, has been used to produce high-quality computed tomography (CT) images given low-quality measurements. This technique permits one-time, unsupervised training of a CT prior, which can then be incorporated with an arbitrary data model. However, current methods rely on a linear model of X-ray CT physics to reconstruct. Although it is common to linearize the transmission tomography reconstruction problem, this is an approximation to the true and inherently nonlinear forward model. We propose a DPS method that integrates a general nonlinear measurement model., Approach: We implement a traditional unconditional diffusion model by training a prior score function estimator and apply Bayes' rule to combine this prior with a measurement likelihood score function derived from the nonlinear physical model to arrive at a posterior score function that can be used to sample the reverse-time diffusion process. We develop computational enhancements for the approach and evaluate the reconstruction approach in several simulation studies., Results: The proposed nonlinear DPS provides improved performance over traditional reconstruction methods and DPS with a linear model. Moreover, as compared with a conditionally trained deep learning approach, the nonlinear DPS approach shows a better ability to provide high-quality images for different acquisition protocols., Conclusion: This plug-and-play method allows the incorporation of a diffusion-based prior with a general nonlinear CT measurement model. This permits the application of the approach to different systems, protocols, etc., without the need for any additional training., (© 2024 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Introduction: Numerous classifications have been developed to assess tibial plateau fractures (TPF). Of these, the Schatzker system is the most widely reported in the literature yet this system is limited in its characterisation of morphological fracture features underlying the fracture location. The purpose of this study was to compare 3D morphological features of TPFs across different Schatzker types.Methods: This study retrospectively analysed preoperative TPF imaging data to reconstruct 3D models of the fractures. Ninety-one fractures (29 female, 62 male) were analysed and classified using Schatzker. Fracture location across Schatzker types was compared based on division of the articular surface into six 'zones'. Additionally, morphological characteristics of the fractures were compared based on fracture type, including; the number, volume and shape of the fragments.Results: Schatzker II, IV and VI fractures were most common, making up 41%, 16% and 20%, respectively. Type II fractures commonly involved both the lateral and central aspect of the tibial plateau, similarly, type IV fractures incorporated the lateral condyle in most cases. Considering the morphological metrics, statistically significant differences were observed between Schatzker types for the number of; total, articular, cortical and volumetrically significant (all P < 0.001) fragments along with the volume of both primary (P < 0.001) and secondary (P = 0.02) fragments.Discussion: Assessment of underlying fracture characteristics in addition to fracture location can serve to provide greater detail relating to fracture morphology, which has the potential to assist with both surgical decision making and assessment of postoperative outcomes. Incorporating this information as part of a hierarchical or multifactorial framework for classifying fractures may help distinguish subtle differences between fracture types that are classifiable using the most current systems. [ABSTRACT FROM AUTHOR]

OBJECTIVE: In order to solve the blurred structural details and over-smoothing effects in sparse representation dictionary learning reconstruction algorithm, this study aims to test sparse angle CT reconstruction with weighted dictionary learning algorithm based on adaptive Group-Sparsity Regularization (AGSR-SART). METHODS: First, a new similarity measure is defined in which Covariance is introduced into Euclidean distance, Non-local image patches are adaptively divided into groups of different sizes as the basic unit of sparse representation. Second, the weight factor of the regular constraint terms is designed through the residuals represented by the dictionary, so that the algorithm takes different smoothing effects on different regions of the image during the iterative process. The sparse reconstructed image is modified according to the difference between the estimated value and the intermediate image. Last, The SBI (Split Bregman Iteration) iterative algorithm is used to solve the objective function. An abdominal image, a pelvic image and a thoracic image are employed to evaluate performance of the proposed method. RESULTS: In terms of quantitative evaluations, experimental results show that new algorithm yields PSNR of 48.20, the maximum SSIM of 99.06% and the minimum MAE of 0.0028. CONCLUSIONS: This study demonstrates that new algorithm can better preserve structural details in reconstructed CT images. It eliminates the effect of excessive smoothing in sparse angle reconstruction, enhances the sparseness and non-local self-similarity of the image, and thus it is superior to several existing reconstruction algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a fast regularized method for limited angle CT, the preconditioning matrix, which includes a weighting matrix and a filtering matrix, is utilized to improve the convergence speed. The projection and back-projection in the iteration process are replaced by the analytic operators to accelerate the computation speed. In addition, a Hamming window function is added to further suppress the noise and artifacts. Real data experiments show that the proposed method outperforms the other three methods in preserving the edge, convergence speed and computing speed. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to evaluate the reliability and accuracy of radiographic assessment of pedicle screw placement, using computed tomography (CT) as the reference standard. Materials and Methods: Fifteen patients who underwent posterior spinal fusion were retrospectively reviewed. Pedicle screw position was rated using postoperative anteroposterior and lateral radiographs by two independent spinal surgeons twice at least four weeks apart. CT was rated by two other spinal surgeons collectively and reached a consensus decision. Kappa analysis was used to measure the relative agreement between radiographic ratings and CT evaluation. Results: A total of 367 screws were evaluated. According to CT evaluation, 69 (18.8%) pedicle screws were determined as malpositioned. However, in plain radiographs, 53 (14.4%) pedicle screws were rated as malpositioned. The intraobserver reliability of radiographic ratings was almost perfect for both observers at both times (ICC=0.833 and 0.817). On the other hand, the interobserver reliability was substantial at both occasions (ICC=0.722 and 0.808). The agreement between radiographic ratings and CT evaluation (reference standard) was substantial (kappa=0.686, 95% confidence interval=0.586-0.785). The sensitivity and specificity of radiographs to detect a malpositioned screw were 65.2% and 97.3%, respectively. Conclusion: Radiographic ratings showed substantial and almost perfect agreement among observers. However, the accuracy of radiography was low compared to CT evaluation. In case of suspicion, CT should be the choice of the imaging modality to decide on a malpositioned screw. [ABSTRACT FROM AUTHOR]

Purpose: Computed tomography image reconstruction using truncated or sparsely acquired projection data to reduce radiation dose, iodine volume, and patient motion artifacts has been widely investigated. To continue these efforts, we investigated the use of machine learning‐based reconstruction techniques using deep convolutional generative adversarial networks (DCGANs) and evaluated its effect using standard imaging metrics. Methods: Ten thousand head computed tomography (CT) scans were collected from the 2019 RSNA Intracranial Hemorrhage Detection and Classification Challenge dataset. Sinograms were simulated and then resampled in both a one‐third truncated and one‐third sparse manner. DCGANs were tasked with correcting the incomplete projection data, either in the sinogram domain where the full sinogram was recovered by the DCGAN and then reconstructed, or the reconstruction domain where the incomplete data were first reconstructed and the sparse or truncation artifacts were corrected by the DCGAN. Seventy‐five hundred images were used for network training and 2500 were withheld for network assessment using mean absolute error (MAE), structural similarity index measure (SSIM), and peak signal‐to‐noise ratio (PSNR) between results of different correction techniques. Image data from a quality‐assurance phantom were also resampled in the two manners and corrected and reconstructed for network performance assessment using line profiles across high‐contrast features, the modulation transfer function (MTF), noise power spectrum (NPS), and Hounsfield Unit (HU) linearity analysis. Results: Better agreement with the fully sampled reconstructions were achieved from sparse acquisition corrected in the sinogram domain and the truncated acquisition corrected in the reconstruction domain. MAE, SSIM, and PSNR showed quantitative improvement from the DCGAN correction techniques. HU linearity of the reconstructions was maintained by the correction techniques for the sparse and truncated acquisitions. MTF curves reached the 10% modulation cutoff frequency at 5.86 lp/cm for the truncated corrected reconstruction compared with 2.98 lp/cm for the truncated uncorrected reconstruction, and 5.36 lp/cm for the sparse corrected reconstruction compared with around 2.91 lp/cm for the sparse uncorrected reconstruction. NPS analyses yielded better agreement across a range of frequencies between the resampled corrected phantom and truth reconstructions. Conclusions: We demonstrated the use of DCGANs for CT‐image correction from sparse and truncated simulated projection data, while preserving imaging quality of the fully sampled projection data. [ABSTRACT FROM AUTHOR]

The reconstruction of sparsely sampled projection data will generate obvious streaking artifacts, resulting in image quality degradation and affecting medical diagnosis results. Wavelet transform can effectively decompose directional components of image, so the artifact features and edge details with high directionality can be better detected in the wavelet domain. Therefore, a hybrid domain method based on wavelet transform is proposed in this paper for the sparse-view CT reconstruction. The reconstruction model combines wavelet, spatial, and radon domains to restore the projection consistency and enhance image details. In addition, the global distribution of artifacts requires the network to have a large receptive field, so that a multi-level wavelet transform network (MWCNN) is applied to the hybrid domain model. Wavelet transform is used in the encoding part of the network to reduce the size of feature maps instead of pooling operation and inverse wavelet transform is deployed in the decoding part to recover image details. The proposed method can achieve PSNR of 41.049 dB and SSIM of 0.958 with 120 projections of three angular intervals, and obtain the highest values in this paper. Through the results of numerical analysis and reconstructed images, it shows that the hybrid domain method is superior to the single-domain methods. At the same time, the multi-level wavelet transform model is more suitable for CT reconstruction than the single-level wavelet transform.

Nowadays, with the rapid development of computed tomography (CT), the theoretical expansion of CT reconstruction has become the crucial ingredient to break through technical bottlenecks. This article supplements relevant knowledge of the method for constructing CT projection-wise filters. From the perspective of the mathematical principle of CT reconstruction, the second-order divided-difference back projection (SDBP) technique is firstly proposed, which is an implementation of accurate and efficient inverse Radon transform. On the basis of the SDBP technique, a brand new computational model for filter expressions is derived. The model reveals the correlation between the convolution kernel for data restoration and the filter expression and also specifies the principle of filter construction. On the proposed filter construction model, the decomposition form of filters is discovered for the first time. A series of basic filters are acquired, which can be used to compose filters in actual need. According to the superposition principle, the properties of a filter depend on the decomposed basic filters. The selection of the kernel function on the properties of basic filters clarifies the general rule of filter construction. These theories proposed in this article are of reference value for the filter optimization research in the CT reconstruction field.

Objective . Despite recent advancements in quantum computing, the limited number of available qubits has hindered progress in CT reconstruction. This study investigates the feasibility of utilizing quantum annealing-based computed tomography (QACT) with current quantum bit levels. Approach . The QACT algorithm aims to precisely solve quadratic unconstrained binary optimization problems. Furthermore, a novel approach is proposed to reconstruct images by approximating real numbers using the variational method. This approach allows for accurate CT image reconstruction using a small number of qubits. The study examines the impact of projection data quantity and noise on various image sizes ranging from 4 × 4 to 24 × 24 pixels. The reconstructed results are compared against conventional reconstruction algorithms, namely maximum likelihood expectation maximization (MLEM) and filtered back projection (FBP). Main result . By employing the variational approach and utilizing two qubits for each pixel of the image, accurate reconstruction was achieved with an adequate number of projections. Under conditions of abundant projections and lower noise levels, the image quality in QACT algorithm outperformed that of MLEM and FBP algorithms. However, in situations with limited projection data and in the presence of noise, the image quality in QACT was inferior to that in MLEM. Significance . This study developed the QACT reconstruction algorithm using the variational approach for real-number reconstruction. Remarkably, only 2 qubits were required for each pixel representation, demonstrating their sufficiency for accurate reconstruction., (© 2024 Institute of Physics and Engineering in Medicine.)

X-ray computed tomography of the cladotherian Henkelotherium guimarotae from the Kimmeridgian Guimarota coal mine (Portugal) adds new information on its postcranial skeleton. Contrary to earlier description, the humerus of Henkelotherium shows a plesiomorphic cladotherian condition similar to that seen in Dryolestes leiriensis in having individual radial and ulnar condyles on the anterior aspect of the distal joint, whereas the posterior aspect appears to be trochlear. The scapula is also more plesiomorphic than previously assumed in having a laterally flared crest on the inferior margin. In this regard, it is similar to the scapula of spalacotherioid "symmetrodontans" such as Zhangheotherium but more derived than the latter, in having a larger supraspinous fossa. The hind limb is similar to that of small extant therians, which is interpreted to be assocated with an upright limb posture. The previously proposed hypothesis of an arboreal lifestyle for Henkelotherium is corroborated by a number of skeletal features such as large humeral entepicondyles, prominent digital flexor tubercles, elongated and slender tail vertebrae, as well as a high pedal phalangeal index of 153%. [ABSTRACT FROM AUTHOR]

Sparse view computed tomography (SVCT) aims to reduce the number of X-ray projection views required for reconstructing the cross-sectional image of an object. While SVCT significantly reduces X-ray radiation dose and speeds up scanning, insufficient projection data give rise to issues such as severe streak artifacts and blurring in reconstructed images, thereby impacting the diagnostic accuracy of CT detection. To address this challenge, a dual-domain reconstruction network incorporating multi-level wavelet transform and recurrent convolution is proposed in this paper. The dual-domain network is composed of a sinogram domain network (SDN) and an image domain network (IDN). Multi-level wavelet transform is employed in both IDN and SDN to decompose sinograms and CT images into distinct frequency components, which are then processed through separate network branches to recover detailed information within their respective frequency bands. To capture global textures, artifacts, and shallow features in sinograms and CT images, a recurrent convolution unit (RCU) based on convolutional long and short-term memory (Conv-LSTM) is designed, which can model their long-range dependencies through recurrent calculation. Additionally, a self-attention-based multi-level frequency feature normalization fusion (MFNF) block is proposed to assist in recovering high-frequency components by aggregating low-frequency components. Finally, an edge loss function based on the Laplacian of Gaussian (LoG) is designed as the regularization term for enhancing the recovery of high-frequency edge structures. The experimental results demonstrate the effectiveness of our approach in reducing artifacts and enhancing the reconstruction of intricate structural details across various sparse views and noise levels. Our method excels in both performance and robustness, as evidenced by its superior outcomes in numerous qualitative and quantitative assessments, surpassing contemporary state-of-the-art CNNs or Transformer-based reconstruction methods.

Objective. Over the past several decades, dual-energy CT (DECT) imaging has seen significant advancements due to its ability to distinguish between materials. DECT statistical iterative reconstruction (SIR) has exhibited potential for noise reduction and enhanced accuracy. However, its slow convergence and substantial computational demands render the elapsed time for 3D DECT SIR often clinically unacceptable. The objective of this study is to accelerate 3D DECT SIR while maintaining subpercentage or near-subpercentage accuracy. Approach. We incorporate DECT SIR into a deep-learning model-based unrolling network for 3D DECT reconstruction (MB-DECTNet), which can be trained end-to-end. This deep learning-based approach is designed to learn shortcuts between initial conditions and the stationary points of iterative algorithms while preserving the unbiased estimation property of model-based algorithms. MB-DECTNet comprises multiple stacked update blocks, each containing a data consistency layer (DC) and a spatial mixer layer, with the DC layer functioning as a one-step update from any traditional iterative algorithm. Main results. The quantitative results indicate that our proposed MB-DECTNet surpasses both the traditional image-domain technique (MB-DECTNet reduces average bias by a factor of 10) and a pure deep learning method (MB-DECTNet reduces average bias by a factor of 8.8), offering the potential for accurate attenuation coefficient estimation, akin to traditional statistical algorithms, but with considerably reduced computational costs. This approach achieves 0.13% bias and 1.92% mean absolute error and reconstructs a full image of a head in less than 12 min. Additionally, we show that the MB-DECTNet output can serve as an initializer for DECT SIR, leading to further improvements in results. Significance. This study presents a model-based deep unrolling network for accurate 3D DECT reconstruction, achieving subpercentage error in estimating virtual monoenergetic images for a full head at 60 and 150 keV in 30 min, representing a 40-fold speedup compared to traditional approaches. These findings have significant implications for accelerating DECT SIR and making it more clinically feasible., (Creative Commons Attribution license.)

Quantitative computed tomography has been posed as an alternative imaging modality to investigate osteoporosis. We examined the influence of computed tomography convolution back-projection reconstruction kernels on the analysis of bone quantity and estimated mechanical properties in the proximal femur. Eighteen computed tomography scans of the proximal femur were reconstructed using both a standard smoothing reconstruction kernel and a bone-sharpening reconstruction kernel. Following phantom-based density calibration, we calculated typical bone quantity outcomes of integral volumetric bone mineral density, bone volume, and bone mineral content. Additionally, we performed finite element analysis in a standard sideways fall on the hip loading configuration. Significant differences for all outcome measures, except integral bone volume, were observed between the 2 reconstruction kernels. Volumetric bone mineral density measured using images reconstructed by the standard kernel was significantly lower (6.7%, p < 0.001) when compared with images reconstructed using the bone-sharpening kernel. Furthermore, the whole-bone stiffness and the failure load measured in images reconstructed by the standard kernel were significantly lower (16.5%, p < 0.001, and 18.2%, p < 0.001, respectively) when compared with the image reconstructed by the bone-sharpening kernel. These data suggest that for future quantitative computed tomography studies, a standardized reconstruction kernel will maximize reproducibility, independent of the use of a quantitative calibration phantom. [ABSTRACT FROM AUTHOR]

Highlights • A nonconvex and nonsmooth optimization model is investigated for limited-angle CT imaging. • The proposed method can avoid computing the inverse of a large system matrix. • We show that each bounded sequence generated by our method globally converges to a critical point. • The results by our method are shown superior than some classical CT reconstruction methods by real data experiments. Abstract Due to the restriction of computed tomography (CT) scanning environment, the acquired projection data may be incomplete for exact CT reconstruction. Though some convex optimization methods, such as total variation minimization based method, can be used for incomplete data reconstruction, the edge of reconstruction image may be partly distorted for limited-angle CT reconstruction. To promote the quality of reconstruction image for limited-angle CT imaging, in this paper, a nonconvex and nonsmooth optimization model was investigated. To solve the model, a variational proximal alternating linearized minimization (VPALM) method based on proximal mapping in a given metric was proposed. The proposed method can avoid computing the inverse of a huge system matrix thus can be used to deal with the larger-scale inverse problems. What's more, we show that each bounded sequence generated by VPALM globally converges to a critical point based on the Kurdyka–Lojasiewicz property. Real data experiments are used to demonstrate the viability and effectiveness of VPALM method, and the results show that the proposed method outperforms two classical CT reconstruction methods. [ABSTRACT FROM AUTHOR]

Docodon was the first described docodont, and has long featured prominently in the comparative and functional morphology of mammaliaform mandibles and teeth. We have now re-examined the dental and mandibular fossils of Docodon from Yale Quarry 9 at the Como Bluff site in the Late Jurassic Morrison Formation, using Computed Tomography (CT) scans and 3D image analyses. Our CT study revealed that some features used to distinguish the several Docodon "species" in historical studies many decades ago were based on incorrect manual restoration of mandibles, and on variable features of deciduous premolars, which are replaced in typical mammaliaform fashion. This supports a long-held notion that the multiple species of Docodon from the same quarry of the Morrison Formation are over-split and should be synonymized. We formally propose that the specimens examined in this study be synonymized under Docodon victor. Our new Occlusal Fingerprint Analysis (OFA) of Docodon molars supports the hypothesis that Docodon had a consistent dorso-posterior or palinal component in chewing in the disto-distolingual to distolingual direction. This helps to resolve the controversial historical alternative hypotheses postulated for tooth occlusion in the taxon. The Docodon occlusal pattern is likely a derived feature, evolved within Docodonta. A posterior component in the occlusal trajectory is a functional convergence of Docodon on unrelated traversodontid cynodonts. [ABSTRACT FROM AUTHOR]