Computed Tomography Image Reconstruction Research Articles

Preoperative Vertebral Fracture: A Prognostic Factor in Stage I-III Colorectal Cancer.

This study evaluated the prognostic impact of vertebral fractures (VFs) on the survival of patients with colorectal cancer (CRC). We included 299 patients with stage I-III CRC who had undergone elective surgery. The patients were divided into the VF group (n=94) and non-VF group (n=205). VFs were assessed using sagittal computed tomography image reconstruction (Th11-L5) performed preoperatively. Disease-free survival (DFS) and overall survival (OS) rates were analyzed. The VF group had lower 5-year DFS and OS rates compared to the non-VF group (both, p<0.001). The independent predictors of DFS were carbohydrate antigen 19-9 (CA19-9) ≥37.0 ng/ml, T3/T4 disease, stage III CRC, osteopenia, and VF; for OS, CA19-9 ≥37.0 ng/ml, stage III, osteopenia, and VF. VF, compared with osteopenia, was a more significant prognostic factor for DFS and OS in patients with stage I+ II CRC (both, p<0.001). Preoperative VF was associated with worse DFS and OS following CRC resection.

A new spectral conjugate subgradient method with application in computed tomography image reconstruction

A new spectral conjugate subgradient method is presented to solve nonsmooth unconstrained optimization problems. The method combines the spectral conjugate gradient method for smooth problems with the spectral subgradient method for nonsmooth problems. We study the effect of two different choices of line search, as well as three formulas for determining the conjugate directions. In addition to numerical experiments with standard nonsmooth test problems, we also apply the method to several image reconstruction problems in computed tomography, using total variation regularization. Performance profiles are used to compare the performance of the algorithm using different line search strategies and conjugate directions to that of the original spectral subgradient method. Our results show that the spectral conjugate subgradient algorithm outperforms the original spectral subgradient method, and that the use of the Polak–Ribière formula for conjugate directions provides the best and most robust performance.

Iterative Tomographic Image Reconstruction Algorithm Based on Extended Power Divergence by Dynamic Parameter Tuning

Computed tomography (CT) imaging plays a crucial role in various medical applications, but noise in projection data can significantly degrade image quality and hinder diagnosis accuracy. Iterative algorithms for tomographic image reconstruction outperform transform methods, especially in scenarios with severe noise in projections. In this paper, we propose a method to dynamically adjust two parameters included in the iterative rules during the reconstruction process. The algorithm, named the parameter-extended expectation-maximization based on power divergence (PXEM), aims to minimize the weighted extended power divergence between the measured and forward projections at each iteration. Our numerical and physical experiments showed that PXEM surpassed conventional methods such as maximum-likelihood expectation-maximization (MLEM), particularly in noisy scenarios. PXEM combines the noise suppression capabilities of power divergence-based expectation-maximization with static parameters at every iteration and the edge preservation properties of MLEM. The experimental results demonstrated significant improvements in image quality in metrics such as the structural similarity index measure and peak signal-to-noise ratio. PXEM improves CT image reconstruction quality under high noise conditions through enhanced optimization techniques.

Optimizing computed tomography image reconstruction for focal hepatic lesions: Deep learning image reconstruction vs iterative reconstruction

BackgroundDeep learning image reconstruction (DLIR) is a novel computed tomography (CT) reconstruction technique that minimizes image noise, enhances image quality, and enables radiation dose reduction. This study aims to compare the diagnostic performance of DLIR and iterative reconstruction (IR) in the evaluation of focal hepatic lesions. MethodsWe conducted a retrospective study of 216 focal hepatic lesions in 109 adult participants who underwent abdominal CT scanning at our institution. We used DLIR (low, medium, and high strength) and IR (0 %, 10 %, 20 %, and 30 %) techniques for image reconstruction. Four experienced abdominal radiologists independently evaluated focal hepatic lesions based on five qualitative aspects (lesion detectability, lesion border, diagnostic confidence level, image artifact, and overall image quality). Quantitatively, we measured and compared the level of image noise for each technique at the liver and aorta. ResultsThere were significant differences (p < 0.001) among the seven reconstruction techniques in terms of lesion borders, image artifacts, and overall image quality. Low-strength DLIR (DLIR-L) exhibited the best overall image quality. Although high-strength DLIR (DLIR-H) had the least image noise and fewest artifacts, it also had the lowest scores for lesion borders and overall image quality. Image noise showed a weak to moderate positive correlation with participants’ body mass index and waist circumference. ConclusionsThe optimal-strength DLIR significantly improved overall image quality for evaluating focal hepatic lesions compared to the IR technique. DLIR-L achieved the best overall image quality while maintaining acceptable levels of image noise and quality of lesion borders.

Structure-aware diffusion for low-dose CT imaging

Reducing the radiation dose leads to the x-ray computed tomography (CT) images suffering from heavy noise and artifacts, which inevitably interferes with the subsequent clinic diagnostic and analysis. Leading works have explored diffusion models for low-dose CT imaging to avoid the structure degeneration and blurring effects of previous deep denoising models. However, most of them always begin their generative processes with Gaussian noise, which has little or no structure priors of the clean data distribution, thereby leading to long-time inference and unpleasant reconstruction quality. To alleviate these problems, this paper presents a Structure-Aware Diffusion model (SAD), an end-to-end self-guided learning framework for high-fidelity CT image reconstruction. First, SAD builds a nonlinear diffusion bridge between clean and degraded data distributions, which could directly learn the implicit physical degradation prior from observed measurements. Second, SAD integrates the prompt learning mechanism and implicit neural representation into the diffusion process, where rich and diverse structure representations extracted by degraded inputs are exploited as prompts, which provides global and local structure priors, to guide CT image reconstruction. Finally, we devise an efficient self-guided diffusion architecture using an iterative updated strategy, which further refines structural prompts during each generative step to drive finer image reconstruction. Extensive experiments on AAPM-Mayo and LoDoPaB-CT datasets demonstrate that our SAD could achieve superior performance in terms of noise removal, structure preservation, and blind-dose generalization, with few generative steps, even one step only.

Multiobjective optimization guided by image quality index for limited-angle CT image reconstruction.

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.

DdeNet: A dual-domain end-to-end network combining Pale-Transformer and Laplacian convolution for sparse view CT reconstruction

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.

Geometric artifacts correction of equiangular fan-beam industrial CT system

To address the issue of geometric artifacts in equiangular fan-beam industrial computed tomography (CT) systems, this paper proposes a projection sinogram-based method for correcting these artifacts. Firstly, we discuss the formation mechanism of geometric artifacts in equiangular fan-beam industrial CT system when there is geometric deviation. Secondly, we establish a geometric model that incorporates these deviations and mathematically describe the projected position curve of a point within the plane. Subsequently, we use a needle gauge as a surrogate for the point, and fit the curve in the projection sinusoidal graph obtained from scanning the needle gauge to calibrate the geometric deviations. Finally, we introduce the calibrated geometric deviations into the filtered back projection (FBP) algorithm to achieve more accurate reconstructed images. The proposed method precisely calibrates geometrical deviations in the equiangular fan-beam industrial computed tomography (CT) system and effectively eliminates geometric artifacts in the reconstructed CT images, this is demonstrated by simulation and practical experiment. This method has also been successfully applied in engineering practice.

Image fast reconstruction for sparse view computed tomography with reduced sampling integration time

Industrial computed tomography (CT) can detect the internal structure of objects nondestructively. Its scanning and image reconstruction are time-consuming, which limits its application in the fast detection field. In this work, we reduce the sampling integration time in sparse view CT to achieve ultra-fast scanning in only 5 s. However, this ultra-fast scanning method produces more noise and streak artifacts in the reconstructed CT images. The existing reconstruction methods cannot effectively reconstruct high quality CT images under this ultra-fast scanning. To solve this issue, a faster and higher quality CT image reconstruction method is proposed in this paper. We construct a novel objective function using multi-directional TV and adaptive non-local means (ANLM), and design a solution algorithm to solve the objective function. The multi-directional TV-norm takes into account the differences in horizontal, vertical, and 45° directional gradients to obtain more image detail information, while the ANLM-norm focuses on noise suppression. Simulation and practical experiments show that the proposed method can reconstruct higher quality CT images under faster CT scanning strategy, and the peak signal to noise ratio (PSNR) is 1.3 dB higher than the optimal baseline algorithm. On the other hand, our method converges faster and achieves a shorter CT image reconstruction time, which is 47 s shorter than the current popular iterative reconstruction methods. This reconstruction method provides a reliable scheme for faster CT scanning and image reconstruction.

The Influence of Radiographic Parameter on the S2 Alar-Iliac Screw Virtual Trajectory in Degenerative Lumbar Scoliosis Patients: A Computed Tomography Study.

S2 alar-iliac (S2AI) screw had been widely used in the pelvic fusion for degenerative lumbar scoliosis (DLS) patients. However, whether S2AI screw trajectory was influenced by sagittal profile in DLS patients had not been comprehensively investigated. The objective of this study was to evaluate the associations between the optimal S2 alar-iliac (S2AI) screw trajectory and sagittal spinopelvic parameters in DLS patients. Computed tomography (CT) scans of pelvis were performed in 47 DLS patients for three-dimensional reconstruction of S2AI screw trajectory from September 2019 to November 2021. Five S2AI screw trajectory parameters were measured in CT reconstruction images, including: 1) angle in the transverse plane (Tsv angle); 2) angle in the sagittal plane (Sag angle); 3) maximal screw length; 4) screw width; and 5) skin distance. The lumbar Cobb angle, lumbar apical vertebral translation (AVT); global kyphosis (GK); thoracic kyphosis (TK); lumbar lordosis (LL); sagittal vertical axis (SVA); sacral slope (SS); pelvic tilt (PT); and pelvic incidence (PI) were measured in standing X-ray films of the whole spine and pelvis. Both Tsv angle and Sag angle had significant positive associations with SS (p < 0.05) but negative associations with both PT (p < 0.05) and LL (p < 0.05) in all cases. Patients with SS less than 15° had both smaller Tsv angle and Sag angle than those with SS equal to or more than 15° (p < 0.05). The decreased LL would lead to the backward rotation of the pelvis, resulting in a more cephalic and less divergent trajectory of S2AI screw in DLS patients. For DLS patients with lumbar kyphosis, spine surgeons should avoid both excessive Tsv and Sag angles for S2AI screw insertion, especially when using free-hand technique.

Metal Artifact Correction in Industrial CT Images Based on a Dual-Domain Joint Deep Learning Framework

Industrial computed tomography (CT) images reconstructed directly from projection data using the filtered back projection (FBP) method exhibit strong metal artifacts due to factors such as beam hardening, scatter, statistical noise, and deficiencies in the reconstruction algorithms. Traditional correction approaches, confined to either the projection domain or the image domain, fail to fully utilize the rich information embedded in the data. To leverage information from both domains, we propose a joint deep learning framework that integrates UNet and ResNet architectures for the correction of metal artifacts in CT images. Initially, the UNet network is employed to correct the imperfect projection data (sinograms), the output of which serves as the input for the CT image reconstruction unit. Subsequently, the reconstructed CT images are fed into the ResNet, with both networks undergoing a joint training process to optimize image quality. We take the projection data obtained by analytical simulation as the data set. The resulting optimized industrial CT images show a significant reduction in metal artifacts, with the average Peak Signal-to-Noise Ratio (PSNR) reaching 36.13 and the average Structural Similarity Index (SSIM) achieving 0.953. By conducting simultaneous correction in both the projection and image domains, our method effectively harnesses the complementary information from both, exhibiting a marked improvement in correction results over the deep learning-based single-domain corrections. The generalization capability of our proposed method is further verified in ablation experiments and multi-material phantom CT artifact correction.

Model-driven CT reconstruction algorithm for nano-resolution X-ray phase contrast imaging

Abstract The low-density imaging performance of a zone plate based nano-resolution hard X-ray computed tomography (CT) system can be significantly improved by incorporating a grating-based Lau interferometer. Due to the diffraction, however, the acquired nano-resolution phase signal may suffer splitting problem, which impedes the direct reconstruction of phase contrast CT (nPCT) images. To overcome, a new modeldriven nPCT image reconstruction algorithm is developed in this study. In it, the diffraction procedure is mathematically modeled into a matrix B, from which the projections without signal splitting can be generated invertedly. Furthermore, a penalized weighed least-square model with total variation (PWLS-TV) is employed to denoise these projections, from which nPCT images with high accuracy are directly reconstructed. Numerical experiments demonstrate that this new algorithm is able to work with phase projections having any splitting distances. Moreover, results also reveal that nPCT images of higher signal-to-noiseratio (SNR) could be reconstructed from projections having larger splitting distances. In summary, a novel model-driven nPCT image reconstruction algorithm with high accuracy and robustness is verified for the Lau interferometer based hard X-ray nano-resolution phase contrast imaging.

Split Bregman quantum noise removal algorithm for 3D reconstruction of neutron computed tomography image

The low intensity of the neutron source for neutron computed tomography (CT) results in a long acquisition time for a single projection, which causes the neutron projection data to contain a large amount of quantum noise. Quantum noise will degrade the quality of neutron CT reconstruction images. Therefore, an efficient quantum noise removal algorithm must be used in CT reconstruction. In this paper, an efficient quantum noise removal algorithm for neutron CT 3D image reconstruction is proposed by analysing classical image processing algorithms and quantum image processing algorithms, which employs the maximum likelihood expectation maximization to reconstruct the image and split Bregman algorithm to solve for the total variation (MLEM-SBTV). Experimental results show that MLEM-SBTV performs well in removing quantum noise and reconstructing the detailed structure of images.

An analytical form of ring artifact correction for computed tomography based on directional gradient domain optimization.

Ring artifact is a common problem in Computed Tomography (CT), which can lead to inaccurate diagnoses and treatment plans. It can be caused by various factors such as detector imperfections, anti-scatter grids, or other nonuniform filters placed in the x-ray beam. Physics-based corrections for these x-ray source and detector non-uniformity, in general cannot completely get rid of the ring artifacts. Therefore, there is a need for a robust method that can effectively remove ring artifacts in the image domain while preservingdetails. This study aims to develop an effective method for removing ring artifacts from reconstructed CTimages. The proposed method starts by converting the reconstructed CT image containing ring artifacts into polar coordinates, thereby transforming these artifacts into stripes. Relative Total Variation is used to extract the image's overall structural information. For the efficient restoration of intricate details, we introduce Directional Gradient Domain Optimization (DGDO) and design objective functions that make use of both the image's gradient and its overall structure. Subsequently, we present an efficient analytical algorithm to minimize these objective functions. The image obtained through DGDO is then transformed back into Cartesian coordinates, finalizing the ring artifact correctionprocess. Through a series of synthetic and real-world experiments, we have effectively demonstrated the prowess of our proposed method in the correction of ring artifacts while preserving intricate details in reconstructed CT images. In a direct comparison, our method has exhibited superior visual quality compared to several previous approaches. These results underscore the remarkable potential of our approach for enhancing the overall quality and clinical utility of CTimaging. The proposed method offers an analytical solution for removing ring artifacts from CT images while preserving details. As ring artifacts are a common problem in CT imaging, this method has high practical value in the medical field. The proposed method can improve image quality and reduce the difficulty of disease diagnosis, thereby contributing to better patientcare.

Comparison of Hounsfield Units within the Humeral Trochlea and Medial Coronoid Process in a Population of Labrador X Golden Retriever Guide Dogs and Border Collies.

The aim of this study was to determine if variations in Hounsfield units (HU) are present within the elbow between asymptomatic dogs of two breeds of dogs susceptible to elbow dysplasia. Guide Dogs and Border Collies that presented for routine computed tomography (CT) screening for elbow dysplasia prior to breeding were evaluated. All dogs had no documented history of lameness. Dogs diagnosed with CT as being free of elbow dysplasia were included. The CT images were randomized and assessed by three blinded observers. A standardised approach to CT image reconstruction to create consistent image planes was used. Hounsfield units were measured within a standardised region of interest (ROI) at the humeral trochlea and medial coronoid process. The minimum, mean and maximum HU within each ROI was recorded. Eighty-six elbows were included in the study with 32 Guide Dogs, and 11 Border Collies. Guide Dogs had significantly higher minimum (99.75 HU, 95% confidence interval [CI]: 15.02-184.48, p = 0.022), mean (115.09 HU, 95% CI: 80.53-149.64, p < 0.01) and maximum (74.00 HU, 95% CI: 44.58-103.42, p < 0.01) difference in HU within the medial coronoid process ROI, and significantly higher mean (146.49 HU, 95% CI: 100.12-192.87, p < 0.01) and maximum (147.77 HU, 95% CI: 102.57-192.97, p < 0.01) difference in HU within the humeral trochlea ROI. In this dataset breed variations in elbow HU were present between asymptomatic Guide Dogs and Border Collies. This needs to be considered in breeding screening programmes to avoid over-interpretation of elbow sclerosis, in the absence of elbow pathology.

CT image reconstruction via industrial CT fast scanning

In automated manufacturing and safety inspection, there is a high demand for fast computed tomography (CT) scanning and image reconstruction. Currently, faster scanning can be achieved by reducing the X-ray exposure time within sparse view CT. The faster scanning strategy introduces significant streak artefacts and noise during the sampling process. Consequently, streak artefacts and noise need to be simultaneously suppressed, which is poses a challenge for existing reconstruction methods. This paper presents a fast iterative reconstruction algorithm that can simultaneously suppress both streak artefacts and noise. This method can not only reconstruct high-fidelity images from rapidly acquired projection data, but also has a faster reconstruction speed than the existing iterative reconstruction algorithms. First, we present a high-order multi-directional total variation (HOM-TV) method that specifically focuses on preserving edge details of the image. Then, we present a fast iterative reconstruction model by incorporating HOM-TV and non-local means into the objective function. Finally, the effectiveness of the presented reconstruction model is validated by simulation and real experiments. The faster scanning method can complete the scan in only 5 seconds, and the structural similarity index (SSIM) of the CT image reconstructed by our method is 0.9755, which is higher than 0.0175 of the Fast Null Space Reconstruction (FNSR) algorithm. The peak signal-to-noise ratio (PSNR) index is 1.656, which is higher than that of the contrast algorithm. In terms of reconstruction time, our algorithm can achieve reconstruction in as little as 36 seconds, outperforming the baseline algorithms.

Proj2Proj: self-supervised low-dose CT reconstruction.

In Computed Tomography (CT) imaging, one of the most serious concerns has always been ionizing radiation. Several approaches have been proposed to reduce the dose level without compromising the image quality. With the emergence of deep learning, thanks to the increasing availability of computational power and huge datasets, data-driven methods have recently received a lot of attention. Deep learning based methods have also been applied in various ways to address the low-dose CT reconstruction problem. However, the success of these methods largely depends on the availability of labeled data. On the other hand, recent studies showed that training can be done successfully without the need for labeled datasets. In this study, a training scheme was defined to use low-dose projections as their own training targets. The self-supervision principle was applied in the projection domain. The parameters of a denoiser neural network were optimized through self-supervised training. It was shown that our method outperformed both traditional and compressed sensing-based iterative methods, and deep learning based unsupervised methods, in the reconstruction of analytic CT phantoms and human CT images in low-dose CT imaging. Our method's reconstruction quality is also comparable to a well-known supervised method.

Detection of Hyperdense Arterial Sign in Acute Ischemic Stroke with Dual-Energy Computed Tomography: Optimal Combination with X-ray Energy and Slice Thickness.

The hyperdense artery sign (HAS) in acute ischemic stroke (AIS) is considered an important marker of a thrombus on computed tomography (CT). An advantage of scanning with dual-energy CT (DECT) is its ability to reconstruct CT images with various energies using the virtual monochromatic imaging (VMI) technique. The aim of this study was to investigate the optimal combination of X-ray energy and slice thickness to detect HASs on DECT. A total of 32 patients with confirmed occlusion of the horizontal (M1) portion of the middle cerebral artery were included in this study. Modified contrast-to-noise ratio (modified CNR) analysis was used as a method for evaluating HASs in AIS. A region of interest (ROI) was set as an HAS, the M1 portion, and an approximately 2 cm diameter ROI was set as the background including the HAS and measured. CT images with X-ray energies from 40 to 190 keV, with increments of 10 keV, were reconstructed based on VMI with 1, 2, and 3 mm slice thicknesses. The top five combinations of X-ray energy and slice thickness in descending order of the mean HAS-modified CNR were as follows: Rank 1, 60 keV-1 mm; Rank 2, 70 keV-1 mm; Rank 3, 60 keV-2 mm; Rank 4, 80 keV-2 mm; Rank 5, 60 keV-3 mm. Our study showed that the optimal combination to detect an HAS was 60 keV and a 1 mm slice thickness on DECT.

Sparse-view image reconstruction with total-variation minimization applied to sparsely sampled projection data from SiPM-based photon-counting CT

We constructed a sparse-view computed tomography (CT) system that combines a compressed sensing (CS)-based image-reconstruction algorithm and SiPM-based photon-counting (PC) CT. CS-based image-reconstruction algorithms have been extensively studied for X-ray CT image reconstruction using fewer projections because they are expected to reduce CT imaging time and radiation exposure while maintaining CT image quality. In most previous studies, CS-based image-reconstruction algorithms have been applied to data obtained through numerical simulations or conventional dual-energy CT. However, studies on PC-CT have been scarce. Therefore, we applied a CS-based image-reconstruction algorithm to the projection data obtained using our previously established SiPM-based PC-CT system and evaluated its image quality. We prepared static phantoms equivalent to iodine-containing contrast agents and a mouse model injected with iodine-containing contrast agents as subjects. Thereafter, CT scanning was performed. The obtained projection data were downsampled to simulate a sparse-view situation, and a CS-based image-reconstruction algorithm with total-variation minimization was applied. Consequently, sparse-view CT images were successfully reconstructed, and the image quality was maintained even after downsampling the projection data (downsampling ratios of 1/10 and 1/2 for the rod phantom and mouse model, respectively). Thus, the imaging time and exposure dose could be remarkably reduced (by a factor of 10 or 2), indicating that the CS-based image-reconstruction algorithm is effective for PC-CT.

Intrahepatic and anterior course of the inferior vena cava: CT image and 3D reconstruction of a rare anatomical variation.

The widespread use of computed tomography (CT) for diagnosing and screening abdominal conditions often reveals rare, asymptomatic anomalies. There is a wide range of documented congenital variations in the anatomy of the inferior vena cava (IVC) and hepatic veins. In this report, we detail an exceptionally unusual variant of the IVC that follows a frontward and intraliver course, terminating at the anterior section of the right atrium. To gain a deeper insight into this anomaly, we employed 3D reconstruction techniques using the software Slicer and Blender.