Computed Tomography Projection Research Articles

Pixel-by-pixel correction of beam hardening artifacts by bowtie filter in fan-beam CT

Objective. Beam hardening (BH) artifacts in computed tomography (CT) images originate from the polychromatic nature of x-ray photons. In a CT system with a bowtie filter, residual BH artifacts remain when polynomial fits are used. These artifacts lead to worse visuals, reduced contrast, and inaccurate CT numbers. This work proposes a pixel-by-pixel correction (PPC) method to reduce the residual BH artifacts caused by a bowtie filter. Approach. The energy spectrum for each pixel at the detector after the photons pass through the bowtie filter was calculated. Then, the spectrum was filtered through a series of water slabs with different thicknesses. The polychromatic projection corresponding to the thickness of the water slab for each detector pixel could be obtained. Next, we carried out a water slab experiment with a mono energy E = 69 keV to get the monochromatic projection. The polychromatic and monochromatic projections were then fitted with a 2nd-order polynomial. The proposed method was evaluated on digital phantoms in a virtual CT system and phantoms in a real CT machine. Main results. In the case of a virtual CT system, the standard deviation of the line profile was reduced by 23.8%, 37.3%, and 14.3%, respectively, in the water phantom with different shapes. The difference of the linear attenuation coefficients (LAC) in the central and peripheral areas of an image was reduced from 0.010 to 0.003 cm−1 and 0.007 cm−1 to 0 in the biological tissue phantom and human phantom, respectively. The method was also validated using CT projection data obtained from Activion16 (Canon Medical Systems, Japan). The difference in the LAC in the central and peripheral areas can be reduced by a factor of two. Significance. The proposed PPC method can successfully remove the cupping artifacts in both virtual and authentic CT images. The scanned object’s shapes and materials do not affect the technique.

Estimate and compensate head motion in non-contrast head CT scans using partial angle reconstruction and deep learning.

Patient head motion is a common source of image artifacts in computed tomography (CT) of the head, leading to degraded image quality and potentially incorrect diagnoses. The partial angle reconstruction (PAR) means dividing the CT projection into several consecutive angular segments and reconstructing each segment individually. Although motion estimation and compensation using PAR has been developed and investigated in cardiac CT scans, its potential for reducing motion artifacts in head CT scans remains unexplored. To develop a deep learning (DL) model capable of directly estimating head motion from PAR images of head CT scans and to integrate the estimated motion into an iterative reconstruction process to compensate for the motion. Head motion is considered as a rigid transformation described by six time-variant variables, including the three variables for translation and three variables for rotation. Each motion variable is modeled using a B-spline defined by five control points (CP) along time. We split the full projections from 360° into 25 consecutive PARs and subsequently input them into a convolutional neural network (CNN) that outputs the estimated CPs for each motion variable. The estimated CPs are used to calculate the object motion in each projection, which are incorporated into the forward and backprojection of an iterative reconstruction algorithm to reconstruct the motion-compensated image. The performance of our DL model is evaluated through both simulation and phantom studies. The DL model achieved high accuracy in estimating head motion, as demonstrated in both the simulation study (mean absolute error (MAE) ranging from 0.28 to 0.45mm or degree across different motion variables) and the phantom study (MAE ranging from 0.40 to 0.48mm or degree). The resulting motion-corrected image, , exhibited a significant reduction in motion artifacts when compared to the traditional filtered back-projection reconstructions, which is evidenced both in the simulation study (image MAE drops from 178 33HU to 37 9HU, structural similarity index (SSIM) increases from 0.60 0.06 to 0.98 0.01) and the phantom study (image MAE drops from 117 17HU to 42 19HU, SSIM increases from 0.83 0.04 to 0.98 0.02). We demonstrate that using PAR and our proposed deep learning model enables accurate estimation of patient head motion and effectively reduces motion artifacts in the resulting head CT images.

ACnerf: enhancement of neural radiance field by alignment and correction of pose to reconstruct new views from a single x-ray* *This work was supported in part by the Science and Technology Commission of Shanghai Municipality under Grant 20DZ2254400 and 20DZ2261200; in part by Shanghai Municipal Science and Technology Major Project under Grant ZD2021CY001.

Objective. Computed tomography (CT) is widely used in medical research and clinical diagnosis. However, acquiring CT data requires patients to be exposed to considerable ionizing radiance, leading to physical harm. Recent studies have considered using neural radiance field (NERF) techniques to infer the full-view CT projections from single-view x-ray projection, thus aiding physician judgment and reducing Radiance hazards. This paper enhances this technique in two directions: (1) accurate generalization capabilities for control models. (2) Consider different ranges of viewpoints. Approach. Building upon generative radiance fields (GRAF), we propose a method called ACnerf to enhance the generalization of the NERF through alignment and pose correction. ACnerf aligns with a reference single x-ray by utilizing a combination of positional encoding with Gaussian random noise (latent code) obtained from GRAF training. This approach avoids compromising the 3D structure caused by altering the generator. During inference, a pose judgment network is employed to correct the pose and optimize the rendered viewpoint. Additionally, when generating a narrow range of views, ACnerf employs frequency-domain regularization to fine-tune the generator and achieve precise projections. Main results. The proposed ACnerf method surpasses the state-of-the-art NERF technique in terms of rendering quality for knee and chest data with varying contrasts. It achieved an average improvement of 2.496 dB in PSNR and 41% in LPIPS for 0°–360° projections. Additionally, for −15° to 15° projections, ACnerf achieved an average improvement of 0.691 dB in PSNR and 25.8% in LPIPS. Significance. With adjustments in alignment, inference, and rendering range, our experiments and evaluations on knee and chest data of different contrasts show that ACnerf effectively reduces artifacts and aberrations in the new view. ACnerf’s ability to recover more accurate 3D structures from single x-rays has excellent potential for reducing damage from ionising radiation in clinical diagnostics.

In-situ NDT CT image restoration method for concrete based on deep learning by modeling non-ideal focal spot

This paper presents a novel neural network-based framework designed to enhance the imaging quality of in-situ computed tomography (CT) for testing large and intricate specimens, especially concrete structures. The proposed framework is specifically engineered to restore degraded CT projection data that arises from non-ideal ray sources. It accomplishes this by employing a multi-Gaussian convolutional kernel point spread function (PSF), tailored to the degradation characteristics of the ray source and the frequency domain data characteristics of the projection image. The experimental results demonstrate remarkable advances in quantitative metrics, including structural similarity index measure (SSIM) scores reaching up to 0.9768, as well as peak signal-to-noise ratios (PSNR) climbing to as high as 38.8592 over the corrupted images. This evidence underscores the strong generalization capacity and noise suppression capabilities achieved by the proposed framework.

A total variation prior unrolling approach for computed tomography reconstruction.

With the rapid development of deep learning technology, deep neural networks can effectively enhance the performance of computed tomography (CT) reconstructions. One kind of commonly used method to construct CT reconstruction networks is to unroll the conventional iterative reconstruction (IR) methods to convolutional neural networks (CNNs). However, most unrolling methods primarily unroll the fidelity term of IR methods to CNNs, without unrolling the prior terms. The prior terms are always directly replaced by neural networks. In conventional IR methods, the prior terms play a vital role in improving the visual quality of reconstructed images. Unrolling the hand-crafted prior terms to CNNs may provide a more specialized unrolling approach to further improve the performance of CT reconstruction. In this work, a primal-dual network (PD-Net) was proposed by unrolling both the data fidelity term and the total variation (TV) prior term, which effectively preserves the image edges and textures in the reconstructed images. By further deriving the Chambolle-Pock (CP) algorithm instance for CT reconstruction, we discovered that the TV prior updates the reconstructed images with its divergences in each iteration of the solution process. Based on this discovery, CNNs were applied to yield the divergences of the feature maps for the reconstructed image generated in each iteration. Additionally, a loss function was applied to the predicted divergences of the reconstructed image to guarantee that the CNNs' results were the divergences of the corresponding feature maps in the iteration. In this manner, the proposed CNNs seem to play the same roles in the PD-Net as the TV prior in the IR methods. Thus, the TV prior in the CP algorithm instance can be directly unrolled to CNNs. The datasets from the Low-Dose CT Image and Projection Data and the Piglet dataset were employed to assess the effectiveness of our proposed PD-Net. Compared with conventional CT reconstruction methods, our proposed method effectively preserves the structural and textural information in reference to ground truth. The experimental results show that our proposed PD-Net framework is feasible for the implementation of CT reconstruction tasks. Owing to the promising results yielded by our proposed neural network, this study is intended to inspire further development of unrolling approaches by enabling the direct unrolling of hand-crafted prior terms to CNNs.

CG-DRR：digital reconstructed radiograph generation algorithm based on Cycle-GAN

CG-DRR：digital reconstructed radiograph generation algorithm based on Cycle-GAN

Learning CT projection denoising from adjacent views.

Many learning-based low-dose (LD) computed tomography (CT) imaging methods require large paired full- and low-dose datasets for training, which are usually unavailable in clinic. Whereas models trained on simulated data often face the generalization problem on real clinical data. To develop an unsupervised learning technique to acquire clean CT projection from its adjacent LD projections. Given a sequential LD projection set, the method extracts out the middle projection as the target and treats the rest ones as the input. The model is trained with the mean absolute error with proposed inter-view gradient constraint term, which helps to suppress outliers and preserve edges in the denoised projection. The simulated low-dose CT grand challenge dataset and a real physical torso phantom dataset were employed for experiment. The peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) were calculated for quantitative evaluation. In experiments with both the simulated and real datasets, visual comparisons reveal that the proposed method obtained images superior to unsupervised and supervised methods working in both image and projection domain. For numerical comparison, our method obtains larger SSIMs than other unsupervised methods at quarter and eighth dose levels. As for PSNR, our method obtains larger value at eighth dose whereas smaller value at quarter dose. The supervised models obtain better numerical results than all unsupervised models on simulated datasets. The proposed method can reduce the noise in CT projections effectively, making it an attractive tool for practical LDCT pre-processing.

Deterministic small-scale undulations of image-based risk predictions from the deep learning of lung tumors in motion.

Deep learning (DL) models that use medical images to predict clinical outcomes are poised for clinical translation. For tumors that reside in organs that move, however, the impact of motion (i.e., degenerated object appearance or blur) on DL model accuracy remains unclear. We examine the impact of tumor motion on an image-based DL framework that predicts local failure risk after lung stereotactic body radiotherapy (SBRT). We input pre-therapy free breathing (FB) computed tomography (CT) images from 849 patients treated with lung SBRT into a multitask deep neural network to generate an image fingerprint signature (or DL score) that predicts time-to-event local failure outcomes. The network includes a convolutional neural network encoder for extracting imaging features and building a task-specific fingerprint, a decoder for estimating handcrafted radiomic features, and a task-specific network for generating image signature for radiotherapy outcome prediction. The impact of tumor motion on the DL scores was then examined for a holdout set of 468 images from 39 patients comprising: (1) FB CT, (2) four-dimensional (4D) CT, and (3) maximum-intensity projection (MIP) images. Tumor motion was estimated using a 3D vector of the maximum distance traveled, and its association with DL score variance was assessed by linear regression. The variance and amplitude in 4D CT image-derived DL scores were associated with tumor motion (R2 =0.48 and 0.46, respectively). Specifically, DL score variance was deterministic and represented by sinusoidal undulations in phase with the respiratory cycle. DL scores, but not tumor volumes, peaked near end-exhalation. The mean of the scores derived from 4D CT images and the score obtained from FB CT images were highly associated (Pearson r=0.99). MIP-derived DL scores were significantly higher than 4D- or FB-derived risk scores (p<0.0001). An image-based DL risk score derived from a series of 4D CT images varies in a deterministic, sinusoidal trajectory in a phase with the respiratory cycle. These results indicate that DL models of tumors in motion can be robust to fluctuations in object appearance due to movement and can guide standardization processes in the clinical translation of DL models for patients with lung cancer.

MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray.

Computed tomography (CT) is an effective med-ical imaging modality, widely used in the field of clinical medicine for the diagnosis of various pathologies. Advances in Multidetector CT imaging technology have enabled additional functionalities, including generation of thin slice multi planar cross-sectional body imaging and 3D reconstructions. However, this involves patients being exposed to a considerable dose of ionising radiation. Excessive ionising radiation can lead to deterministic and harmful effects on the body. This paper proposes a Deep Learning model that learns to reconstruct CT projections from a few or even a single-view X-ray. This is based on a novel architecture that builds from neural radiance fields, which learns a continuous representation of CT scans by disentangling the shape and volumetric depth of surface and internal anatomical structures from 2D images. Our model is trained on chest and knee datasets, and we demonstrate qual-itative and quantitative high-fidelity renderings and compare our approach to other recent radiance field-based methods. Our code and link to our datasets are available at https://qithub.com/abrilcf/mednerf Clinical relevance- Our model is able to infer the anatomical 3D structure from a few or a single-view X-ray showing future potential for reduced ionising radiation exposure during the imaging process.

Iterative excitation with noise rejection techniques for X-ray computed tomography of hollow turbine blades

ABSTRACT The features of aero-engine hollow turbine blades show a complicated inner air tract, which have a significant influence on the engine’s performance. Inspection of the internal structure and flaws of the blades become indispensable. Non-destructive testing, such as computed tomography (CT), is an effective method for detecting internal problems. The purpose of this study is to demonstrate how an iterative excitation can be used to recover an incomplete projection in CT for a turbine blade. Firstly, the variance of the background was gathered as previous information. Then, to make up for the missed sample at the ill-angle position, a noise map was added and filtered as prep work for the forward projection. The original projections were retained, and the revised projections were used to extract additional characteristics from the damaged data. Finally, both simulation and actual tests were studied. The Normalised Mean Square Distance (NMSD) of the reconstructed image was reduced by over 20%. The Structural Similarity Index Measure (SSIM) and the Universal Quality Index (UQI) were both enhanced by at least 60% and 41%, respectively. It was demonstrated that the technique can increase the accuracy of reconstruction for a hollow turbine blade.

A truth-based primal-dual learning approach to reconstruct CT images utilizing the virtual imaging trial platform.

Inherent to Computed tomography (CT) is image reconstruction, constructing 3D voxel values from noisy projection data. Modeling this inverse operation is not straightforward. Given the ill-posed nature of inverse problem in CT reconstruction, data-driven methods need regularization to enhance the accuracy of the reconstructed images. Besides, generalization of the results hinges upon the availability of large training datasets with access to ground truth. This paper offers a new strategy to reconstruct CT images with the advantage of ground truth accessible through a virtual imaging trial (VIT) platform. A learned primal-dual deep neural network (LPD-DNN) employed the forward model and its adjoint as a surrogate of the imaging's geometry and physics. VIT offered simulated CT projections paired with ground truth labels from anthropomorphic human models without image noise and resolution degradation. The models included a library of anthropomorphic, computational patient models (XCAT). The DukeSim simulator was utilized to form realistic projection data emulating the impact of the physics and geometry of a commercial-equivalent CT scanner. The resultant noisy sinogram data associated with each slice was thus generated for training. Corresponding linear attenuation coefficients of phantoms' materials at the effective energy of the x-ray spectrum were used as the ground truth labels. The LPD-DNN was deployed to learn the complex operators and hyper-parameters in the proximal primal-dual optimization. The obtained validation results showed a 12% normalized root mean square error with respect to the ground truth labels, a peak signal-to-noise ratio of 32 dB, a signal-to-noise ratio of 1.5, and a structural similarity index of 96%. These results were highly favorable compared to standard filtered-back projection reconstruction (65%, 17 dB, 1.0, 26%).

A content-adaptive unstructured grid based regularized CT reconstruction method with a SART-type preconditioned fixed-point proximity algorithm

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.

DDPTransformer: Dual-Domain With Parallel Transformer Network for Sparse View CT Image Reconstruction

Computed tomography (CT) is increasingly essential for clinical diagnosis nowadays while X-ray ionizing radiation is harmful and may increase the risk of cancers. Researchers have thus proposed to obtain the sparse sinograms by reducing the number of detectors to reduce the radioactive impact on human body. However, the sparse-view sinograms have some problems including blurring and streak artifacts, which may adversely confuse the diagnostic assessment to some extent. In this paper, we propose a Transformer-based module named DDPTransformer Block to capture the long-term dependencies within the images and reconstruct high-quality CT images from sparsely sampled sinograms. More specifically, we replace the traditional convolution operation with the Parallel Transformers, and augment the edge information of patch blocks by different splits. In addition, considering that the images to be processed are two-dimensioned, we propose the Layer-Conv-Layer (LCL) module to replace the multilayer perceptron (MLP) in Transformer for feature extraction. Finally, with DDPTransformer Block as the backbone, we propose a deep learning model with four stages including Interpolation, Sinogram Domain Subnet, Filtered BackProjection (FBP) and Image Domain SubNet. For training the model, we use the dataset from the “ Low Dose CT Image and Projection Data (LDCT-and-Projection data)”. Experimental results show that the proposed model outperforms the state-of-the-art algorithms for various sparsely sampled sinograms and its robustness has been further verified through other datasets. The code and model are publicly available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/MikasaLee/DDPTransformer</uri> .

Photon Starvation Artifact Reduction by Shift-Variant Processing

The x-ray computed tomography (CT) images with low dose are noisy and may contain photon starvation artifacts. The artifacts are location and direction dependent. Therefore, the common shift-invariant denoising filters do not work well. The state-of-the-art methods to process the low-dose CT images are image reconstruction based; they require the raw projection data. In many situations, the raw CT projections are not accessible. This paper suggests a method to denoise the low-dose CT image using the pseudo projections generated by the application of a forward projector on the low-dose CT image. The feasibility of the proposed method is demonstrated by real clinical data.

Simulation on system configuration for stationary head CT using linear carbon nanotube x-ray source arrays.

Purpose: The invention of carbon nanotube (CNT) x-ray source arrays has enabled the development of novel imaging systems, including stationary tomosynthesis and stationary computed tomography (CT) with fast data acquisition, mechanically robust structures, and reduced image blur from source-detector motion. In this work, we report the results of simulation studies of potential system configurations for a stationary head CT (s-HCT) using linear CNT x-ray sources and detector arrays. Approach: We explored s-HCT configurations that utilize one, two, and three linear CNT source arrays. Simulations were implemented using three digital phantoms with both CPU and GPU computing. Sinogram coverage was used for qualitative evaluation of the CT projection collection efficiency for each configuration. A modified low-contrast Shepp-Logan (SL) phantom was implemented for image quality assessment using quantitative metrics. Different iterative reconstruction (IR) methods were compared with both qualitative and quantitative assessments. Results: Sinogram coverage of s-HCT configurations was sensitive to the number of CNT source arrays and geometry. The simulations suggest that a s-HCT configuration with three planes gives near complete sinogram coverage. Such a configuration enables accurate reconstruction of the low-contrast SL phantom and considerably diminished artifacts caused by the system geometry. Conclusions: An optimized s-HCT system configuration with three linear CNT x-ray source arrays is feasible. IR algorithms can diminish artifacts caused by sparse and asymmetrical scans. The proposed s-HCT system configuration is currently under construction.

A scanner-specific framework for simulating CT images with tube current modulation

Although tube current modulation (TCM) is routinely implemented in modern computed tomography (CT) scans, no existing CT simulator is capable of generating realistic images with TCM. The goal of this study was to develop such a framework to (1) facilitate patient-specific optimization of TCM parameters and (2) enable future virtual imaging trials (VITs) with more clinically realistic image quality and x-ray flux distributions. The framework was created by developing a TCM module and integrating it with an existing CT simulator (DukeSim). The developed module utilizes scanner-calibrated TCM parameters and two localizer radiographs to compute the mAs for each simulated CT projection. This simulation pipeline was validated in two parts. First, DukeSim was validated in the context of a commercial scanner with TCM (SOMATOM Force, Siemens Healthineers) by imaging a physical CT phantom (Mercury, Sun Nuclear) and its computational analogue. Second, the TCM module was validated by imaging a computational anthropomorphic phantom (ATOM, CIRS) using DukeSim with real and module-generated TCM profiles. The validation demonstrated DukeSim’s realism in terms of noise magnitude, noise texture, spatial resolution, and image contrast (with average differences of 0.38%, 6.31%, 0.43%, and −9 HU, respectively). It also demonstrated the TCM module’s realism in terms of projection-level mAs and resulting noise magnitude (2.86% and −2.60%, respectively). Finally, the framework was applied to a pilot VIT simulating images of three computational anthropomorphic phantoms (XCAT, with body mass indices (BMIs) of 24.3, 28.2, and 33.0) under five different TCM settings. The optimal TCM for each phantom was characterized based on various criteria, such as minimizing mAs or maximizing image quality. ‘Very Weak’ TCM minimized noise for the 24.3 BMI phantom, while ‘Very Strong’ TCM minimized noise for the 33.0 BMI phantom. This illustrates the utility of the developed framework for future optimization studies of TCM parameters and, more broadly, large-scale VITs with scanner-specific TCM.

Fast system matrix calculation based on voxel projection in cone-beam CT

In computed tomography (CT) image reconstruction, the iterative algorithms have many advantages that the analytic algorithms don’t have. However, the calculation of system matrix is time-consuming, which affects their application in real-time CT image reconstruction. In this paper, a fast system matrix calculation method is proposed on the basis of that the distance between the voxel and the adjacent rays reflects the contribution of the voxel to the ray projections. Firstly, every voxel is projected at a projection angle, and then the weighting factor is determined according to the distance between the voxel’s projection and the adjacent ray projection on the detector. Finally, the ray equations and weighting factors corresponding to every voxel at all projection angles are obtained. The calculation amount of the proposed method are greatly improved compared with the current grid traversal (GT) method, and it has good parallel computing performance. In the simulation experiment, the Shepp-Logan head phantom is reconstructed by using the proposed system matrix calculation method and algebraic reconstruction technique (ART). Compared with GT method, its cost is reduced by nearly 30 %, and its acceleration ratio is four times that of the GT method. The experimental results show that it is a fast and effective method to calculate the system matrix.

Artifacts and quantitative biases in neutron tomography introduced by systematic and random errors

Systematic and random errors are often introduced in white-beam neutron Computed Tomography (CT) due to the nature of the neutron source, neutron-matter interactions or limitations in hardware. These errors can cause bias in measured linear attenuation coefficients (LACs) and artifacts in the reconstructed images, reducing image quality and impeding quantitative analysis. Three sources of error in neutron tomography were investigated and quantified that are particularly pertinent to spallation source facilities. These include detector to sample stage misalignment, beam fluctuations and undersampling due to missing CT projections. Quantitative analyses of the grayscale biases and misalignment artifacts were performed on the reconstructed data using ImageJ. Furthermore, the efficacy of classical iterative reconstruction algorithms for the suppression of missing projection artifacts was explored. Several image qualifying metrics were used to compare Filtered Back Projection (FBP) and iterative algorithms quantitatively. We show that beam intensity fluctuations if left uncorrected provide a minimal bias even when exaggerated across a stack of projections. In addition, detector-sample stage misalignment causes a geometrical misalignment blur when not corrected for and severe ring artifacts despite image rotation correction. We suggest that iterative algorithms are more favourable in suppressing missing projection neutron artifacts, however they inaccurately reproduce the LACs for missing-wedge reconstructions compared to FBP.

Novel FBP based sparse-view CT reconstruction scheme using self-shaping spatial filter based morphological operations and scaled reprojections

Computed tomography (CT) has been a major contributor in revolutionizing and commercializing the medical imaging industry. However, most of the commonly used CT reconstruction algorithms need sufficiently dense angular sampling views or a substantial dose of hazardous X-ray radiations. Reducing the radiation dose causes degradation in the quality of the reconstructed image, due to additional artifacts. The paper presents a novel algorithm for efficient CT reconstruction from under-sampled projections; which leads to radiation dose reduction with quality image reconstruction. The Sparse-View projection data is enhanced using a series of post-processing algorithms and computer based reprojection. The process involves enhancement through self-shaping/amoeba based morphological spatial filtering. The use of self-shaping spatial filter kernel in the area of under-sampled CT reconstruction is a novel contribution. The scheme is supported by computer simulations using fan-beam projections of clinically reconstructed and simulated head CT phantoms. The proposed scheme is compared with classical reconstruction techniques for reconstruction image quality, accuracy, speed, and robustness in the presence of noise. Promising results indicate the efficacy of proposed scheme. An efficient scheme for image enhancement of Sparse-View CT is presented. The results demonstrate that the proposed scheme is visually and statistically better than classical CT reconstruction techniques, as evaluated using various image quality matrices. The presented scheme is more robust to noise in CT projections and effective for enhancing few-views reconstruction.

Influence of Data Acquisition Algorithms on X-Ray Phase Contrast Imaging Computed Tomography

Abstract X-ray phase contrast imaging (XPCI) is a nondestructive evaluation technique that enables high-contrast detection of low-attenuation materials that are largely transparent in traditional radiography. Extending a grating-based Talbot-Lau XPCI system to three-dimensional imaging with computed tomography (CT) imposes two motion requirements: the analyzer grating must translate transverse to the optical axis to capture image sets for XPCI reconstruction, and the sample must rotate to capture angular data for CT reconstruction. The acquisition algorithm choice determines the order of movement and positioning of the two stages. The choice of the image acquisition algorithm for XPCI CT is instrumental to collecting high fidelity data for reconstruction. We investigate how data acquisition influences XPCI CT by comparing two simple data acquisition algorithms and determine that capturing a full phase-stepping image set for a CT projection before rotating the sample results in higher quality data.