Cone-beam Reconstruction Research Articles

Mandibular Actinomyces Osteitis Associated with Florid Cemento-Osseous Dysplasia

Introduction: Actinomycosis is a rare chronic suppurative granulomatous infection caused by actinomyces. The Florid cemento-osseous dysplasia (FCOD) is a benign fibro-osseous lesion. We report a case of simultaneous association of these two entities with the use of PRF for the first time in this context. Case Report: A 59-year-old female with no medical history was received in our clinic for painful swelling on the right mandible. On clinical examination, we noticed a facial asymmetry caused by the swelling and bone exposure with pus on the right mandibular ridge. The panoramic radiography showed right and left posterior mandibular opacities. On the cone beam reconstructions, we noted a hyperdense posterior right mandibular image surrounded by a hypodense border and a hyperdense posterior left mandibular image surrounded by a hypodense border, in contact with the apices of tooth 36. Based on the clinical, radiographic and pathological findings, the present case was diagnosed as mandibular actinomyces osteitis associated with florid cemento-osseous dysplasia. The treatment consisted of surgical excision of the lesion, filling of the defect with PRF and prescription of antibiotics. Conclusion: Despite its rarity, practitioners should be aware of the possibility of the coexistence of these two entities simultaneously. Thus, they can initiate the appropriate treatment.

Motion compensated cone-beam CT reconstruction using an a priori motion model from CT simulation: a pilot study

Objective. To combat the motion artifacts present in traditional 4D-CBCT reconstruction, an iterative technique known as the motion-compensated simultaneous algebraic reconstruction technique (MC-SART) was previously developed. MC-SART employs a 4D-CBCT reconstruction to obtain an initial model, which suffers from a lack of sufficient projections in each bin. The purpose of this study is to demonstrate the feasibility of introducing a motion model acquired during CT simulation to MC-SART, coined model-based CBCT (MB-CBCT). Approach. For each of 5 patients, we acquired 5DCTs during simulation and pre-treatment CBCTs with a simultaneous breathing surrogate. We cross-calibrated the 5DCT and CBCT breathing waveforms by matching the diaphragms and employed the 5DCT motion model parameters for MC-SART. We introduced the Amplitude Reassignment Motion Modeling technique, which measures the ability of the model to control diaphragm sharpness by reassigning projection amplitudes with varying resolution. We evaluated the sharpness of tumors and compared them between MB-CBCT and 4D-CBCT. We quantified sharpness by fitting an error function across anatomical boundaries. Furthermore, we compared our MB-CBCT approach to the traditional MC-SART approach. We evaluated MB-CBCT’s robustness over time by reconstructing multiple fractions for each patient and measuring consistency in tumor centroid locations between 4D-CBCT and MB-CBCT. Main results. We found that the diaphragm sharpness rose consistently with increasing amplitude resolution for 4/5 patients. We observed consistently high image quality across multiple fractions, and observed stable tumor centroids with an average 0.74 ± 0.31 mm difference between the 4D-CBCT and MB-CBCT. Overall, vast improvements over 3D-CBCT and 4D-CBCT were demonstrated by our MB-CBCT technique in terms of both diaphragm sharpness and overall image quality. Significance. This work is an important extension of the MC-SART technique. We demonstrated the ability of a priori 5DCT models to provide motion compensation for CBCT reconstruction. We showed improvements in image quality over both 4D-CBCT and the traditional MC-SART approach.

Calibration-free 3D ray-tracing beam hardening correction in computed tomography

In this paper we introduce a calibration-free beam-hardening correction technique in computed tomography, which improves the results achieved by a state-of-the-art correction method. Two main practical points are here underlined: (1) the need to perform a beam hardening correction without previous calibration; (2) the need to consider the shape of the object in all its whole three-dimensional extent. The proposed method requires: (a) a first complete reconstruction of the object by means of a cone-beam filtered back projection algorithm; (b) a segmentation of the reconstructed volume; (c) the construction of the attenuation when varying path-length graph; (d) the interpolation of the experimental data on the graph with a convenient mathematical function; (e) the correction of the measured attenuation values; (f) to perform again the complete cone-beam reconstruction. The result is an effective beam-hardening calibration-free correction over a large variety of objects, easy to use and quite fast. The proposed method results particularly useful when both the measurement time and the control of the geometry of the system are limited, like the ones generally faced in Cultural Heritage diagnostics. Computed Tomography reconstructions for test samples and for real cases are provided to highlight the effectiveness and the advantages of the new suggested approach.

Surrogate-driven respiratory motion model for projection-resolved motion estimation and motion compensated cone-beam CT reconstruction from unsorted projection data

Objective. As the most common solution to motion artefact for cone-beam CT (CBCT) in radiotherapy, 4DCBCT suffers from long acquisition time and phase sorting error. This issue could be addressed if the motion at each projection could be known, which is a severely ill-posed problem. This study aims to obtain the motion at each time point and motion-free image simultaneously from unsorted projection data of a standard 3DCBCT scan. Approach. Respiration surrogate signals were extracted by the Intensity Analysis method. A general framework was then deployed to fit a surrogate-driven motion model that characterized the relation between the motion and surrogate signals at each time point. Motion model fitting and motion compensated reconstruction were alternatively and iteratively performed. Stochastic subset gradient based method was used to significantly reduce the computation time. The performance of our method was comprehensively evaluated through digital phantom simulation and also validated on clinical scans from six patients. Results. For digital phantom experiments, motion models fitted with ground-truth or extracted surrogate signals both achieved a much lower motion estimation error and higher image quality, compared with non motion-compensated results.For the public SPARE Challenge datasets, more clear lung tissues and less blurry diaphragm could be seen in the motion compensated reconstruction, comparable to the benchmark 4DCBCT images but with a higher temporal resolution. Similar results were observed for two real clinical 3DCBCT scans. Significance. The motion compensated reconstructions and motion models produced by our method will have direct clinical benefit by providing more accurate estimates of the delivered dose and ultimately facilitating more accurate radiotherapy treatments for lung cancer patients.

Joint denoising and interpolating network for low-dose cone-beam CT reconstruction under hybrid dose-reduction strategy

Cone-beam computed tomography (CBCT) is generally reconstructed with hundreds of two-dimensional X-Ray projections through the FDK algorithm, and its excessive ionizing radiation of X-Ray may impair patients’ health. Two common dose-reduction strategies are to either lower the intensity of X-Ray, i.e., low-intensity CBCT, or reduce the number of projections, i.e., sparse-view CBCT. Existing efforts improve the low-dose CBCT images only under a single dose-reduction strategy. In this paper, we argue that applying the two strategies simultaneously can reduce dose in a gentle manner and avoid the extreme degradation of the projection data in a single dose-reduction strategy, especially under ultra-low-dose situations. Therefore, we develop a Joint Denoising and Interpolating Network (JDINet) in projection domain to improve the CBCT quality with the hybrid low-intensity and sparse-view projections. Specifically, JDINet mainly includes two important components, i.e., denoising module and interpolating module, to respectively suppress the noise caused by the low-intensity strategy and interpolate the missing projections caused by the sparse-view strategy. Because FDK actually utilizes the projection information after ramp-filtering, we develop a filtered structural similarity constraint to help JDINet focus on the reconstruction-required information. Afterward, we employ a Postprocessing Network (PostNet) in the reconstruction domain to refine the CBCT images that are reconstructed with denoised and interpolated projections. In general, a complete CBCT reconstruction framework is built with JDINet, FDK, and PostNet. Experiments demonstrate that our framework decreases RMSE by approximately 8 %, 15 %, and 17 %, respectively, on the 1/8, 1/16, and 1/32 dose data, compared to the latest methods. In conclusion, our learning-based framework can be deeply imbedded into the CBCT systems to promote the development of CBCT. Source code is available at https://github.com/LianyingChao/FusionLowDoseCBCT.

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.

Single-slice rebinning reconstruction method for segmented helical computed tomography.

Recently, to easily extend the helical field-of-view (FOV), the segmented helical computed tomography (SHCT) method was proposed, as well as the corresponding generalized backprojection filtration (G-BPF) type algorithm. Similar to the geometric relationship between helical and circular CT, SHCT just becomes full-scan multiple source-translation CT (F-mSTCT) when the pitch is zero and the number of scan cycles is one. The strategy of G-BPF follows the idea of the generalized Feldkamp approximate cone-beam algorithm for helical CT, i.e., using the F-mSTCT cone-beam BPF algorithm to approximately perform reconstruction for SHCT. The image quality is limited by the pitch size, which implies that satisfactory quality could only be obtained under the conditions of small pitches. To extend the analytical reconstruction for SHCT, an effective single-slice rebinning (SSRB) method for SHCT is investigated here. Transforming the SHCT cone-beam reconstruction into the virtual F-mSTCT fan-beam stack reconstruction task with low computational complexity, and then some techniques are developed to address the challenges involved. By using the basic BPF reconstruction with derivating along the detector (D-BPF), our experiments demonstrate that SSRB has fewer interlayer artifacts, higher z-resolution, more uniform in-plane resolution, and higher reconstruction efficiency compared to G-BPF. SSRB could promote the effective application of deep learning in SHCT reconstruction.

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.

Improved Cone-Beam Reconstruction Using a Prior Breathing Motion Model

<h3>Purpose/Objective(s)</h3> To demonstrate whether incorporating a pre-measured breathing motion model into algebraic cone-beam CT (CBCT) reconstruction could improve daily target imaging for radiotherapy delivery of lung tumors. <h3>Materials/Methods</h3> We employed our clinic's 5DCT simulation technique to generate the breathing motion model. 5DCT provides a voxel-by-voxel representation of breathing motion as a function of breathing amplitude and rate, and the motion model was used to characterize the breathing motion during CBCT acquisition. We acquired CBCT projections before treatment and simultaneously acquired a breathing amplitude signal using a respiratory bellows. The 5DCT reference image was registered to the CBCT geometry using a rigid registration of the spine. To enable the use of the 5DCT motion model on the CBCT data, a correspondence between the simulation and CBCT surrogates needed to be made. The breathing surrogates of the simulation were correlated with the CBCT using the diaphragm shadow in the CBCT projections and the 5DCT reference image deformed using the motion model. By adjusting the 5DCT model breathing amplitude, the diaphragm shadow moved until the CBCT projection and 5DCT diaphragm shadows overlapped, yielding the correspondence between the two surrogates such that the 5DCT motion model could be directly applied to the CBCT reconstruction. To reconstruct the CBCT image, we modified the motion-compensated simultaneous algebraic reconstruction technique (MC-SART) by introducing the aligned and scaled 5DCT motion model parameters. The modified MC-SART compared the raw projections to corresponding model-generated projections with various levels of binning. To quantify the 5DCT model's ability to describe the breathing motion during the CBCT, we reconstructed the CBCT dividing the breathing amplitude into 1, 2, 4 and 8 bins. We examined the corresponding sharpness of the diaphragm to see if the diaphragm boundary continuously sharpened with increasing bins. <h3>Results</h3> We found that the diaphragm appeared most blurred when only 1 amplitude bin was used, appearing sharper as bins were added. At 8 bins, the diaphragm boundary was only 2 voxels, consistent with typical breath-hold CT reconstruction. This demonstrated that the motion model from the CT simulation could quantitatively track breathing motion during the CBCT. However, while the diaphragm appeared sharper, the reconstructed images with increasing bins also had poorer image contrast. <h3>Conclusion</h3> We have shown that using a previously determined breathing motion model with algebraic reconstruction can improve image sharpness. We will expand the current study to more patients and fractions and develop techniques to improve image contrast. However, since the purpose of this approach will be to localize lung tumors and characterize their motion at the treatment table, the image contrast may be sufficient. We will also compare our approach to published 4DCT approaches to determine if ours better manages irregular breathing.

Angle prediction model when the imaging plane is tilted about z-axis.

Computer Tomography (CT) is a complicated imaging system, requiring highly geometric positioning. We found a special artifact caused by detection plane tilted around z-axis. In short scan cone-beam reconstruction, this kind of geometric deviation result in half circle shaped fuzzy around highlighted particles in reconstructed slices. This artifact is distinct near the slice periphery, but deficient around the slice center. We generated mathematical models, and InceptionV3-R deep network to study the slice artifact features to estimate the detector z-axis tilt angle. The testing results are: mean absolute error of 0.08819 degree, the Root mean square error of 0.15221 degree and R-square of 0.99944. A geometric deviation recover formula was deduced, which can eliminate this artifact efficiently. This research enlarges the CT artifact knowledge hierarchy, and verifies the capability of machine learning in CT geometric deviation artifact recoveries.

Sparse-view Cone-beam Breast CT Reconstruction via cGAN Constrained by Image Edges

Clinical applications of cone-beam breast CT(CBBCT) are hindered by relatively higher radiation dose and longer scan time. This study proposes sparse-view CBBCT, i.e. with a small number of projections, to overcome the above bottlenecks. A deep learning method - conditional generative adversarial network constrained by image edges (ECGAN) - is proposed to suppress artifacts on sparse-view CBBCT images reconstructed by filtered backprojection (FBP). The discriminator of the ECGAN is the combination of patchGAN and LSGAN for preserving high frequency information, with a modified U-net as the generator. To further preserve subtle structures and micro calcifications which are particularly important for breast cancer screening and diagnosis, edge images of CBBCT are added to both the generator and the discriminator to guide the learning. The proposed algorithm has been evaluated on 20 clinical raw datasets of CBBCT. ECGAN substantially improves the image qualities of sparse-view CBBCT, with a performance superior to those of total variation (TV) based iterative reconstruction and FBPConvNet based post-processing. On one CBBCT case with the projection number reduced from 300 to 100, ECGAN enhances peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) on FBP reconstruction from 24.26 and 0.812 to 37.78 and 0.963, respectively. These results indicate that ECGAN successfully reduces radiation dose and scan time of CBBCT by 1/3 with only small image degradations.

An In-House Cone-Beam Tomographic Reconstruction Package for Laboratory X-ray Phase-Contrast Imaging

Phase-contrast, and in general, multi-modal, X-ray micro-tomography is proven to be very useful for low-density, low-attention samples enabling much better contrast than its attenuation-based pendant. Therefore, it is increasingly applied in bio- and life sciences primarily dealing with such samples. Although there is a plethora of literature regarding phase-retrieval algorithms, access to implementations of those algorithms is relatively limited and very few packages combining phase-retrieval methods with the full tomographic reconstruction pipeline are available. This is especially the case for laboratory-based phase-contrast imaging typically featuring cone-beam geometry. We present here an in-house cone-beam tomographic reconstruction package for laboratory X-ray phase-contrast imaging. It covers different phase-contrast techniques and phase retrieval methods. The paper explains their implementation and integration in the filtered back projection chain. Their functionality and efficiency will be demonstrated through applications on a few dedicated samples.

A new weighting scheme for arc based circle cone-beam CT reconstruction.

In this paper, we present an arc based fan-beam computed tomography (CT) reconstruction algorithm by applying Katsevich's helical CT image reconstruction formula to 2D fan-beam CT scanning data. Specifically, we propose a new weighting function to deal with the redundant data. Our weighting function ϖ(x_,λ) is an average of two characteristic functions, where each characteristic function indicates whether the projection data of the scanning angle contributes to the intensity of the pixel x_. In fact, for every pixel x_, our method uses the projection data of two scanning angle intervals to reconstruct its intensity, where one interval contains the starting angle and another contains the end angle. Each interval corresponds to a characteristic function. By extending the fan-beam algorithm to the circle cone-beam geometry, we also obtain a new circle cone-beam CT reconstruction algorithm. To verify the effectiveness of our method, the simulated experiments are performed for 2D fan-beam geometry with straight line detectors and 3D circle cone-beam geometry with flat-plan detectors, where the simulated sinograms are generated by the open-source software "ASTRA toolbox." We compare our method with the other existing algorithms. Our experimental results show that our new method yields the lowest root-mean-square-error (RMSE) and the highest structural-similarity (SSIM) for both reconstructed 2D and 3D fan-beam CT images.

Principles for an Implementation of a Complete CT Reconstruction Tool Chain for Arbitrary Sized Data Sets and Its GPU Optimization.

This article describes the implementation of an efficient and fast in-house computed tomography (CT) reconstruction framework. The implementation principles of this cone-beam CT reconstruction tool chain are described here. The article mainly covers the core part of CT reconstruction, the filtered backprojection and its speed up on GPU hardware. Methods and implementations of tools for artifact reduction such as ring artifacts, beam hardening, algorithms for the center of rotation determination and tilted rotation axis correction are presented. The framework allows the reconstruction of CT images of arbitrary data size. Strategies on data splitting and GPU kernel optimization techniques applied for the backprojection process are illustrated by a few examples.

Role Of Cbct In The Treatment Of Orbital Floor Blow Out Fractures In Children

Background, Aim, and Objectives: Orbital floor blow out fractures are uncommon in children but can present with a dilemma in the Emergency department upon presentation. We collected case reports of 17 cases over a three-year period. The patients were selected from the age group six to 13 years of age with history of trivial blunt orbital trauma. The main complaint was mild pain in the eye upon presentation. Five patients were having clinical presentation of oculo-cardiac reflex. The suspected patients underwent Cone Beam CT of the midface with multiplanar cone beam reconstruction which confirmed the diagnosis of orbital floor fractures with trap door defect or minimal displacement and in a few cases inferior rectus entrapment. Orbital floor trapdoor fractures have oblivious features upon presentation and can easily be overlooked if not evaluated managed by expert healthcare providers which can lead to significant morbidity and even mortality in patients with oculocardiac reflex. Cone Beam CT of mid face with multiplanar reconstruction is the standard of care in the diagnosis and management of white eyed blow out orbital floor fractures in the provision of evidenced based healthcare practice. Methodology: This is a retrospective cohort study to evaluate the results of pediatric age group with trapdoor and blow out orbital floor fractures who underwent CBCT for the diagnosis and further management. Seventeen cases were selected who were in the age group between 6 to 13 years.12 cases underwent surgery for orbital floor exploration and nine were having inferior rectus muscle entrapment which was released. Five patients were managed non surgically. Result: One patient disappeared in this group during one-year post-operative follow up. No residual defect was found in the remaining sixteen patients. Cone beam Computer tomography with multiplanar reconstruction should be the standard of care for the diagnosis and treatment of blow out and trapdoor orbital fractures. Strength and limitations: Although this study is of a limited number of pediatric patients, but it highlights the significance of CBCT in the management of trapdoor and blow out orbital floor fractures in children. Further studies are needed to elaborate the utilization of CBCT in the treatment of orbital floor and medial orbital wall fractures. Conclusion: Our study suggests that CBCT has a higher value of specificity and less radiation exposure in the diagnosis of orbital fractures in pediatric age group when there is isolated orbital or mid face trauma, and CT brain is not recommended. Cone Beam CT with multiplanar reconstruction is considered the standard of care in the diagnosis of white eyed blow out orbital floor fractures in the provision of evidenced based healthcare practice. Perioperative CBCT and navigation should be universalized to achieve the best outcome.

Fourier method for 3-dimensional data fusion of X-ray Computed Tomography and ultrasound

X-ray Computed Tomography (CT) is essential for nondestructive inspection of many manufactured components but is susceptible to various forms of artifacts and noise. In particular, multi-detector row cone-beam CT systems can suffer from so called “cone-beam” artifacts and partial volume effects, particularly on planar edges at the periphery of the field of view in the cone angle dimension. Uni-directional ultrasonic testing methods generally have extremely accurate in-plane depth resolution but poorer lateral resolution due to physical and geometric constraints. This paper presents a novel technique called Computed Tomographic Fusion (CT-F) which uses three-dimensional Fourier filtering to combine x-ray cone-beam CT reconstructions and ultrasound data in the frequency space. The result is a single image with improved accuracy and quality. CT-F mitigates artifacts while allowing for rapid, accurate characterization of large three-dimensional CT volumes. Using simulations and experiments, we demonstrate artifact reduction and edge contrast improvement in volumetric reconstructions.

Hybrid regularized cone-beam reconstruction for axially symmetric object tomography

Hybrid regularized cone-beam reconstruction for axially symmetric object tomography

Fast and effective single-scan dual-energy cone-beam CT reconstruction and decomposition denoising based on dual-energy vectorization.

Flat-panel detector (FPD) based dual-energy cone-beam computed tomography (DE-CBCT) is a promising imaging technique for dedicated clinical applications. In this paper, we proposed a fully analytical method for fast and effective single-scan DE-CBCT image reconstruction and decomposition. A rotatable Mo filter was inserted between an x-ray source and imaged object to alternately produce low and high-energy x-ray spectra. First, filtered-backprojection (FBP) method was applied on down-sampled projections to reconstruct low and high-energy images. Then, the two images were converted into a vectorized form represented with an amplitude and an argument image. Using amplitude image as a guide, a joint bilateral filter was applied to denoise the argument image. Then, high-quality dual-energy images were recovered from the amplitude image and the denoised argument image. Finally, the recovered dual-energy images were further used for low-noise material decomposition and electron density synthesis. Imaging was conducted on a Catphan® 600 phantom and an anthropomorphic head phantom. The proposed method was evaluated via comparison with the traditional two-scan method and a commonly used filtering method (HYPR-LR). On the Catphan® 600 phantom, the proposed method successfully reduced streaking artifacts and preserved spatial resolution and noise-power-spectrum (NPS) pattern. In the electron density image, the proposed method increased contrast-to-noise ratio (CNR) by more than 2.5 times and achieved <1.2% error for electron density values. On the anthropomorphic head phantom, the proposed method greatly improved the soft-tissue contrast and the fine detail differentiation ability. In the selected ROIs on different human tissues, the differences between the CT number obtained by the proposed method and that by the two-scan method were less than 4HU. In the material images, the proposed method suppressed noise by over 75.5% compared with two-scan results, and by over 40.4% compared with HYPR-LR results. Implementation of the whole algorithm took 44.5s for volumetric imaging, including projection preprocessing, FBP reconstruction, joint bilateral filtering, and material decomposition. Using down-sampled projections in single-scan DE-CBCT, the proposed method could effectively and efficiently produce high-quality DE-CBCT images and low-noise material decomposition images. This method demonstrated superior performance on spatial resolution enhancement, NPS preservation, noise reduction, and electron density accuracy, indicating better prospect in material differentiation and dose calculation.

The adaptation and investigation of cone-beam CT reconstruction algorithms for horizontal rotation fixed-gantry scans of rabbits

Fixed-gantry radiation therapy has been proposed as a low-cost alternative to the conventional rotating-gantry radiation therapy, that may help meet the rising global treatment demand. Fixed-gantry systems require gravitational motion compensated reconstruction algorithms to produce cone-beam CT (CBCT) images of sufficient quality for image guidance. The aim of this work was to adapt and investigate five CBCT reconstruction algorithms for fixed-gantry CBCT images. The five algorithms investigated were Feldkamp–Davis–Kress (FDK), prior image constrained compressed sensing (PICCS), gravitational motion compensated FDK (GMCFDK), motion compensated PICCS (MCPICCS) (a novel CBCT reconstruction algorithm) and simultaneous motion estimation and iterative reconstruction (SMEIR). Fixed-gantry and rotating-gantry CBCT scans were acquired of 3 rabbits, with the rotating-gantry scans used as a reference. Projections were sorted into rotation bins, based on the angle of rotation of the rabbit during image acquisition. The algorithms were compared using the structural similarity index measure root mean square error, and reconstruction time. Evaluation of the reconstructed volumes showed that, when compared with the reference rotating-gantry volume, the conventional FDK algorithm did not accurately reconstruct fixed-gantry CBCT scans. Whilst the PICCS reconstruction algorithm reduced some motion artefacts, the motion estimation reconstruction methods (GMCFDK, MCPICCS and SMEIR) were able to greatly reduce the effect of motion artefacts on the reconstructed volumes. This finding was verified quantitatively, with GMCFDK, MCPICCS and SMEIR reconstructions having RMSE 17%–19% lower and SSIM 1% higher than a conventional FDK. However, all motion compensated fixed-gantry CBCT reconstructions had a 56%–61% higher RMSE and 1.5% lower SSIM than FDK reconstructions of conventional rotating-gantry CBCT scans. The results show that motion compensation is required to reduce motion artefacts for fixed-gantry CBCT reconstructions. This paper further demonstrates the feasibility of fixed-gantry CBCT scans, and the ability of CBCT reconstruction algorithms to compensate for motion due to horizontal rotation.

Four-dimensional cone-beam CT reconstruction based on motion-compensated robust principal component analysis

To propose a motion compensation reconstruction method based on robust principal component analysis (RPCA) to reduce the influence of streak artifacts on accurate estimation of interphase motion deformation fields. We propose a RPCA motion compensation reconstruction algorithm to improve the estimation of motion deformation fields based on the traditional MC-FDK algorithm. RPCA was used to decompose the cone-beam computed tomography (CBCT) images into low-rank and sparse components, and the motion deformation fields between different phase images were then estimated using Horn and Schunck optical flow method from the low-rank images to reduce the influence of striping artifacts on the accuracy of estimation of interphase motion deformation fields. The performance of the algorithm was evaluated using simulation data and real data. The simulation phantom data was obtained by back-projection of 4D-CT images acquired from Philips 16-slice spiral CT using MATLAB software programming according to the scanning geometry of Varian Edge accelerator. The real patient data were obtained using the Elekta Synergy system of CBCT scanning system with half-fan mode CB projection data from lung cancer patients. Compared with images reconstructed using the traditional MC-FDK algorithm, the reconstructed image using the proposed method had clearer tissue boundaries with reduced motion artifact was reduced. The results of phantom data reconstruction showed that compared with the MC- FDK algorithm, the proposed algorithms resulted in improvements of PSNR by 25.4% and SSIM by 7.6%; compared with the FDK algorithm, PSNR was improved by 37.9% and SSIM by 17.6%. The proposed algorithm can achieve accurate estimation of inter-phase motion deformation fields and improve the quality of the reconstructed CBCT images.