Limited Angle Projection Data Research Articles

A dual-energy CT reconstruction method based on anchor network from dual quarter scans.

Compared with conventional single-energy computed tomography (CT), dual-energy CT (DECT) provides better material differentiation but most DECT imaging systems require dual full-angle projection data at different X-ray spectra. Relaxing the requirement of data acquisition is an attractive research to promote the applications of DECT in wide range areas and reduce the radiation dose as low as reasonably achievable. In this work, we design a novel DECT imaging scheme with dual quarter scans and propose an efficient method to reconstruct the desired DECT images from the dual limited-angle projection data. We first study the characteristics of limited-angle artifacts under dual quarter scans scheme, and find that the negative and positive artifacts of DECT images are complementarily distributed in image domain because the corresponding X-rays of high- and low-energy scans are symmetric. Inspired by this finding, a fusion CT image is generated by integrating the limited-angle DECT images of dual quarter scans. This strategy enhances the true image information and suppresses the limited-angle artifacts, thereby restoring the image edges and inner structures. Utilizing the capability of neural network in the modeling of nonlinear problem, a novel Anchor network with single-entry double-out architecture is designed in this work to yield the desired DECT images from the generated fusion CT image. Experimental results on the simulated and real data verify the effectiveness of the proposed method. This work enables DECT on imaging configurations with half-scan and largely reduces scanning angles and radiation doses.

PDS-MAR: a fine-grained projection-domain segmentation-based metal artifact reduction method for intraoperative CBCT images with guidewires

Objective. Since the invention of modern Computed Tomography (CT) systems, metal artifacts have been a persistent problem. Due to increased scattering, amplified noise, and limited-angle projection data collection, it is more difficult to suppress metal artifacts in cone-beam CT, limiting its use in human- and robot-assisted spine surgeries where metallic guidewires and screws are commonly used. Approach. To solve this problem, we present a fine-grained projection-domain segmentation-based metal artifact reduction (MAR) method termed PDS-MAR, in which metal traces are augmented and segmented in the projection domain before being inpainted using triangular interpolation. In addition, a metal reconstruction phase is proposed to restore metal areas in the image domain. Main results. The proposed method is tested on both digital phantom data and real scanned cone-beam computed tomography (CBCT) data. It achieves much-improved quantitative results in both metal segmentation and artifact reduction in our phantom study. The results on real scanned data also show the superiority of this method. Significance. The concept of projection-domain metal segmentation would advance MAR techniques in CBCT and has the potential to push forward the use of intraoperative CBCT in human-handed and robotic-assisted minimal invasive spine surgeries.

Feasibility of internal-source tracking with C-arm CT/SPECT imaging with limited-angle projection data for online in vivo dose verification in brachytherapy: A Monte Carlo simulation study

PURPOSEThe current protocol for use of the image-guided adaptive brachytherapy (IGABT) procedure entails transport of a patient between the treatment room and the 3-D tomographic imaging room after implantation of the applicators in the body, which movement can cause position displacement of the applicator. Moreover, it is not possible to track 3-D radioactive source movement inside the body, even though there can be significant inter- and intra-fractional patient-setup changes. In this paper, therefore, we propose an online single-photon emission computed tomography (SPECT) imaging technique with a combined C-arm fluoroscopy X-ray system and attachable parallel-hole collimator for internal radioactive source tracking of every source position in the applicator. METHODS AND MATERIALSIn the present study, using Geant4 Monte Carlo (MC) simulation, the feasibility of high-energy gamma detection with a flat-panel detector for X-ray imaging was assessed. Further, a parallel-hole collimator geometry was designed based on an evaluation of projection image quality for a 192Ir point source, and 3-D limited-angle SPECT-image-based source-tracking performances were evaluated for various source intensities and positions. RESULTSThe detector module attached to the collimator could discriminate the 192Ir point source with about 3.4% detection efficiency when including the total counts in the entire deposited energy region. As the result of collimator optimization, hole size, thickness, and length were determined to be 0.5, 0.2, and 45 mm, respectively. Accordingly, the source intensities and positions also were successfully tracked with the 3-D SPECT imaging system when the C-arm was rotated within 110° in 2 seconds. CONCLUSIONSWe expect that this system can be effectively implemented for online IGABT and in vivo patient dose verification.

SPEAR-Net: Self-Prior Enhanced Artifact Removal Network for Limited-Angle DECT

Dual-energy computed tomography (DECT) is a fully functional instrument for disease detection in clinical practice because of its ability to identify substances and quantify materials. In some practical applications, due to the limitation of scanning conditions, projection data can only be collected from a limited angle, and the consistency of measurement cannot be guaranteed. Existing dual-energy CT reconstruction methods fail to address well the severe artifacts and noise in dual-energy CT images caused by limited-angle scanning. In this paper, we proposed a Self-Prior Enhanced Artifact Removal Network (SPEAR-Net) for limited-angle DECT, which can effectively combine the complementary information in the high- and low-energy domains and self-prior information to contribute positively to the reconstruction of high-quality dual-energy CT images. The SPEAR-Net consists of an image-domain self-prior network(IP-Net), two dual-energy image-domain networks(DIP-Net), and a dual-energy sinogram-domain self-prior network(DSP-Net). Specifically, the IP-Net and DIP-Net are adopted to extract the features of the dual-energy CT reconstructed images under dual-quarter scanning as prior information. The self-prior projection obtained from the forward projection of the prior CT image is harnessed by DSP-Net to complete the dual-energy limited-angle projection data and to facilitate the performance of SPEAR-Net in removing artifacts in the reconstructed dual-energy images. Qualitative and quantitative analyses demonstrate the superior capability of SPEAR-Net in dual-energy limited-angle projection data complementation, detail preservation, and artifact removal. Two popular DECT applications, virtual non-contrast (VNC) imaging and iodine contrast agent quantification, reveal that images reconstructed by SPEAR-Net have promising clinical significance.

A novel few-views arrangement of the fixed X-ray tubes for tomosynthesis.

Tomosynthesis is a technique that reconstructs a volume image from limited-angle projection data. In conventional tomosynthesis, the examination time is long, so it can be difficult for patients to hold their breath during certain examinations, such as chest imaging. Few-views tomosynthesis, which uses a linear arrangement of fixed X-ray tubes and enables an image to be obtained within 1 s, was found to be useful in the clinical setting in our previous study. In the present study, we attempted to develop a novel few-views tomosynthesis system that can obtain images with an improved image quality. A novel few-views arrangement of X-ray tubes was proposed and the image reconstruction method with regularization term was applied. The linear arrangement was used for the X-ray tube arrangement in our previous few-views tomosynthesis, in contrast, a circular arrangement was proposed in this study. The validation of this system was conducted with a numerical simulation and a real data experiment. The wider the scan angle, the more the object shadow spreads from "in-plane", allowing for artifact suppression. In the circular arrangement, the constant scan angle of θ is used, but in the linear arrangement the scan angle is set from 0 to θ. The artifacts in "out-of-plane" were more strongly suppressed in the circular arrangement than in the linear arrangement. Artifacts spreading in the z-direction were more strongly suppressed using the circular arrangement than the linear arrangement. Therefore, the circular arrangement was deemed appropriate for few-views tomosynthesis.

A preconditioned landweber iteration scheme for the limited-angle image reconstruction.

The limited-angle reconstruction problem is of both theoretical and practical importance. Due to the severe ill-posedness of the problem, it is very challenging to get a valid reconstructed result from the known small limited-angle projection data. The theoretical ill-posedness leads the normal equation AT Ax = AT b of the linear system derived by discretizing the Radon transform to be severely ill-posed, which is quantified as the large condition number of AT A. To develop and test a new valid algorithm for improving the limited-angle image reconstruction with the known appropriately small angle range from [0,π3]∼[0,π2]. We propose a reweighted method of improving the condition number of AT Ax = AT b and the corresponding preconditioned Landweber iteration scheme. The weight means multiplying AT Ax = AT b by a matrix related to AT A, and the weighting process is repeated multiple times. In the experiment, the condition number of the coefficient matrix in the reweighted linear system decreases monotonically to 1 as the weighting times approaches infinity. The numerical experiments showed that the proposed algorithm is significantly superior to other iterative algorithms (Landweber, Cimmino, NWL-a and AEDS) and can reconstruct a valid image from the known appropriately small angle range. The proposed algorithm is effective for the limited-angle reconstruction problem with the known appropriately small angle range.

Nonlocal Low-Rank and Prior Image-Based Reconstruction in a Wavelet Tight Frame Using Limited-Angle Projection Data

Computed tomography (CT) reconstruction for limited-angle projection data is an ill-posed inverse problem, that often produces artifacts near the edges of an image. In this study, a hybrid minimization model based on a nonlocal low-rank approximation and a prior image in a wavelet tight framework is proposed to improve reconstructions from limited-angle projections. Low-frequency wavelet coefficients of the reconstructed image were estimated using a nonlocal low-rank approximation and the $l_{2}$ norm minimization was applied to the difference between the high-frequency components of a prior image and the reconstructed image. In addition, the alternative direction method of multipliers (ADMM) was used for alternately minimization to solve two regularization terms which produce the most parameters. Experimental results demonstrated that the proposed algorithm offers several advantages over conventional iterative reconstruction techniques, including faster convergence, suppression of limited-angle artifacts, noise reduction, and the preservation of edges and other image details. This study represents the first time that nonlocal low-rank prior information has been applied to limited-angle CT.

Sparse and limited-angle CT image reconstruction using dictionary learning constrained by L1 norm

Reconstructed images from computed tomography (CT) using the algebraic reconstruction technique (ART) and simultaneous ART (SART) algorithms often suffer from obvious artefacts when only sparse and limited-angle projection data are available. Using the ability of dictionary learning (DL) in image feature extraction and sparse signal representation, a new iterative reconstruction algorithm, ART-DL-L1, is proposed to overcome the aforementioned limitations. This new algorithm is based on DL and an L1 norm constraint, combined with ART. An alternate iterative solving strategy based on an approach of 'ART first, then adaptive dictionary learning' is suggested and is explicitly described in a flowchart depicting the ART-DL-L1 algorithm. For both a noisy projection of 360° sparse data and limitedangle data of 120°, simulation reconstruction results from the classic Shepp-Logan image obtained using ART-DL-L1 appear to be better than those obtained using SART and total variation (TV) algorithms and also better than the cutting-edge ART-DL-L2 algorithm. Five evaluation metrics corresponding to the root-mean-square error (RMSE), the mean absolute error (MAE), the peak signal-to-noise ratio (PSNR), the residuals and the structural similarity (SSIM) index are adopted to estimate the reconstruction effect. The results suggest that the five metrics obtained using ART-DL-L1 outperform those obtained using the other three algorithms. The impact of using patches of various sizes played by the DL part in ART-DL-L1 is considered in the simulations and the patch size achieving the best reconstructed image quality is identified in this case as 25 (5 × 5). Overall, the proposed ART-DL-L1 algorithm may reduce artefacts and suppress noise from incomplete noisy projection CT imaging to some degree.

Guided Image Filtering Based Limited-Angle CT Reconstruction Algorithm Using Wavelet Frame

Computed tomography (CT) has its irreplaceable function in nondestructive testing and medical diagnosis. In some practical CT imaging applications, the limited-angle scanning is common due to X-ray’s potential harm to human and the limitation of the scanning conditions. Under these circumstances, analytic reconstruction algorithms, like filtered backprojection (FBP), will not obtain satisfactory results because of lacking the projection data. Iterative reconstruction (IR) methods that can incorporate prior knowledge have attracted attention in many fields, and wavelet frame-based regularization reconstruction algorithms have proven to be a useful means to reduce slope artifacts and noise for limited-angle CT. However, with the obtained projection data of the scanned object further reduces, the edge structures and the details of the reconstructed image worsen. For the sake of improving the quality of the reconstructed image from the limited-angle projection data, a guided image filtering (GIF)-based limited-angle CT reconstruction algorithm using wavelet frame was proposed. In each iteration of the proposed algorithm, the reconstructed result constrained by the wavelet frame was used as the guidance image to transfer the important features it contains to the reconstructed result of SART method by GIF. Furthermore, some simulated experiments and real data tests were conducted to evaluate the feasibility and validity of the proposed algorithm, and the qualitative and quantitative indexes indicated that the proposed algorithm was superior to other iterative reconstruction algorithms in artifacts reduction, noise suppression, and structure preservation.

An adaptive multiscale anisotropic diffusion regularized image reconstruction method for digital breast tomosynthesis.

As a special case of tomography, digital breast tomosynthesis (DBT) can realize quasi-3D image reconstruction for breast lesion detection from few-view and limited-angle projection data. For DBT image reconstruction, iterative algorithms are needed to suppress artifacts due to undersampling, and adaptive regularizations are necessary for preserving the edges of masses and calcifications. This paper presents a novel reconstruction method by regularizing projection onto convex sets (POCS) with multiscale Tikhonov-total variation (MTTV). The regularization, known as adaptive multiscale anisotropic diffusion, is able to preserve edges to a considerable extent and selectively suppress noise without introducing artifacts. The proposed method is referred to as MTTV-POCS and is evaluated quantitatively using 3D numerical breast and Shepp-Logan phantoms as well as two clinical volume images acquired from an advanced DBT machine. Experimental results show that the proposed method has better performance in metrics of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) over two existing methods: adaptive-steepest-descent-POCS (ASD-POCS) and selective-diffusion regularized simultaneous algebraic reconstruction technique (SD-SART). As indicated by the results, the proposed method is applicable to DBT for high-quality image reconstruction.

Weighting Algorithm and Relaxation Strategies of the Landweber Method for Image Reconstruction

The iterative approach is important for image reconstruction with ill-posed problem, especially for limited angle reconstruction. Most of iterative algorithms can be written in the general Landweber scheme. In this context, appropriate relaxation strategies and appropriately chosen weights are critical to yield reconstructed images of high quality. In this paper, based on reducing the condition number of matrix ATA, we find one method of weighting matrices for the general Landweber method to improve the reconstructed results. For high resolution images, the approximate iterative matrix is derived. And the new weighting matrices and corresponding relaxation strategies are proposed for the general Landweber method with large dimensional number. Numerical simulations show that the proposed weighting methods are effective in improving the quality of reconstructed image for both complete projection data and limited angle projection data.

Wavelet tight frame and prior image-based image reconstruction from limited-angle projection data

The limited-angle projection data of an object, in some practical applications of computed tomography (CT), are obtained due to the restriction of scanning condition. In these situations, since the projection data are incomplete, some limited-angle artifacts will be presented near the edges of reconstructed image using some classical reconstruction algorithms, such as filtered backprojection (FBP). The reconstructed image can be fine approximated by sparse coefficients under a proper wavelet tight frame, and the quality of reconstructed image can be improved by an available prior image. To deal with limited-angle CT reconstruction problem, we propose a minimization model that is based on wavelet tight frame and a prior image, and perform this minimization problem efficiently by iteratively minimizing separately. Moreover, we show that each bounded sequence, which is generated by our method, converges to a critical or a stationary point. The experimental results indicate that our algorithm can efficiently suppress artifacts and noise and preserve the edges of reconstructed image, what's more, the introduced prior image will not miss the important information that is not included in the prior image.

Review of reconstruction algorithms with incomplete projection data of computed tomography

This paper mainly reviews the progress of methods, and research development in the field of computed tomography(CT) with incomplete projection data, at home and abroad, on limited angle projection data reconstruction with the detector fully covered, and truncated data reconstruction with the detector partially covered. Firstly, the discrete model iterative reconstruction algorithm and the compressed sensing(CS) sampling reconstruction algorithm are discussed for the sparsely uniform and limited-view angle sampling in the case that the detector fully covers the detected object. Secondly, the reconstruction algorithm of back-projection filtration(BPF) for helical cone beam and improved ones for cone beam FDK are discussed in the case that the detector could not fully cover the detected object. This paper could provide the researchers in CT reconstruction field the criticism of methods and summaries. Furthermore, it also points out current focus of the study and the research direction in the future.

SU‐F‐500‐09: Molecular Breast Imaging Using Emission Tomosynthesis

Purpose: Tour objective is to design a novel SPECT system for molecular breast imaging (MBI) and evaluate its performance. The limited angle SPECT system, or emission tomosynthesis, is designed to achieve 3D images of the breast with high spatial resolution/sensitivity. The system uses a simplified detector motion and is conducive to on‐board biopsy and multi‐modal imaging with mammography. Methods: The novel feature of the proposed gamma camera is a variable‐angle, slant‐hole (VASH) collimator, which is well suited for limited angle SPECT of a mildly compressed breast. The collimator holes change slant angle while the camera surface remains flush against the compression paddle. This allows the camera to vary the angular view (±30°, ±45°) for tomographic imaging while keeping the camera close to the object for high spatial resolution and/or sensitivity. Theoretical analysis and Monte Carlo simulations were performed assuming a point source and isolated breast phantom. Spatial resolution, sensitivity, contrast and SNR were measured. Results were compared to single‐view, planar images and conventional SPECT. For both conventional SPECT and VASH, data were reconstructed using iterative algorithms. Finally, a proof‐of‐concept VASH collimator was constructed for experimental evaluation. Results: Measured spatial resolution/sensitivity with VASH showed good agreement with theory including depth‐of‐interaction (DOI) effects. The DOI effect diminished the depth resolution by approximately 2 mm. Increasing the slant angle range from ±30° to ±45° resulted in an approximately 1 mm improvement in the depth resolution. In the breast phantom images, VASH showed improved contrast and SNR over conventional SPECT and improved contrast over planar scintimmammography. Reconstructed images from the proof‐of‐concept VASH collimator demonstrated reasonable depth resolution capabilities using limited angle projection data. Conclusion: We conclude that this limited angle SPECT approach using a VASH collimator can achieve superior 3D image quality for MBI in a design that has attractive characteristics for clinical imaging.

Three-dimensional dental X-ray imaging by combination of panoramic and projection data

A novel three-dimensional dental X-ray imaging method is introduced, based on hybrid data collected with a dental panoramic device. Such a device uses geometric movement of the X-ray source and detector around the head of a patient to produce a panoramic image, where all teeth are in sharp focus and details at a distance from the dental arc are blurred. A digital panoramic device is reprogrammed to collect two-dimensional projection radiographs. Two complementary types of data are measured from a region of interest: projection data with a limited angle of view, and a panoramic image. Tikhonov regularization is applied to these data in order to produce three-dimensional reconstructions. The algorithm is tested with simulated data and real-world in vitro measurements from a dry mandible. Reconstructions from limited-angle projection data alone do provide the dentist with three-dimensional information useful for dental implant planning. Furthermore, adding panoramic data to the process improves the reconstruction precision in the direction of the dental arc. The presented imaging modality can be seen as a cost-effective alternative to a full-angle CT scanner.

SU‐GG‐J‐93: Feasibility Study On Artifact Reduction Using Dual Energy Digital Tomosynthesis

Purpose: We propose a simulation study to examine the feasibility of incorporating dual energy (DE) subtraction with digital tomosynthesis (DTS) to help reduce DTS reconstruction artifacts. Method and Materials: We have previously investigated the incorporation of DTS into radiation therapy for patient localization and found the initial results promising. Due to the limited sampling of the Fourier space, high contrast signals, such as bone, tend to bleed into other slices of the reconstruction. These artifacts could be misconstrued or reduce the conspicuity of low‐contrast signals, especially in the lungs. For this simulation study, an advanced 4D NURBS based Cardiac‐Torso (NCAT) phantom, which includes a detailed whole‐body anatomy of organs including the lungs, airway tree, heart, and vessels, was used. When combined with advanced models of the imaging process, the NCAT phantom can produce realistic imaging data. For this study, a unique x‐ray projection algorithm developed specifically for the NCAT phantom was used to generate limited angle projection data for DTS reconstruction. 81 images were acquired over a 40 degree arc. The breathing cycle was set to simulate breath‐hold imaging. Data sets were generated using spectra of 80 and 120 kVp. For this study, dual energy subtraction (DES) was performed on these two sets of limited angle images to remove the overlying bony anatomy. The resulting images were then reconstructed using a modified filtered back projection technique. This DE‐DTS volume was then visually compared to a standard DTS reconstruction of the 120 kVp data set. Results: The removal of the overlying bony anatomy of the chest in the DE‐DTS images helps to reduce the high contrast artifacts present in the standard DTS reconstruction. Conclusion: This feasibility study demonstrates that DE‐DTS imaging may provide increased visualization of low contrast regions in the chest by reducing high contrast DTS reconstruction artifacts.

Limited Data X-Ray Tomography Using Nonlinear Evolution Equations

A novel approach to the X-ray tomography problem with sparse projection data is proposed. Nonnegativity of the X-ray attenuation coefficient is enforced by modelling it as $\max\{\Phi(x),0\}$, where $\Phi$ is a smooth function. The function $\Phi$ is computed as the equilibrium solution of a nonlinear evolution equation analogous to the equations used in level set methods. The reconstruction algorithm is applied to (a) simulated full and limited angle projection data of the Shepp-Logan phantom with sparse angular sampling and (b) measured limited angle projection data of in vitro dental specimens. The results are significantly better than those given by traditional backprojection-based approaches, and similar in quality (but faster to compute) compared to the algebraic reconstruction technique.

Exact interior reconstruction from truncated limited-angle projection data.

Using filtered backprojection (FBP) and an analytic continuation approach, we prove that exact interior reconstruction is possible and unique from truncated limited-angle projection data, if we assume a prior knowledge on a subregion or subvolume within an object to be reconstructed. Our results show that (i) the interior region-of-interest (ROI) problem and interior volume-of-interest (VOI) problem can be exactly reconstructed from a limited-angle scan of the ROI/VOI and a 180 degree PI-scan of the subregion or subvolume and (ii) the whole object function can be exactly reconstructed from nontruncated projections from a limited-angle scan. These results improve the classical theory of Hamaker et al. (1980).

A METHOD FOR STOP-ACTION IMAGING OF THE HEART USING GATED COMPUTED TOMOGRAPHY

A new method for the reconstruction of limited angle projection data in rotary fan-beam X-ray computed tomography (CT) is presented. Missing views resulting from ECG-gated cardiac CT are estimated, and the standard fan-beam reconstruction algorithm is used to convolve and backproject both measured and estimated views. The estimation of the missing views takes place in three stages: first, the projection data is augmented by incorporating into each missing view the line integrals that do not pass through the heart, and which otherwise would be considered missing due to ECG-gating; second, line integrals corresponding to source positions in the range 180°±fan angle away from missing view angles are reflected; third, those line integrals that remain missing are estimated by interpolation. This method has been applied to ECG-gated cardiac imaging in dogs without requiring extensive interpolation; end-systolic and end-diastolic images were generated with short-interval gating (?cycle) and total scan time (breath holding period) of 12 s. An important advantage of this method over other proposed limited angle reconstruction techniques is that it uses the existing fan-beam convolution-backprojection algorithm for image reconstruction.

A method for stop-action imaging of the heart using gated computed tomography.

A new method for the reconstruction of limited angle projection data in rotary fan-beam X-ray computed tomography (CT) is presented. Missing views resulting from ECG-gated cardiac CT are estimated, and the standard fan-beam reconstruction algorithm is used to convolve and backproject both measured and estimated views. The estimation of the missing views takes place in three stages: first, the projection data is augmented by incorporating into each missing view the line integrals that do not pass through the heart, and which otherwise would be considered missing due to ECG-gating; second, line integrals corresponding to source positions in the range 180°±fan angle away from missing view angles are reflected; third, those line integrals that remain missing are estimated by interpolation. This method has been applied to ECG-gated cardiac imaging in dogs without requiring extensive interpolation; end-systolic and end-diastolic images were generated with short-interval gating (?cycle) and total scan time (breath holding period) of 12 s. An important advantage of this method over other proposed limited angle reconstruction techniques is that it uses the existing fan-beam convolution-backprojection algorithm for image reconstruction.