Cone-beam Computed Tomography Image Reconstruction Research Articles

Do the number of zirconia implants and the thickness of CBCT image reconstruction affect the detection of peri-implant bone defect? A diagnostic accuracy exvivo study.

To evaluate the influence of multiplanar reconstruction thickness on the detection of peri-implant bone defects with a standalone zirconia implant and compare it to when another implant is in the vicinity using cone-beam computed tomography (CBCT). Five dry human mandibles were used to create twenty implant sites in the second premolar and first molar regions. The OP300 Maxio was used to acquire CBCT images (90 kVp, 6.3 mA, 5 × 5 cm FOV, and 0.125 mm3 voxel size) before and after creating 3 mm peri-implant bone defects in the buccal aspect of the premolar region. Half of the scans featured a single zirconia implant in the premolar region, while the others had two implants in the premolar and molar regions. Three reconstruction thicknesses (0.125 mm, 1 mm, and 2 mm) were considered for the multiplanar reconstruction analyses. Five oral and maxillofacial radiologists assessed the detection of peri-implant bone defects using a 5-point scale. Diagnostic parameters were calculated and compared using Two-way ANOVA (α = .05). The studied factors showed no significant influence on the diagnosis of peri-implant bone defects (p > .05). Diagnostic performance was notably higher with a single implant, especially with a 2-mm reconstruction thickness (AUC = 0.88, sensitivity = 0.68, specificity = 0.94). Although the differences were not statistically significant, the results were more modest when two implants were present (AUC = 0.80, sensitivity = 0.58, specificity = 0.82). The presence of an adjacent zirconia implant and variations in reconstruction thickness did not influence the detection of 3 mm buccal peri-implant bone defects on CBCT images.

Sparse-view cone-beam computed tomography iterative reconstruction based on new multi-gradient direction total variation.

The accurate reconstruction of cone-beam computed tomography (CBCT) from sparse projections is one of the most important areas for study. The compressed sensing theory has been widely employed in the sparse reconstruction of CBCT. However, the total variation (TV) approach solely uses information from the i-coordinate, j-coordinate, and k-coordinate gradients to reconstruct the CBCT image. It is well recognized that the CBCT image can be reconstructed more accurately with more gradient information from different directions. Thus, this study introduces a novel approach, named the new multi-gradient direction total variation minimization method. The method uses gradient information from the ij-coordinate, ik-coordinate, and jk-coordinate directions to reconstruct CBCT images, which incorporates nine different types of gradient information from nine directions. This study assessed the efficacy of the proposed methodology using under-sampled projections from four different experiments, including two digital phantoms, one patient's head dataset, and one physical phantom dataset. The results indicated that the proposed method achieved the lowest RMSE index and the highest SSIM index. Meanwhile, we compared the voxel intensity curves of the reconstructed images to assess the edge structure preservation. Among the various methods compared, the curves generated by the proposed method exhibited the highest level of consistency with the gold standard image curves. In summary, the proposed method showed significant potential in enhancing the quality and accuracy of CBCT image reconstruction.

Effect of auto-adaptive metal artifact reduction (aMAR) program in cone-beam computed tomography on assessing pre-implant bone levels.

This research aimed to introduce an auto-adaptive metal artifact reduction (aMAR) algorithm in cone-beam computed tomography (CBCT) to assess the levels of the pre-implant alveolar crest. Dental implants as a treatment modality for edentulous patients consist of a titanium alloy, which creates a metal artifact, resulting in a dark dental structure in the CBCT scans. Metallic artifacts are limiting factors for the precise detection in CBCT images. These are related to the dark areas around materials and metallic structures (e.g., restorations, implants, and endodontic instruments). To overcome this problem, the metal artifact reduction (MAR) program has been recommended as a post-procedure stage for CBCT image reconstruction. Recent developments offer CBCT scanners with an aMAR option with a greater dynamic range to help overcome the challenges of peri-implant bone evaluation to reach accurate dental diagnoses.

A highly accurate quantum optimization algorithm for CT image reconstruction based on sinogram patterns

Computed tomography (CT) has been developed as a nondestructive technique for observing minute internal images in samples. It has been difficult to obtain photorealistic (clean or clear) CT images due to various unwanted artifacts generated during the CT scanning process, along with the limitations of back-projection algorithms. Recently, an iterative optimization algorithm has been developed that uses an entire sinogram to reduce errors caused by artifacts. In this paper, we introduce a new quantum algorithm for reconstructing CT images. This algorithm can be used with any type of light source as long as the projection is defined. Assuming an experimental sinogram produced by a Radon transform, to find the CT image of this sinogram, we express the CT image as a combination of qubits. After acquiring the Radon transform of the undetermined CT image, we combine the actual sinogram and the optimized qubits. The global energy optimization value used here can determine the value of qubits through a gate model quantum computer or quantum annealer. In particular, the new algorithm can also be used for cone-beam CT image reconstruction and for medical imaging.

Nonlinear ill-posed problem in low-dose dental cone-beam computed tomography

Abstract This paper describes the mathematical structure of the ill-posed nonlinear inverse problem of low-dose dental cone-beam computed tomography (CBCT) and explains the advantages of a deep learning-based approach to the reconstruction of computed tomography images over conventional regularization methods. This paper explains the underlying reasons why dental CBCT is more ill-posed than standard computed tomography. Despite this severe ill-posedness, the demand for dental CBCT systems is rapidly growing because of their cost competitiveness and low radiation dose. We then describe the limitations of existing methods in the accurate restoration of the morphological structures of teeth using dental CBCT data severely damaged by metal implants. We further discuss the usefulness of panoramic images generated from CBCT data for accurate tooth segmentation. We also discuss the possibility of utilizing radiation-free intra-oral scan data as prior information in CBCT image reconstruction to compensate for the damage to data caused by metal implants.

Local edge computing for radiological image reconstruction and computer-assisted detection: A feasibility study

Computational requirements for data processing at different stages of the radiology value chain are increasing. Cone beam computed tomography (CBCT) is a diagnostic imaging technique used in dental and extremity imaging, involving a highly demanding image reconstruction task. In turn, artificial intelligence (AI) assisted diagnostics are becoming increasingly popular, thus increasing the use of computation resources. Furthermore, the need for fully independent imaging units outside radiology departments and with remotely performed diagnostics emphasize the need for wireless connectivity between the imaging unit and hospital infrastructure. In this feasibility study, we propose an approach based on a distributed edge-cloud computing platform, consisting of small-scale local edge nodes, edge servers with traditional cloud resources to perform data processing tasks in radiology. We are interested in the use of local computing resources with Graphics Processing Units (GPUs), in our case Jetson Xavier NX, for hosting the algorithms for two use-cases, namely image reconstruction in cone beam computed tomography and AI-assisted cancer detection from mammographic images. Particularly, we wanted to determine the technical requirements for local edge computing platform for these two tasks and whether CBCT image reconstruction and breast cancer detection tasks are possible in a diagnostically acceptable time frame. We validated the use-cases and the proposed edge computing platform in two stages. First, the algorithms were validated use-case-wise by comparing the computing performance of the edge nodes against a reference setup (regular workstation). Second, we performed qualitative evaluation on the edge computing platform by running the algorithms as nanoservices. Our results, obtained through real-life prototyping, indicate that it is possible and technically feasible to run both reconstruction and AI-assisted image analysis functions in a diagnostically acceptable computing time. Furthermore, based on the qualitative evaluation, we confirmed that the local edge computing capacity can be scaled up and down during runtime by adding or removing edge devices without the need for manual reconfigurations. We also found all previously implemented software components to be transferable as such. Overall, the results are promising and help in developing future applications, e.g., in mobile imaging scenarios, where such a platform is beneficial.

Triple-source saddle-curve cone-beam photon counting CT image reconstruction: A simulation study

PurposeThe most common detector material in the PC CT system, cannot achieve the best performance at a relatively higher photon flux rate. In the reconstruction view, the most commonly used filtered back projection, is not able to provide sufficient reconstructed image quality in spectral computed tomography (CT). Developing a triple-source saddle-curve cone-beam photon counting CT image reconstruction method can improve the temporal resolution. MethodsTriple-source saddle-curve cone-beam trajectory was rearranged into four trajectory sets for simulation and reconstruction. Projection images in different energy bins were simulated by forward projection and photon counting CT respond model simulation. After simulation, the object was reconstructed using Katsevich’s theory after photon counts correction using the pseudo inverse of photon counting CT response matrix. The material decomposition can be performed based on images in different energy bins. ResultsRoot mean square error (RMSE) and structural similarity index (SSIM) are calculated to quantify the image quality of reconstruction images. Compared with FDK images, the RMSE for the triple-source image was improved by 27%, 21%, 14%, 8%, and 6% for the reconstrued image of 20–33, 33–47, 47–58, 58–69, 69–80 keV energy bin. The SSIM was improved by 1.031%, 0.665%, 0.396%, 0.235%, 0.174% for corresponding energy bin. The decomposition image based on corrected images shows improved RMSE and SSIM, each by 33.861% and 0.345%. SSIM of corrected decomposition image of iodine reaches 99.415% of the original image. ConclusionsA new Triple-source saddle-curve cone-beam PC CT image reconstruction method was developed in this work. The exact reconstruction of the triple-source saddle-curve improved both the image quality and temporal resolution.

General simultaneous motion estimation and image reconstruction (G-SMEIR)

To achieve better performance for 4D multi-frame reconstruction with the parametric motion model (MF-PMM), a general simultaneous motion estimation and image reconstruction (G-SMEIR) method is proposed. In G-SMEIR, projection domain motion estimation and image domain motion estimation are performed alternatively to achieve better 4D reconstruction. This method can mitigate the local optimum trapping problem in either domain. To improve computational efficiency, the image domain motion estimation is accelerated by adapting fast convergent algorithms and graphics processing unit (GPU) computing. The proposed G-SMEIR method is tested using a cone-beam computed tomography (CBCT) simulation study of 4D XCAT phantom at different dose levels and compared with 3D total variation-based reconstruction (3D TV), 4D reconstruction with image domain motion estimation (IM4D), and SMEIR. G-SMEIR shows strong denoising capability and achieves similar performance at regular dose and half dose. The root mean squared error (RMSE) of G-SMEIR is the best among the four methods and improved about 12% over SMEIR for all respiratory phase images at full dose. G-SMEIR also achieved the best structural similarity index (SSIM) values among all methods. More importantly, G-SMEIR leads to more than 40% improvement of the mean deviation from the phantom tumor motion over SMEIR. A preliminary patient CBCT image reconstruction also shows better image quality of G-SMEIR than that of the frame-by-frame reconstruction (3D TV) and MF-PMM either using image domain motion estimation (IM4D) or using projection domain motion estimation (SMEIR) alone. G-SMEIR with a flexible combination of image domain and projection domain motion estimation provides an effective tool for 4D tomographic reconstruction.

Joint Motion Estimation and Compensation for Four-Dimensional Cone-Beam Computed Tomography Image Reconstruction

Four-Dimensional Cone-Beam Computed Tomography (4D-CBCT) imaging is important in upper abdomen tumor radiation treatment as it can obtain the tumor motion information immediately before treatment. However, the number of projections at a single phase for 4D-CBCT imaging is very small, and the CT images reconstructed by using conventional algorithms will be contaminated by view aliasing artifacts and noises. To address this problem, we propose a framework to jointly estimate and compensate the inter-phase motion for 4D-CBCT image reconstruction. Specifically, by introducing the intensity-based optical flow (OF) constraint into the reconstruction framework, the model can deal with the inter-frame displacements and improve the image quality simultaneously. The primal-dual algorithm method was used to optimize the cost function of the proposed model. Experiments on physical phantoms and patient data show that the proposed approach can effectively reduce the noise and artifacts in 4D–CBCT images and achieve the inter-phase motion vector fields (MVFs).

Decoupling of bowtie and object effects for beam hardening and scatter artefact reduction in iterative cone-beam CT.

Cone-beam computed tomography (CBCT) is an important imaging modality for image-guided radiotherapy and adaptive radiotherapy. Feldkamp-Davis-Kress (FDK) method is widely adopted in clinical CBCT reconstructions due to its fast and robust application. While iterative algorithms have been shown to outperform FDK techniques in reducing noise and imaging dose, they are unable to correct projection-domain artefacts such as beam hardening and scatter. Empirical correction techniques require a holistic approach as beam hardening and scatter coexist in the measurement data. This multi-part proof of concept study conducted in MATLAB presents a novel approach to artefact reduction for CBCT image reconstruction. Firstly, we decoupled the beam hardening and scatter contributions originating from the imaging object and the bowtie filter. Next, a model was constructed to apply pixel-wise corrections to separately account for artefacts induced by the imaging object and the bowtie filter, in order to produce mono-energetic equivalent and scatter-compensated projections. Finally, the effectiveness of the correction model was tested on an offset phantom scan as well as a clinical brain scan. A conjugate-gradient least-squares algorithm was implemented over five iterations using FDK result as the initial input. Our proposed correction model was shown to effectively reduce cupping and shading artefacts in both phantom and clinical studies. This simple yet effective correction model could be readily implemented by physicists seeking to explore the benefits of iterative reconstruction.

Data sustained misalignment correction in microscopic cone beam CT via optimization under the Grangeat Epipolar consistency condition.

The misalignment correction in cone beam computed tomography (CBCT), which is usually carried out in an offline manner, is a difficult and tedious process. It becomes even more challenging in microscopic CBCT due to the much higher requirements on spatial resolution. In practice, however, an offline approach for misalignment correction may not be readily implementable, especially in the situations where either time is of the essence or the process needs to be carried out repetitively. Thus, an online self-calibration (i.e., data sustained misalignment correction without the involvement of specific alignment phantom) would be more practical. In this work, we investigate the data sustained misalignment correction in microscopic CBCT via optimization under the Grangeat Epipolar Consistence Condition and evaluate its performance via phantom and specimen studies. With the cost function defined according to the Grangeat Epipolar Consistency Condition (G-ECC) and by minimizing the cost function using the simplex-simulated annealing algorithm (SIMPSA), we evaluate and verify the G-ECC optimization-based online self-calibration method's performance. Performance is measured in sensitivity, robustness, and accuracy using the projection data of phantoms generated by computer simulation and botanical specimens acquired by a prototype microscopic CBCT. The online data sustained misalignment correction in microscopic CBCT via G-ECC optimization works very well in sensitivity and robustness, in addition to its accuracy of 0.27%, 0.48%, and 0.34% relative errors, respectively, in obtaining the three geometric parameters that are the most critical to image reconstruction in CBCT. Quantitatively, the performance in meeting the requirements on spatial resolution is comparable to, if not better than, that of the offline misalignment correction method, in which a specific alignment phantom has to be used. The G-ECC optimization-based online self-calibration approach provides a practical solution (as long as no latitudinal (lateral) data truncation occurs) for misalignment correction in microscopic CBCT, an application that demands high accuracy in geometric alignment for biological (cellular) imaging at super high spatial resolutions in the order of micrometers (2.1µm).

Analysis of the middle region of the pharynx in adolescents with different anteroposterior craniofacial skeletal patterns.

ABSTRACTObjective: To assess the volume and morphology of the middle region of the pharynx (MRP) in adolescents with different anteroposterior craniofacial skeletal patterns. Methods: One hundred twenty-six patients (56 male and 70 female), who had cone-beam computed tomography (CBCT) within their records, were selected for this cross-sectional study. Participants were classified, according to their ANB angle value, in Class I (1o ≤ ANB ≤ 3o), Class II (ANB > 3o) and Class III (ANB < 1o). The total volume (tV), minimum axial area (AxMin) and morphology of the MRP and its subdivisions - velopharynx (VP) and oropharynx (OP) - were characterized by CBCT and 3-dimensional image reconstruction software. Intergroup comparisons were performed by ANOVA and Tukey post-hoc tests. Correlations between tV and Axmin with the ANB angle values were tested using linear regression analysis, considering sex as covariable. Results: Statistically significant difference between groups were observed in tV only for the VP region; Class II individuals presented significantly lower tV (6863.75 ± 2627.20 mm3) than Class III subjects (9011.62 ± 3442.56 mm3) (p< 0.05). No significant differences were observed between groups for any other variable assessed, neither in MRP nor in the OP region (p> 0.05). A significant negative correlation was evidenced between tV and Axmin and the ANB angle values; sexual dimorphism was observed for some variables. Conclusions: Class II subjects have smaller tV in the VP region. tV and Axmin tend to decrease in all evaluated regions when the ANB angle values increase.

Cone-beam computed tomography for determination of mesial root canal curvatures of human mandibular first molars

To investigate mesial root canal curvatures of human mandibular first molars using cone-beam computed tomography (CBCT). CBCT was performed for scanning the mandibular first molars of 1600 Chinese subjects, among whom the subjects identified to have middle mesial canals were divided into 5 age groups, namely group A (18-29 years), group B (30-39 years), group C (40-49 years), group D (50-59 years), and group E (60-80 years) for further analysis. All the CBCT images were reconstructed using Planmeca Romexis CBCT image reconstruction system, and the curvature of the mesial root was measured using the Schneider method. A total of 2856 CBCT images were obtained from the 1600 subjects, among whom 168 (5.88%) were found to have middle mesial canals in the mandibular first molars. The prevalence of middle mesial canals was 37.50% in group A, 17.80% in group B, 14.88% in group C, 19.64% in group D, and 10.12% in group E. The moderate of the curvature in the mesiodistal direction was significantly higher in group A than in the other groups (P < 0.05); the curvature in mesiodistal direction differed significantly among the 5 groups (P < 0.05), and was the greatest in group A followed by group C and group D, and was the smallest in group E. The curvature in the buccolingual direction was similar among the 5 groups. The incidence of mesiodistal and buccolingual curvature of the middle mesial canals was significantly higher in the middle 1/3 of the root canal than in the upper 1/3 and apical 1/3 of the root canal. The incidence of middle mesial canals in the mesial root of the mandibular first molars decreases with age. The middle mesial canal system of the mandibular first molar is complex and variable, and most of the first molars have obvious curvature in the mesiodistal and buccolingual directions. CBCT can provide reference for clinical treatment to reduce the treatment failure rate.

Quantification of bone microstructure in the wrist using cone-beam computed tomography.

Due to the rising life expectancy, bone diseases (e.g. osteoporosis, osteoarthritis) and trauma (e.g. fracture) have become an important socio-economic burden. Accurate visualization and quantification of the bone microstructure in vivo is seen as an important step to enhance diagnosis and treatment. Micro-computed tomography (microCT) has become the gold standard in three-dimensional (3D) imaging of trabecular bone structure. Yet, usage is limited to ex vivo analyses, hence, it cannot be used to evaluate bone and bone adaptive responses in a patient. High-resolution peripheral computed tomography (HR-pQCT) is considered the best technique to measure the bone microarchitecture in vivo. By design HR-pQCT is limited to scanning extremities, such as the distal radius and distal tibia with a limited field of view and long scanning time (~2 à 3 min. for a stack of 0.9 cm). Cone-beam computed tomography (CBCT) is a promising alternative with a much larger field of view. Yet, CBCT is challenged by artefacts that reduce image contrast, such that it is currently being used for qualitative evaluation only. Therefore, the aims of this work were first to enhance image contrast and second to determine the accuracy of high-resolution CBCT for bone microarchitectural assessment. Trapezia of nineteen female arthritic patients were scanned twice ex vivo; once using CBCT (NewTom 5G, Cefla, Verona, Italy) at a nominal voxel size of 75 μm and once using microCT (SkyScan 1172, Bruker, Kontich, Belgium) at a voxel size of 19.84 μm. The CBCT-scans were reconstructed following 2 protocols: (1) using the commercial software delivered with the scanner and (2) using in-house developed software. After reconstruction and image processing, the images were segmented using adaptive thresholding. Bone morphometric parameters including bone volume (BV), total tissue volume (TV), bone volume fraction (BV/TV), bone surface density (BS/TV), trabecular thickness (Tb.Th), trabecular separation (Tb.Sp) and trabecular number (Tb.N) were calculated. Statistical evaluations were made at a significance level of 5%. Significant correlations were found between the CBCT-based bone parameters and the microCT-based parameters with R2 > 0.68 The in-house reconstructed software outperformed the commercial software. Smaller bias (overestimation of Tb.Th decreased from 114.24% to 59.96%) as well as higher correlations were observed for the in-house processed images. Still, a significant overestimation was observed for BV/TV and Tb. Th and an underestimation for Tb.N. We conclude that our CBCT image reconstruction improved image contrast which allowed for an accurate quantification of trabecular bone microarchitecture.

Cone-beam CT reconstruction for non-periodic organ motion using time-ordered chain graph model

PurposeThe purpose of this study is to introduce the new concept of a four-dimensional (4D) cone-beam computed tomography (CBCT) reconstruction approach for non-periodic organ motion in cooperation with the time-ordered chain graph model (TCGM) and to compare it with previously developed methods such as total variation-based compressed sensing (TVCS) and prior-image constrained compressed sensing (PICCS).Materials and MethodsOur proposed reconstruction is based on a model including the constraint originating from the images of neighboring time phases. Namely, the reconstructed time-series images depend on each other in this TCGM scheme, and the time-ordered images are concurrently reconstructed in the iterative reconstruction approach. In this study, iterative reconstruction with the TCGM was carried out with 90° projection ranges. The images reconstructed by the TCGM were compared with the images reconstructed by TVCS (200° projection ranges) and PICCS (90° projection ranges).Two kinds of projection data sets–an elliptic-cylindrical digital phantom and two clinical patients’ data–were used. For the digital phantom, an air sphere was contained and virtually moved along the longitudinal axis by 3 cm/30 s and 3 cm/60 s; the temporal resolution was evaluated by measuring the penumbral width of the air sphere. The clinical feasibility of the non-periodic time-ordered 4D CBCT image reconstruction was examined with the patient data in the pelvic region.ResultsIn the evaluation of the digital-phantom reconstruction, the penumbral widths of the TCGM yielded the narrowest result; the results obtained by PICCS and TCGM using 90° projection ranges were 2.8% and 18.2% for 3 cm/30 s, and 5.0% and 23.1% for 3 cm/60 s narrower than that of TVCS using 200° projection ranges. This suggests that the TCGM has a better temporal resolution, whereas PICCS seems similar to TVCS. These reconstruction methods were also compared using patients’ projection data sets. Although all three reconstruction results showed motion related to rectal gas or stool, the result obtained by the TCGM was visibly clearer with less blurring.ConclusionThe TCGM is a feasible approach to visualize non-periodic organ motion. The digital-phantom results demonstrated that the proposed method provides 4D image series with a better temporal resolution compared to TVCS and PICCS. The clinical patients’ results also showed that the present method enables us to visualize motion related to rectal gas and flatus in the rectum.

Enhancement of four-dimensional cone-beam computed tomography by compressed sensing with Bregman iteration

In four-dimensional (4D) cone-beam computed tomography (CBCT), there is a spatio-temporal tradeoff that currently limits the accuracy. The aim of this study is to develop a Bregman iteration based formalism for high quality 4D CBCT image reconstruction from a limited number of low-dose projections. The 4D CBCT problem is first divided into multiple 3D CBCT subproblems by grouping the projection images corresponding to the phases. To maximally utilize the information from the under-sampled projection data, a compressed sensing (CS) method with Bregman iterations is employed for solving each subproblem. We formulate an unconstrained optimization problem based on least-square criterion regularized by total-variation. The least-square criterion reflects the inconsistency between the measured and the estimated line integrals. Furthermore, the unconstrained problem is updated and solved repeatedly by Bregman iterations. The performance of the proposed algorithm is demonstrated through a series of simulation studies and phantom experiments, and the results are compared to those of previously implemented compressed sensing technique using other gradient-based methods as well as conventional filtered back-projection (FBP) results. The simulation and experimental studies have shown that artifact suppressed images can be obtained with as small as 41 projections per phase, which is adequate for clinical 4D CBCT reconstruction. With such small number of projections, the conventional FDK failed to yield meaningful 4D CBCT images, and CS technique using conjugate gradient was not able to recover sharp edges. The proposed method significantly reduces the radiation dose and scanning time to achieve the high quality images compared to the 4D CBCT imaging based on the conventional FDK technique and the existing CS techniques.

WE‐A‐301‐03: Enhancement of Four‐Dimensional Cone‐Beam Computed Tomography by Compressed Sensing with Bregman Iteration

Purpose: In four‐dimensional (4D) cone‐beam computed tomography (CBCT), there is a spatio‐temporal tradeoff. The aim of this study is to develop a Bregman iteration based formalism for high quality 4D CBCT image reconstruction from low‐dose projections. Methods: The 4D CBCT problem is first divided into multiple 3D CBCT subproblems by grouping the projection images corresponding to the phases. To maximally utilize the information from the under‐sampled projection data, a compressed sensing method is employed for solving each subproblem. We formulate an unconstrained lasso (least absolute shrinkage and selection operator) problem based on least‐square criterion regularized by total‐variation. The least‐square criterion reflects the inconsistency between the measured and the estimated line integrals. Furthermore, the unconstrained lasso problem is updated and solved repeatedly by Bregman iterations. Results: The performance of the proposed algorithm is demonstrated through a series of phantom experiments, and the results are compared to those of conventional filtered back‐projection (FBP). The simulation studies have shown that artifact suppressed images can be obtained with as small as 41 projections per phase, which is adequate for clinical 4D CBCT reconstruction without slowing down the gantry rotation. With such small number of projections, the conventional FBP failed to yield meaningful 4D CBCT images. Conclusions: The proposed method significantly reduces the radiation dose and scanning time to achieve the high quality images compared to the conventional 4D CBCT imaging based on FBP technique.

Combining scatter reduction and correction to improve image quality in cone‐beam computed tomography (CBCT)

The authors propose a combined scatter reduction and correction method to improve image quality in cone-beam computed tomography (CBCT). Although using a beam-block approach similar to previous techniques to measure the scatter, this method differs in that the authors utilize partially blocked projection data obtained during scatter measurement for CBCT image reconstruction. This study aims to evaluate the feasibility of the proposed approach. A 1D grid, composed of lead septa, was placed between the radiation source and the imaging object for scatter measurement. Image data were collected from the grid interspace regions while the scatter distribution was measured in the blocked regions under the grid. Scatter correction was performed by subtracting the measured scatter from the imaging data. Image information in the penumbral regions of the grid was derived. Three imaging modes were developed to reconstruct full CBCT images from partial projection data. The single-rotation half-fan mode uses interpolation to fill the missing data. The dual-rotation half-fan mode uses two rotations, with the grid offset by half a grid cycle, to acquire two complementary sets of projections, which are then merged to form complete projections for reconstruction. The single-rotation full-fan mode was designed for imaging a small object or a region of interest. Full-fan projection images were acquired over a 360 degrees scan angle with the grid shifting a distance during the scan. An enlarged Catphan phantom was used to evaluate potential improvement in image quality with the proposed technique. An anthropomorphic pelvis phantom was used to validate the feasibility of reconstructing a complete set of CBCT images from the partially blocked projections using three imaging modes. Rigid-body image registration was performed between the CBCT images from the single-rotation half-fan mode and the simulation CT and the results were compared to that for the CBCT images from dual-rotation mode and conventional CBCT images. The proposed technique reduced the streak artifact index from 58% to 1% in comparison with the conventional CBCT. It also improved CT number linearity from 0.880 to 0.998 and the contrast-to-noise ratio (CNR) from 4.29 to 6.42. Complete sets of CBCT images with overall improved image quality were achieved for all three image modes. The longitudinal resolution was slightly compromised for the single-rotation half-fan mode. High resolution was retained for the dual-rotation half-fan and single-rotation full-fan modes in the longitudinal direction. The registration error for the CBCT images from the single-rotation half-fan mode was 0.8 +/- 0.3 mm in the longitudinal direction and negligible in the other directions. The proposed method provides combined scatter correction and direct scatter reduction. Scatter correction may eliminate scatter artifacts, while direct scatter reduction may improve the CNR to compensate the CNR degradation due to scatter correction. Complete sets of CBCT images are reconstructed in all three imaging modes. The single-rotation mode can be used for rigid-body patient alignment despite degradation in longitudinal resolution. The dual-rotation mode may be used to improve CBCT image quality for soft tissue delineation in adaptive radiation therapy.

New iterative cone beam CT reconstruction software: Parameter optimisation and convergence study

Cone beam computed tomography (CBCT) provides a volumetric image reconstruction from tomographic projection data. Image quality is the main concern for reconstruction in comparison to conventional CT. The reconstruction algorithm used is clearly important and should be carefully designed, developed and investigated before it can be applied clinically. The Multi-Instrument Data Analysis System (MIDAS) tomography software originally designed for geophysical applications has been modified to CBCT image reconstruction. In CBCT reconstruction algorithms, iterative methods offer the potential to generate high quality images and would be an advantage especially for down-sampling projection data. In this paper, studies of the CBCT iterative algorithms implemented in MIDAS are presented. Stability, convergence rate, quality of reconstructed image and edge recovery are suggested as the main criteria for monitoring reconstructive performance. Accordingly, the selection of relaxation parameter and number of iterations are studied in detail. Results are presented, where images are reconstructed from full and down-sampled cone beam CT projection data using iterative algorithms. Various iterative algorithms have been implemented and the best selection of the iteration number and relaxation parameters are investigated for ART. Optimal parameters are chosen where the errors in projected data as well as image errors are minimal.

Fast iterative reconstructions for animal CT

For iterative x-ray computed tomography (CT) reconstruction, the convex algorithm combined with ordered subset (OSC) [1]is a relatively fast algorithm and has shown its potential for low-dose situations. But it needs one forward projection and two backprojections per iteration. Unlike convex algorithm, the gradient algorithm only requires one forward projection and one backprojection per iteration. Here, we applied ordered subsets of projection data to a modified gradient algorithm. In order to further reduce computation time, the new algorithm, the ordered subset gradient (OSG) algorithm, can be adjusted with a step size. We also implemented another OS-type algorithm called OSTR. The OSG algorithm is compared with OSC algorithm and OSTR algorithm using three-dimensional simulated helical cone-beam CT data. The performance is evaluated in terms of log-likelihood, contrast recovery, and bias-variance studies. Results show that images of OSG has compatible visual image quality to those of OSC and OSTR, but in the resolution and bias-variance studies, OSG seems to reach stable values with faster speed. In particular, OSTR has better recovery in a smoother region, but both OSG and OSC have better recovery in the high-frequency regions. Moreover, in terms of log likelihood with respect to computation time, OSG has faster convergence rate than that of OSC and similar to that of OSTR. We conclude that OSG has potential to provide comparable image quality and is more computationally efficient, and thus could be suitable for low-dose, helical cone-beam CT image reconstruction.