Computed Tomography Image Reconstruction Research Articles

Non-convex optimization based optimal bone correction for various beam-hardening artifacts in CT imaging.

Tube of X-ray computed tomography (CT) system emitting a polychromatic spectrum of photons leads to beam hardening artifacts such as cupping and streaks, while the metal implants in the imaged object results in metal artifacts in the reconstructed images. The simultaneous emergence of various beam-hardening artifacts degrades the diagnostic accuracy of CT images in clinics. Thus, it should be deeply investigated for suppressing such artifacts. In this study, data consistency condition is exploited to construct an objective function. Non-convex optimization algorithm is employed to solve the optimal scaling factors. Finally, an optimal bone correction is acquired to simultaneously correct for cupping, streaks and metal artifacts. Experimental result acquired by a realistic computer simulation demonstrates that the proposed method can adaptively determine the optimal scaling factors, and then correct for various beam-hardening artifacts in the reconstructed CT images. Especially, as compared to the nonlinear least squares before variable substitution, the running time of the new CT image reconstruction algorithm decreases 82.36% and residual error reduces 55.95%. As compared to the nonlinear least squares after variable substitution, the running time of the new algorithm decreases 67.54% with the same residual error.

On the Simulation of Ultra-Sparse-View and Ultra-Low-Dose Computed Tomography with Maximum a Posteriori Reconstruction Using a Progressive Flow-Based Deep Generative Model.

Ultra-sparse-view computed tomography (CT) algorithms can reduce radiation exposure for patients, but these algorithms lack an explicit cycle consistency loss minimization and an explicit log-likelihood maximization in testing. Here, we propose X2CT-FLOW for the maximum a posteriori (MAP) reconstruction of a three-dimensional (3D) chest CT image from a single or a few two-dimensional (2D) projection images using a progressive flow-based deep generative model, especially for ultra-low-dose protocols. The MAP reconstruction can simultaneously optimize the cycle consistency loss and the log-likelihood. We applied X2CT-FLOW for the reconstruction of 3D chest CT images from biplanar projection images without noise contamination (assuming a standard-dose protocol) and with strong noise contamination (assuming an ultra-low-dose protocol). We simulated an ultra-low-dose protocol. With the standard-dose protocol, our images reconstructed from 2D projected images and 3D ground-truth CT images showed good agreement in terms of structural similarity (SSIM, 0.7675 on average), peak signal-to-noise ratio (PSNR, 25.89 dB on average), mean absolute error (MAE, 0.02364 on average), and normalized root mean square error (NRMSE, 0.05731 on average). Moreover, with the ultra-low-dose protocol, our images reconstructed from 2D projected images and the 3D ground-truth CT images also showed good agreement in terms of SSIM (0.7008 on average), PSNR (23.58 dB on average), MAE (0.02991 on average), and NRMSE (0.07349 on average).

Computational scatter correction in near real-time with a fast Monte Carlo photon transport model for high-resolution flat-panel CT

In computed tomography (CT), scattering causes server quality degradation of the reconstructed CT images by introducing streaks and cupping artifacts which reduce the detectability of low contrast objects. Monte Carlo (MC) simulation is considered the most accurate approach for scatter estimation. However, the existing MC estimators are computationally expensive, especially for high-resolution flat-panel CT. In this paper, we propose a fast and accurate MC photon transport model which describes the physics within the 1 keV to 1 MeV range using multiple controllable key parameters. Based on this model, scatter computation for a single projection can be completed within a range of a few seconds under well-defined model parameters. Smoothing and interpolation are performed on the estimated scatter to accelerate the scatter calculation without compromising accuracy too much compared to measured near scatter-free projection images. Combining the fast scatter estimation with the filtered backprojection (FBP), scatter correction is performed effectively in an iterative manner. To evaluate the proposed MC model, we have conducted extensive experiments on the simulated data and real-world high-resolution flat-panel CT. Compared to the state-of-the-art MC simulators, the proposed MC model achieved a 15times acceleration on a single-GPU compared to the GPU implementation of the Penelope simulator (MCGPU) utilizing several acceleration techniques, and a 202 times speed-up on a multi-GPU system compared to the multi-threaded state-of-the-art EGSnrc MC simulator. Furthermore, it is shown that for high-resolution images, scatter correction with sufficient accuracy is accomplished within one to three iterations using a FBP and the proposed fast MC photon transport model.

Semi-homogeneous model of coal based on 3D reconstruction of CT images and its seepage-deformation characteristics

To study the effect of isolated pores on gas seepage and coal deformation, computed tomography (CT) scanning was used to analyse the porosity and fractal characteristics of isolated pores. A geometric model of coal was then established using 3D reconstruction. Finally, a seepage-stress semi-homogeneous coupling model was established by considering the adsorption and desorption of gas. The significant difference between this model and the existing models is that the non-homogeneity of isolated pores is considered. The results show that the porosity of the isolated pores is 5.73%, accounting for 58.41% of the total pores, and the diameter is concentrated in the range of 0–10 μm. The average fractal dimension of the connected pores (1.25) is smaller than that of the isolated pores (1.35), indicating that isolated pores have a more complex structure. The gas seepage velocity is negatively correlated with the isolated pore porosity, and it varies strongly at each point of the model, with a maximum and minimum of 5.08 m/s and 1.45 × 10−4 m/s, respectively. The inhomogeneous distribution of isolated pores led to a differential deformation of the model. The total displacement of the slices varies from 13,550 μm to 14,300 μm and decreases with increasing isolated pore porosity and fractal dimension. Both low fractal dimension and low isolated pore porosity increase the deformation.

The design and evaluation of bionic porous bone scaffolds in fluid flow characteristics and mechanical properties

Background and objectiveAt present, there is a lack of efficient modeling methods for bionic artificial bone scaffolds, and the tissue fluid/nutrient mass transport characteristics of bone scaffolds has not been evaluated sufficiently. This study aims to explore an effective and efficient modeling method for biomimetic porous bone scaffolds for biological three-dimensional printing based on the imitation of the histomorphological characteristics of human vertebral cancellous bone. The fluid mass transport and mechanical characteristics of the porous scaffolds were evaluated and compared with those of a human cancellous bone,and the relationship between the geometric parameters (e.g., the size, number, shape of pores and porosity) and the performence of biomimetic porous bone scaffolds are revealed. MethodsThe bionic modeling design method proposed in this study considers the biological characteristics of vertebral cancellous tissue and performs imitation and design of vertebrae-like two-dimensional slices images.It then reconstructs the slices layer-by-layer to form porous scaffolds with a three-dimensional reconstruction method, similar to computed tomography image reconstruction. By controlling the design parameters, this method can easily realize the formation of plate-like (femoral cancellous bone-like) or rod-like (vertebral cancellous bone-like) porous scaffolds. The flow characterization of porous structures was performed using the computational fluid simulation method. ResultsThe flow characterization results showed that the permeability of the porous scaffolds and human bone was 10−8∼10−9m2,and when the porosity of the porous scaffolds was higher than 70%, the permeability was higher than that of human vertebrae with a porosity of 82%. The maximum shear stress of the designed porous scaffolds and human vertebra were less than 0.8Mpa, which was conducive to cell adhesion, cell migration, and cell differentiation. The results of 3D printing and mechanical testing showed good printability and reflected the relationship between the mechanical properties and design parameters. ConclusionsThe design method proposed in this study has many controllable parameters, which can be adjusted to generate diversified functional porous structures to meet specific needs, increase the potential of bone scaffold design, and leave room for meeting the new requirements for bone scaffold characteristics in the future.

Tumorous kidney segmentation in abdominal CT images using active contour and 3D-UNet.

The precise segmentation of the kidneys in computed tomography (CT) images is vital in urology for diagnosis, treatment, and surgical planning. Medical experts can get assistance through segmentation, as it provides information about kidney malformations in terms of shape and size. Manual segmentation is slow, tedious, and not reproducible. An automatic computer-aided system is a solution to this problem. This paper presents an automated kidney segmentation technique based on active contour and deep learning. In this work, 210 CTs from the KiTS 19 repository were used. The used dataset was divided into a train set (168 CTs), test set (21 CTs), and validation set (21 CTs). The suggested technique has broadly four phases: (1) extraction of kidney regions using active contours, (2) preprocessing, (3) kidney segmentation using 3D U-Net, and (4) reconstruction of the segmented CT images. The proposed segmentation method has received the Dice score of97.62%, Jaccard index of 95.74%, average sensitivity of 98.28%, specificity of 99.95%, and accuracy of 99.93% over the validation dataset. The proposed method can efficiently solve the problem of tumorous kidney segmentation in CT images by using active contour and deep learning. The active contour was used to select kidney regions and 3D-UNet was used for precisely segmenting the tumorous kidney.

Imaging of Structural Abnormalities of the Sacrum: The Old Faithful and Newly Emerging Techniques.

The sacrum and sacroiliac joints pose a long-standing challenge for adequate imaging because of their complex anatomical form, oblique orientation, and posterior location in the pelvis, making them subject to superimposition. The sacrum and sacroiliac joints are composed of multiple diverse tissues, further complicating their imaging. Varying imaging techniques are suited to evaluate the sacrum, each with its specific clinical indications, benefits, and drawbacks. New techniques continue to be developed and validated, such as dual-energy computed tomography (CT) and new magnetic resonance imaging (MRI) sequences, for example susceptibility-weighted imaging. Ongoing development of artificial intelligence, such as algorithms allowing reconstruction of MRI-based synthetic CT images, promises even more clinical imaging options.

FONT-SIR: Fourth-Order Nonlocal Tensor Decomposition Model for Spectral CT Image Reconstruction.

Spectral computed tomography (CT) reconstructs images from different spectral data through photon counting detectors (PCDs). However, due to the limited number of photons and the counting rate in the corresponding spectral segment, the reconstructed spectral images are usually affected by severe noise. In this paper, we propose a fourth-order nonlocal tensor decomposition model for spectral CT image reconstruction (FONT-SIR). To maintain the original spatial relationships among similar patches and improve the imaging quality, similar patches without vectorization are grouped in both spectral and spatial domains simultaneously to form the fourth-order processing tensor unit. The similarity of different patches is measured with the cosine similarity of latent features extracted using principal component analysis (PCA). By imposing the constraints of the weighted nuclear and total variation (TV) norms, each fourth-order tensor unit is decomposed into a low-rank component and a sparse component, which can efficiently remove noise and artifacts while preserving the structural details. Moreover, the alternating direction method of multipliers (ADMM) is employed to solve the decomposition model. Extensive experimental results on both simulated and real data sets demonstrate that the proposed FONT-SIR achieves superior qualitative and quantitative performance compared with several state-of-the-art methods.

LRR-CED: low-resolution reconstruction-aware convolutional encoder–decoder network for direct sparse-view CT image reconstruction

Objective. Sparse-view computed tomography (CT) reconstruction has been at the forefront of research in medical imaging. Reducing the total x-ray radiation dose to the patient while preserving the reconstruction accuracy is a big challenge. The sparse-view approach is based on reducing the number of rotation angles, which leads to poor quality reconstructed images as it introduces several artifacts. These artifacts are more clearly visible in traditional reconstruction methods like the filtered-backprojection (FBP) algorithm. Approach. Over the years, several model-based iterative and more recently deep learning-based methods have been proposed to improve sparse-view CT reconstruction. Many deep learning-based methods improve FBP-reconstructed images as a post-processing step. In this work, we propose a direct deep learning-based reconstruction that exploits the information from low-dimensional scout images, to learn the projection-to-image mapping. This is done by concatenating FBP scout images at multiple resolutions in the decoder part of a convolutional encoder–decoder (CED). Main results. This approach is investigated on two different networks, based on Dense Blocks and U-Net to show that a direct mapping can be learned from a sinogram to an image. The results are compared to two post-processing deep learning methods (FBP-ConvNet and DD-Net) and an iterative method that uses a total variation (TV) regularization. Significance. This work presents a novel method that uses information from both sinogram and low-resolution scout images for sparse-view CT image reconstruction. We also generalize this idea by demonstrating results with two different neural networks. This work is in the direction of exploring deep learning across the various stages of the image reconstruction pipeline involving data correction, domain transfer and image improvement.

An extended primal-dual algorithm framework for nonconvex problems: application to image reconstruction in spectral CT

Using the convexity of each component of the forward operator, we propose an extended primal-dual algorithm framework for solving a kind of nonconvex and probably nonsmooth optimization problems in spectral computed tomography (CT) image reconstruction. Following the proposed algorithm framework, we present six different iterative schemes or algorithms, and then establish the relationship to some existing algorithms. Under appropriate conditions, we prove the convergence of these schemes for the general case. Moreover, when the proposed schemes are applied to solving a specific problem in spectral CT image reconstruction, namely, total variation regularized nonlinear least-squares problem with nonnegative constraint, we also prove the particular convergence for these schemes by using some special properties. The numerical experiments with densely and sparsely data demonstrate the convergence and accuracy of the proposed algorithm framework in terms of visual inspection of images of realistic anatomic complexity and quantitative analysis with metrics structural similarity, peak signal-to-noise ratio, mean square error and maximum pixel difference. We analyze the computational complexity of these schemes, and discuss the extended applications of this algorithm framework in other nonlinear imaging problems.

Comparison of pedicle screw placement accuracy between two types of imaging support (Artis Zeego versus two-dimensional fluoroscopy): a cross-sectional observational study

BackgroundThe pedicle screw system is widely used in spine surgery, and it provides rigid fixation and leads to successful subsequent deformity correction and bony fusion. The standard imaging technique for pedicle screw insertion is two-dimensional images obtained from C-arm-type X-ray fluoroscopy. Artis Zeego is an emerging intraoperative imaging technique that can provide conventional two-dimensional fluoroscopic images and rapid three-dimensional fluoroscopic computed tomography reconstruction imaging. The aim of this study is to compare the insertion accuracies of PS placement using Artis Zeego and conventional 2D X-ray fluoroscopy.MethodsIn this study, we retrospectively reviewed the postoperative images of thoracolumbar fusion patients who underwent surgery using pedicle screws between 2013 and 2018. Pedicle screw malplacement was assessed using a four-grade classification by Rao et al. Misplacement rates were compared between pedicle screws assisted with Artis Zeego and two-dimensional fluoroscopy.ResultsA total of 1107 pedicle screws in 153 patients were inserted using Artis Zeego, and 427 pedicle screws in 80 patients were inserted using fluoroscopy. The overall perforation rate was 4.2% (46 perforations of 1106 pedicle screws) in the Artis Zeego group and 7.7% (33 perforations of 427 pedicle screws) in the fluoroscopy group. In the Artis Zeego group, 43 (3.9%) screws were classified as grade 1, and three (0.3%) screws were classified as grade 2. In the fluoroscopy group, 21 (4.9%) screws were classified as grade 1, 10 (2.3%) screws were classified as grade 2, and 2 (0.5%) screws were classified as grade 3. The use of Artis Zeego was associated with a significantly lower screw malplacement rate than was the use of fluoroscopy (p < 0.001).ConclusionsOur results demonstrated that pedicle screw placement with Artis Zeego was associated with a lower malplacement rate than was conventional two-dimensional fluoroscopy. No severe malplacement was observed in the Artis Zeego group. Thus, Artis Zeego could be a good option for improving pedicle screw accuracy.

Low-Dose CT Imaging of the Pelvis in Follow-up Examinations-Significant Dose Reduction and Impact of Tin Filtration: Evaluation by Phantom Studies and First Systematic Retrospective Patient Analyses.

Low-dose (LD) computed tomography (CT) is still rarely used in musculoskeletal (MSK) radiology. This study evaluates the potentials of LD CT for follow-up pelvic imaging with special focus on tin filtration (Sn) technology for normal and obese patients with and without metal implants. In a phantom study, 5 different LD and normal-dose (ND) CT protocols with and without tin filtration were tested using a normal and an obese phantom. Iterative reconstruction (IR) and filtered back projection (FBP) were used for CT image reconstruction. In a subsequent retrospective patient study, ND CT images of 45 patients were compared with follow-up tin-filtered LD CT images with a 90% dose reduction. Sixty-four percent of patients contained metal implants at the follow-up examination. Computed tomography images were objectively (image noise, contrast-to-noise ratio [CNR], dose-normalized contrast-to-noise ratio [CNRD]) and subjectively, using a 6-point Likert score, evaluated. In addition, the figure of merit was calculated. For group comparisons, paired t tests, Wilcoxon signed rank test, analysis of variance, or Kruskal-Wallis tests were used, where applicable. The LD Sn protocol with 67% dose reduction resulted in equal values in qualitative (Likert score) and quantitative image analysis (image noise) compared with the ND protocol in the phantom study. For follow-up examinations, dose could be reduced up to 90% by using Sn LD CT scans without impairment in the clinical study. However, metal implants resulted in a mild impairment of Sn LD as well as ND CT images. Cancellous bone ( P < 0.001) was assessed worse and cortical bone ( P = 0.063) equally in Sn LD CT images compared with ND CT images. Figure of merit values were significant ( P ≤ 0.02) lower and hence better in Sn LD as in ND protocols. Obese patients benefited in particular from tin filtration in LD MSK imaging in terms of image noise and CNR ( P ≤ 0.05). Low-dose CT scans with tin filtration allow maximum dose reduction while maintaining high image quality for certain clinical purposes, for example, follow-up examinations, especially metal implant position, material loosening, and consolidation controls. Overweight patients benefit particularly from tin filter technology. Although metal implants decrease image quality in ND as well as in Sn LD CT images, this is not a relevant limitation for assessability.

Multiorientation Simultaneous Computation of Back-Projection CT Image Reconstruction Algorithm in Staging Diagnosis of Bladder Cancer.

The objective of this research was to investigate the multidirectional synchronous calculation of the back-projection computed tomography (CT) image reconstruction algorithm (MSBP) in the staging diagnosis of bladder cancer. Sixty patients with bladder cancer admitted to the hospital were selected for enhanced CT scanning, all of which were randomly divided into control group (n = 30) and study group (n = 30). The filtered back-projection (FBP) algorithm was employed to reconstruct the scanned image, and the MSBP was additionally applied to the images of the study group. Fringe artifact (SA), overall mass (OQ), effective radiation dose (ED), CT dose-exponential volume (CTDI), and dose-length product (DLP) of the two groups of images were compared and analyzed. The results showed that the total time of the traditional algorithm was 5.473 s, and the total time of MSBP combined with FBP algorithm was 2.832 s, which was significantly higher than that of the traditional algorithm (P < 0.05). CT scan bladder cancer staging results of all patients were compared with surgical pathological staging results, and the results were evaluated according to the coincidence rate. SA in the study group was lower than that in the control group (P < 0.05), and OQ was not statistically significant. The ED of the study group was significantly lower than that of the control group by 33%. The coincidence rate of postoperative pathological staging results and CT staging results was 96%, and T1, T2a, and T4 coincidence rate was 100%, The coincidence rates of T2b, T3a, and T3b were 90%, 83.3%, and 66.67%, respectively. In summary, using MSBP method combined with FBP algorithm can improve OQ while reducing ED of patients. The introduction of MSBP into CT reconstruction image simplified the pixel location operation of projection calculation, showing an important application value in preoperative staging diagnosis of bladder cancer.

Development and Biomechanical Evaluation of an Anatomical 3D Printing Modularized Proximal Inter-Phalangeal Joint Implant Based on the Computed Tomography Image Reconstructions.

In this study, we developed a modularized proximal interphalangeal (PIP) joint implant that closely resembles the anatomical bone articular surface and cavity contour based on computed tomography (CT) image reconstruction. Clouds of points of 48 groups reconstructed phalanx articular surfaces of CT images, including the index, middle, ring, and little fingers, were obtained and fitted to obtain the articular surface using iterative closest points algorithm. Elliptical-cone stems, including the length, the major and minor axis at the stem metaphyseal/diaphyseal side for the proximal and middle phalanxes, were designed. The resurfacing PIP joint implant components included the bi-condylar surface for the proximal phalanx with elliptical-cone stem, ultra-high molecular weight polyethylene bi-concave articular surface for middle phalanx with hook mechanism, and the middle phalanx with elliptical-cone stem. Nine sets of modularized designs were made to meet the needs of clinical requirements and the weakness structure from the nine sets, that is, the worst structure case combination was defined and manufactured using titanium alloy three-dimensional (3D) printing. Biomechanical tests including anti-loosening pull-out strength for the proximal phalanx, elliptical-cone stem, and articular surface connection strength for the middle phalanx, and static/dynamic (25000 cycles) dislocation tests under three daily activity loads for the PIP joint implant were performed to evaluate the stability and anti-dislocation capability. Our experimental results showed that the pull-out force for the proximal phalanx implant was 727.8N. The connection force for the hook mechanism to cone stem of the middle phalanx was 49.9N and the hook mechanism was broken instead of stem pull out from the middle phalanx. The static dislocation forces/dynamic fatigue limits (pass 25000 cyclic load) of daily activities for piano-playing, pen-writing, and can-opening were 525.3N/262.5N, 316.0N/158N, and 115.0N/92N, respectively, and were higher than general corresponding acceptable forces of 19N, 17N, and 45N from the literatures. In conclusion, our developed modularized PIP joint implant with anatomical articular surface and elliptical-cone stem manufactured by titanium alloy 3D printing could provide enough joint stability and the ability to prevent dislocation.

Nondestructive cellular-level 3D observation of mouse kidney using laboratory-based X-ray microscopy with paraffin-mediated contrast enhancement.

For three-dimensional observation of unstained bio-specimens using X-ray microscopy with computed tomography (CT), one main problem has been low contrast in X-ray absorption. Here we introduce paraffin-mediated contrast enhancement to visualize biopsy samples of mouse kidney using a laboratory-based X-tray microscope. Unlike conventional heavy-atom staining, paraffin-mediated contrast enhancement uses solid paraffin as a negative contrast medium to replace water in the sample. The medium replacement from water to paraffin effectively lowers the absorption of low-energy X-rays by the medium, which eventually enhances the absorption contrast between the medium and tissue. In this work, paraffin-mediated contrast enhancement with 8 keV laboratory X-rays was used to visualize cylindrical renal biopsies with diameters of about 0.5 mm. As a result, reconstructed CT images from 19.4 h of data collection achieved cellular-level resolutions in all directions, which provided 3D structures of renal corpuscles from a normal mouse and from a disease model mouse. These two structures with and without disease allowed a volumetric analysis showing substantial volume differences in glomerular subregions. Notably, this nondestructive method presents CT opacities reflecting elemental composition and density of unstained tissues, thereby allowing more unbiased interpretation on their biological structures.

Deep learning image reconstruction in pediatric abdominal and chest computed tomography: a comparison of image quality and radiation dose.

Studies on the application of deep learning image reconstruction (DLIR) in pediatric computed tomography (CT) are limited and have so far been mostly based on phantom. The purpose of this study was to compare the image quality and radiation dose of DLIR with that of adaptive statistical iterative reconstruction-Veo (ASiR-V) during abdominal and chest CT for the pediatric population. A pediatric phantom was used for the pilot study, and 20 children were recruited for clinical verification. The preset scan parameter noise index (NI) was 5, 8, 11, 13, 15, and 18 for the phantom study, and 8 and 13 for the clinical pediatric study. We reconstructed CT images with ASiR-V 30%, ASiR-V 70%, DLIR-M (medium) and DLIR-H (high). The regions of interest (ROI) were marked on the organs of the abdomen (liver, kidney, and subcutaneous fat) and the chest (lung, mediastinum, and spine). The CT dose index volume (CTDIvol), CT value, image noise (N), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured and calculated. The subjective image quality was assessed by 3 radiologists blindly using a 5-point scale. The dose reduction efficiency of DLIR was estimated. In the phantom study, the interobserver assessment of the data measurement demonstrated good agreement [intraclass correlation coefficient (ICC) =0.814 for abdomen, 0.801 for chest]. Within the same dose level, the N, SNR, and CNR were statistically different among reconstructions, while the CT value remained the same. The N increased and SNR decreased as the radiation dose decreased. The DLIR-H performed better than ASiR-V when the radiation dose was reduced, without sacrificing image quality. In the patient study, the interobserver assessment of the data measurement demonstrated good agreement (ICC =0.774 for abdomen, 0.751 for chest). DLIR-H had the highest subjective and objective scores in the abdomen. Application of DLIR could help to reduce radiation dose without sacrificing the image quality of pediatric CT scans. Further clinical validation is required.

Deep learning-based X-ray computed tomography image reconstruction and prediction of compression behavior of 3D printed lattice structures

Complex thermal processing during laser powder bed fusion ( L -PBF) inevitably induces heterogenous and anisotropic defects, which can affect the macromechanical response of components. The initial morphological features of L -PBF printed samples can be reconstructed with X-ray micro-computed tomography (μCT) scans and the effects of defects on the mechanical properties of the sample can be predicted using image-based finite element modeling. However, due to balling phenomena, several powder particles adhere to the surface of the lattice structure and the mechanical properties of these particles differ from that of the parent material. The predicted modulus and strength of the direct reconstructed models are much higher than the experimental values because of overestimated particle effect. Therefore, these particles should be removed from the CT images before high-fidelity numerical simulation. This paper proposes a new image segmentation method based on a U-Net convolutional neural network to remove adhered particles on the L -PBF manufactured lattice structure from μCT slices. High-fidelity image-based finite element models were constructed with and without the deep learning-based image preprocessing. The results were compared with those obtained from simulations using an ideal CAD model and experimental results. The proposed deep learning-based preprocessing method enables high-precision reconstruction and efficient finite element simulation prediction of additive manufactured lattice structures.

The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review

Conventional reconstruction techniques, such as filtered back projection (FBP) and iterative reconstruction (IR), which have been utilised widely in the image reconstruction process of computed tomography (CT) are not suitable in the case of low-dose CT applications, because of the unsatisfying quality of the reconstructed image and inefficient reconstruction time. Therefore, as the demand for CT radiation dose reduction continues to increase, the use of artificial intelligence (AI) in image reconstruction has become a trend that attracts more and more attention. This systematic review examined various deep learning methods to determine their characteristics, availability, intended use and expected outputs concerning low-dose CT image reconstruction. Utilising the methodology of Kitchenham and Charter, we performed a systematic search of the literature from 2016 to 2021 in Springer, Science Direct, arXiv, PubMed, ACM, IEEE, and Scopus. This review showed that algorithms using deep learning technology are superior to traditional IR methods in noise suppression, artifact reduction and structure preservation, in terms of improving the image quality of low-dose reconstructed images. In conclusion, we provided an overview of the use of deep learning approaches in low-dose CT image reconstruction together with their benefits, limitations, and opportunities for improvement.

Total variation combining nonlocal means filtration for image reconstruction in X-ray computed tomography.

Image reconstruction for realistic medical images under incomplete observation is still one of the core tasks for computed tomography (CT). However, the stair-case artifacts of Total variation (TV) based ones have restricted the usage of the reconstructed images. This work aims to propose and test an accurate and efficient algorithm to improve reconstruction quality under the idea of synergy between local and nonlocal regularizations. The total variation combining the nonlocal means filtration is proposed and the alternating direction method of multipliers is utilized to develop an efficient algorithm. The first order approximation of linear expansion at intermediate point is applied to overcome the computation of the huge CT system matrix. The proposed method improves root mean squared error by 25.6% compared to the recent block-matching sparsity regularization (BMSR) on simulation dataset of 19 views. The structure similarities of image of the new method is higher than 0.95, while that of BMSR is about 0.92. Moreover, on real rabbit dataset of 20 views, the peak signal-to-noise ratio (PSNR) of the new method is 36.84, while using other methods PSNR are lower than 35.81. The proposed method shows advantages on noise suppression and detail preservations over the competing algorithms used in CT image reconstruction.

A Critical Survey on Developed Reconstruction Algorithms for Computed Tomography Imaging from a Limited Number of Projections

Rapid system and hardware development of X-ray computed tomography (CT) technologies has been accompanied by equally exciting advances in image reconstruction algorithms. Of the two reconstruction algorithms, analytical and iterative, iterative reconstruction (IR) algorithms have become a clinically viable option in CT imaging. The first CT scanners in the early 1970s used IR algorithms, but lack of computation power prevented their clinical use. In 2009, the first IR algorithms became commercially available and replaced conventionally established analytical algorithms as filtered back projection. Since then, IR has played a vital role in the field of radiology. Although all available IR algorithms share the common mechanism of artifact reduction and/or potential for radiation dose reduction, the magnitude of these effects depends upon specific IR algorithms. IR reconstructs images by iteratively optimizing an objective function. The objective function typically consists of a data integrity term and a regularization term. Therefore, different regularization priors are used in IR algorithms. This paper will briefly look at the overall evolution of CT image reconstruction and the regularization priors used in IR algorithms. Finally, a discussion is presented based on the reality of various reconstruction methodologies at a glance to find the preferred one. Consequently, we will present anticipation towards future advancements in this domain.