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Accurate and automatic segmentation of three-dimensional (3D) individual teeth from cone-beam computerized tomography (CBCT) images is a challenging problem because of the difficulty in separating an individual tooth from adjacent teeth and its surrounding alveolar bone. Thus, this paper proposes a fully automated method of identifying and segmenting 3D individual teeth from dental CBCT images. The proposed method addresses the aforementioned difficulty by developing a deep learning-based hierarchical multi-step model. First, it automatically generates upper and lower jaws panoramic images to overcome the computational complexity caused by high-dimensional data and the curse of dimensionality associated with limited training dataset. The obtained 2D panoramic images are then used to identify 2D individual teeth and capture loose- and tight- regions of interest (ROIs) of 3D individual teeth. Finally, accurate 3D individual tooth segmentation is achieved using both loose and tight ROIs. Experimental results showed that the proposed method achieved an F1-score of 93.35 percent for tooth identification and a Dice similarity coefficient of 94.79 percent for individual 3D tooth segmentation. The results demonstrate that the proposed method provides an effective clinical and practical framework for digital dentistry. [ABSTRACT FROM AUTHOR]

Four-dimensional cone-beam computed tomography (4D CBCT) has been developed to provide a sequence of phase-resolved reconstructions in image-guided radiation therapy. However, 4D CBCT images are degraded by severe streaking artifacts and noise because the phase-resolved image is an extremely sparse-view CT procedure wherein a few under-sampled projections are used for the reconstruction of each phase. Aiming at improving the overall quality of 4D CBCT images, we proposed two CNN models, named N-Net and CycN-Net, respectively, by fully excavating the inherent property of 4D CBCT. To be specific, the proposed N-Net incorporates the prior image reconstructed from entire projection data based on U-Net to boost the image quality for each phase-resolved image. Based on N-Net, a temporal correlation among the phase-resolved images is also considered by the proposed CycN-Net. Extensive experiments on both XCAT simulation data and real patient 4D CBCT datasets were carried out to verify the feasibility of the proposed CNNs. Both networks can effectively suppress streaking artifacts and noise while restoring the distinct features simultaneously, compared with the existing CNN models and two state-of-the-art iterative algorithms. Moreover, the proposed method is robust in handling complicated tasks of various patient datasets and imaging devices, which implies its excellent generalization ability. [ABSTRACT FROM AUTHOR]

Data truncation is a common problem in computed tomography (CT). Truncation causes cupping artifacts inside the field-of-view (FOV) and anatomical structures missing outside the FOV. Deep learning has achieved impressive results in CT reconstruction from limited data. However, its robustness is still a concern for clinical applications. Although the image quality of learning-based compensation schemes may be inadequate for clinical diagnosis, they can provide prior information for more accurate extrapolation than conventional heuristic extrapolation methods. With extrapolated projection, a conventional image reconstruction algorithm can be applied to obtain a final reconstruction. In this work, a general plug-and-play (PnP) method for truncation correction is proposed based on this idea, where various deep learning methods and conventional reconstruction algorithms can be plugged in. Such a PnP method integrates data consistency for measured data and learned prior image information for truncated data. This shows to have better robustness and interpretability than deep learning only. To demonstrate the efficacy of the proposed PnP method, two state-of-the-art deep learning methods, FBPConvNet and Pix2pixGAN, are investigated for truncation correction in cone-beam CT in noise-free and noisy cases. Their robustness is evaluated by showing false negative and false positive lesion cases. With our proposed PnP method, false lesion structures are corrected for both deep learning methods. For FBPConvNet, the root-mean-square error (RMSE) inside the FOV can be improved from 92HU to around 30HU by PnP in the noisy case. Pix2pixGAN solely achieves better image quality than FBPConvNet solely for truncation correction in general. PnP further improves the RMSE inside the FOV from 42HU to around 27HU for Pix2pixGAN. The efficacy of PnP is also demonstrated on real clinical head data. [ABSTRACT FROM AUTHOR]

CT scan by use of a beam-filter placed between the x-ray source and the patient allows a single-scan low-dose dual-energy imaging with a minimal hardware modification to the existing CT systems. We have earlier demonstrated the feasibility of such imaging method with a multi-slit beam-filter reciprocating along the direction perpendicular to the CT rotation axis in a cone-beam CT system. However, such method would face mechanical challenges when the beam-filter is supposed to cooperate with a fast-rotating gantry in a diagnostic CT system. In this work, we propose a new scanning method and associated image reconstruction algorithm that can overcome these challenges. We propose to slide a beam-filter that has multi-slit structure with its slits being at a slanted angle with the CT gantry rotation axis during a scan. A streaky pattern would show up in the sinogram domain as a result. Using a notch filter in the Fourier domain of the sinogram, we removed the streaks and reconstructed an image by use of the filtered-backprojection algorithm. The remaining image artifacts were suppressed by applying ${l}_{{0}}$ norm based smoothing. Using this image as a prior, we have reconstructed low- and high-energy CT images in the iterative reconstruction framework. An image-based material decomposition then followed. We conducted a simulation study to test its feasibility using the XCAT phantom and also an experimental study using the Catphan phantom, a head phantom, an iodine-solution phantom, and a monkey in anesthesia, and showed its successful performance in image reconstruction and in material decomposition. [ABSTRACT FROM AUTHOR]

The recent application of Fourier Based Iterative Reconstruction Method (FIRM) has made it possible to achieve high-quality 2D images from a fan beam Computed Tomography (CT) scan with a limited number of projections in a fast manner. The proposed methodology in this article is designed to provide 3D Radon space in linogram fashion to facilitate the use of FIRM with cone beam projections (CBP) for the reconstruction of 3D images in a sparse view angles Cone Beam CT (CBCT). For this reason, in the first phase, the 3D Radon space is generated using CBP data after discretization and optimization of the famous Grangeat’s formula. The method used in this process involves fast Pseudo Polar Fourier transform (PPFT) based on 2D and 3D Discrete Radon Transformation (DRT) algorithms with no wraparound effects. In the second phase, we describe reconstruction of the objects with available Radon values, using direct inverse of 3D PPFT. The method presented in this section eliminates noises caused by interpolation from polar to Cartesian space and exhibits no thorn, V-shaped and wrinkle artifacts. This method reduces the complexity to O(n3 log n) for images of size $ {{\mathrm {n}}\times {\mathrm {n}} \times {\mathrm {n}}}$. The Cone to Radon conversion (Cone2Radon) Toolbox in the first phase and MATLAB/Python toolbox in the second phase were tested on three digital phantoms and experiments demonstrate fast and accurate cone beam image reconstruction due to proposed modifications in all three stages of Grangeat’s method. [ABSTRACT FROM AUTHOR]

Temporal resolution in time-resolved cone-beam CT (TR-CBCT) imaging is often limited by the time needed to acquire a complete data set for image reconstruction. With the recent developments of performing nearly limited-view artifact-free reconstruction from data in a limited-view angle range and a prior image, temporal resolution of TR-CBCT imaging can be improved. One such an example is the use of Simultaneous Multiple Artifacts Reduction in Tomographic RECONstruction (SMART-RECON) technique. However, with SMART-RECON, one can only improve temporal resolution up to 1 frame per second (fps) which is an improvement of 4.5 times over that of the conventional FBP reconstruction. In this paper, a new technique referred to as enhanced SMART-RECON (eSMART-RECON) was introduced to enhance the temporal performance of SMART-RECON in a multi-sweep CBCT data acquisition protocol. Both numerical simulation studies with ground truth and in vivo human subject studies using C-arm CBCT acquisition systems were conducted to demonstrate the following key results: for a multi-sweep CBCT acquisition protocol, eSMART-RECON enables 4–7.5 fps temporal resolution for TR-CBCT which is 4–7.5 times better than that offered by the original SMART-RECON, and 18–34 times better than that offered by the conventional FBP reconstruction. [ABSTRACT FROM AUTHOR]

In this paper, the accuracy of material decomposition (MD) using an energy discriminating photon counting detector was studied. An MD framework was established and validated using calcium hydroxyapatite (CaHA) inserts of known densities (50 mg/cm3, 100 mg/cm3, 250 mg/cm3, 400 mg/cm3), and diameters (1.2, 3.0, and 5.0 mm). These inserts were placed in a cardiac rod phantom that mimics a tissue equivalent heart and measured using an experimental photon counting detector cone beam computed tomography (PCD-CBCT) setup. The quantitative coronary calcium scores (density, mass, and volume) obtained from the MD framework were compared with the nominal values. In addition, three different calibration techniques, signal-to-equivalent thickness calibration (STC), polynomial correction (PC), and projected equivalent thickness calibration (PETC) were compared to investigate the effect of the calibration method on the quantitative values. The obtained MD estimates agreed well with the nominal values for density (mass) with mean absolute percent errors (MAPEs) 8 ± 11% (9 ± 15%) and 4 ± 6% (9 ± 14%) for STC and PETC calibration methods, respectively. PC displayed large MAPEs for density (27 ± 9%), and mass (25 ± 12%). Volume estimation resulted in large deviations between true and measured values with notable MAPEs for STC (40 ± 90%), PC (40 ± 80%), and PETC (40 ± 90%). The framework demonstrated the feasibility of quantitative CaHA mass and density scoring using PCD-CBCT. [ABSTRACT FROM AUTHOR]

Edges tend to be over-smoothed in total variation (TV) regularized under-sampled images. In this paper, symmetric residual convolutional neural network (SR-CNN), a deep learning based model, was proposed to enhance the sharpness of edges and detailed anatomical structures in under-sampled cone-beam computed tomography (CBCT). For training, CBCT images were reconstructed using TV-based method from limited projections simulated from the ground truth CT, and were fed into SR-CNN, which was trained to learn a restoring pattern from under-sampled images to the ground truth. For testing, under-sampled CBCT was reconstructed using TV regularization and was then augmented by SR-CNN. Performance of SR-CNN was evaluated using phantom and patient images of various disease sites acquired at different institutions both qualitatively and quantitatively using structure similarity (SSIM) and peak signal-to-noise ratio (PSNR). SR-CNN substantially enhanced image details in the TV-based CBCT across all experiments. In the patient study using real projections, SR-CNN augmented CBCT images reconstructed from as low as 120 half-fan projections to image quality comparable to the reference fully-sampled FDK reconstruction using 900 projections. In the tumor localization study, improvements in the tumor localization accuracy were made by the SR-CNN augmented images compared with the conventional FDK and TV-based images. SR-CNN demonstrated robustness against noise levels and projection number reductions and generalization for various disease sites and datasets from different institutions. Overall, the SR-CNN-based image augmentation technique was efficient and effective in considerably enhancing edges and anatomical structures in under-sampled 3D/4D-CBCT, which can be very valuable for image-guided radiotherapy. [ABSTRACT FROM AUTHOR]

Identification and treatment of obstructive airway disorders (OADs) are greatly aided by imaging of the geometry of the airway lumen. Anatomical optical coherence tomography (aOCT) is a promising high-speed and minimally invasive endoscopic imaging modality for providing micrometer-resolution scans of the upper airway. Resistance to airflow in OADs is directly caused by the reduction in luminal cross-sectional area (CSA). It is hypothesized that aOCT can produce airway CSA measurements as accurate as that from computed tomography (CT). Scans of machine hollowed cylindrical tubes were used to develop methods for segmentation and measurement of airway lumen in CT and aOCT. Simulated scans of virtual cones were used to validate 3-D resampling and reconstruction methods in aOCT. Then, measurements of two segments of a 3-D printed pediatric airway phantom from aOCT and CT independently were compared to ground truth CSA. In continuous unobstructed regions, the mean CSA difference for each phantom segment was 2.2 ± 3.5 and 1.5 ± 5.3 mm2 for aOCT, and −3.4 ± 4.3 and −1.9 ± 1.2 mm2 for CT. Because of the similar magnitude of these differences, these results support the hypotheses and underscore the potential for aOCT as a viable alternative to CT in airway imaging, while offering greater potential to capture respiratory dynamics. [ABSTRACT FROM AUTHOR]

In this paper, we consider dense volumetric modeling of moving samples such as body parts. Most dense modeling methods consider samples observed with a moving X-ray device and cannot easily handle moving samples. We propose instead a novel method to observe shape motion from a fixed X-ray device and to build dense in-depth attenuation information. This yields a low-cost, low-dose 3-D imaging solution, taking benefit of equipment widely available in clinical environments. Our first innovation is to combine a video-based surface motion capture system with a single low-cost/low-dose fixed planar X-ray device, in order to retrieve the sample motion and attenuation information with minimal radiation exposure. Our second innovation is to rely on Bayesian inference to solve for a dense attenuation volume given planar radioscopic images of a moving sample. This approach enables multiple sources of noise to be considered and takes advantage of very limited prior information to solve an otherwise ill-posed problem. Results show that the proposed strategy is able to reconstruct dense volumetric attenuation models from a very limited number of radiographic views over time on synthetic and in-situ data. [ABSTRACT FROM AUTHOR]

The polychromatic X-ray spectrum and the energy-dependent attenuation coefficient of materials cause beam hardening artifacts in CT reconstructed volumes. These artifacts appear as cupping and streak artifacts depending on the material composition and the geometry of the imaged object. CT scanners employ projection linearization to transform polychromatic attenuation to monochromatic attenuation using a polynomial model. Polynomial coefficients are computed during calibration or using prior information such as X-ray spectrum and attenuation properties of the materials. In this paper, we are presenting a novel method to correct beam hardening artifacts by enforcing cone beam consistency conditions on the projection data. We used consistency conditions derived from Grangeat’s fundamental relation between cone beam projection data and 3-D Radon transform. The optimal polynomial coefficients for artifact reduction are iteratively estimated by minimizing the inconsistency of a set of projection pairs. The results from simulated and real datasets show the visible reduction of artifacts. Our studies also demonstrate the robustness of the algorithm when the projections are perturbed with other physical measurement and geometrical errors. The proposed method requires neither calibration nor prior information like X-ray spectrum, attenuation properties of the materials and detector response. The algorithm can be used for beam hardening correction in clinical, pre-clinical, and industrial CT systems. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this work is to introduce and study a novel imaging geometry for X-ray luminescence computed tomography (XLCT), termed coded aperture compressive X-ray luminescence tomography (CAC-XLCT). Methods: CAC-XLCT is studied through simulations of X-ray and diffuse light propagation and the implementation of a compressed sensing image reconstruction algorithm. Results: CAC-XLCT is compared against cone beam XLCT considering simulated targets with varying complexity, and it is found to offer a remarkable enhancement in spatial resolution and image quality with only a small overhead in image acquisition time. Conclusions and Significance: XLCT has been mainly investigated so far in pencil beam and cone beam excitation geometries which suffer from either very long image acquisition time or low spatial resolution and accuracy. CAC-XLCT presents a very promising alternative, which can offer simultaneously high spatial resolution, high image quality, and fast image acquisition, appropriate for in vivo imaging. [ABSTRACT FROM AUTHOR]

In this paper, we present a new deep learning framework for 3-D tomographic reconstruction. To this end, we map filtered back-projection-type algorithms to neural networks. However, the back-projection cannot be implemented as a fully connected layer due to its memory requirements. To overcome this problem, we propose a new type of cone-beam back-projection layer, efficiently calculating the forward pass. We derive this layer’s backward pass as a projection operation. Unlike most deep learning approaches for reconstruction, our new layer permits joint optimization of correction steps in volume and projection domain. Evaluation is performed numerically on a public data set in a limited angle setting showing a consistent improvement over analytical algorithms while keeping the same computational test-time complexity by design. In the region of interest, the peak signal-to-noise ratio has increased by 23%. In addition, we show that the learned algorithm can be interpreted using known concepts from cone beam reconstruction: the network is able to automatically learn strategies such as compensation weights and apodization windows. [ABSTRACT FROM AUTHOR]

Cone-beam computed tomography (CBCT) plays an important role in radiation therapy. Statistical iterative reconstruction (SIR) algorithms with specially designed penalty terms provide good performance for low-dose CBCT imaging. Among others, the total variation (TV) penalty is the current state-of-the-art in removing noises and preserving edges, but one of its well-known limitations is its staircase effect. Recently, various penalty terms with higher order differential operators were proposed to replace the TV penalty to avoid the staircase effect, at the cost of slightly blurring object edges. We developed a novel SIR algorithm using a neural network for CBCT reconstruction. We used a data-driven method to learn the “potential regularization term” rather than design a penalty term manually. This approach converts the problem of designing a penalty term in the traditional statistical iterative framework to designing and training a suitable neural network for CBCT reconstruction. We proposed using transfer learning to overcome the data deficiency problem and an iterative deblurring approach specially designed for the CBCT iterative reconstruction process during which the noise level and resolution of the reconstructed images may change. Through experiments conducted on two physical phantoms, two simulation digital phantoms, and patient data, we demonstrated the excellent performance of the proposed network-based SIR for CBCT reconstruction, both visually and quantitatively. Our proposed method can overcome the staircase effect, preserve both edges and regions with smooth intensity transition, and provide reconstruction results at high resolution and low noise level. [ABSTRACT FROM AUTHOR]

A single-scan dual-energy low-dose cone-beam CT (CBCT) imaging technique that exploits a multi-slit filter is proposed in this paper. The multi-slit filter installed between the x-ray source and the scanned object is reciprocated during a scan. The x-ray beams through the slits would generate relatively low-energy x-ray projection data, while the filtered beams would make high-energy projection data. An iterative image reconstruction algorithm that uses an adaptive-steepest-descent method to minimize image total-variation under the constraint of data fidelity was applied to reconstructing the image from the low-energy projection data. Since the high-energy projection data suffer from a substantially high noise level due to the beam filtration, we have developed a new algorithm that exploits the joint sparsity between the low- and high-energy CT images for image reconstruction of the high-energy CT image. The proposed image reconstruction algorithm uses a gradient magnitude image (GMI) of the low-energy CT image by regularizing the difference of GMIs of the low- and high-energy CT images to be minimized. The feasibility of the proposed technique has been demonstrated by the use of various phantoms in the experimental CBCT setup. Furthermore, based on the proposed dual-energy imaging, a material differentiation was performed and its potential utility has been shown. The proposed imaging technique produced promising results for its potential application to a low-dose single-scan dual-energy CBCT. [ABSTRACT FROM PUBLISHER]

Cone-beam computed tomography (CBCT) has been widely used in radiation therapy. For accurate patient setup and treatment target localization, it is important to obtain high-quality reconstruction images. The total variation (TV) penalty has shown the state-of-the-art performance in suppressing noise and preserving edges for statistical iterative image reconstruction, but it sometimes leads to the so-called staircase effect. In this paper, we proposed to use a new family of penalties—the Hessian Schatten (HS) penalties—for the CBCT reconstruction. Consisting of the second-order derivatives, the HS penalties are able to reflect the smooth intensity transitions of the underlying image without introducing the staircase effect. We discussed and compared the behaviors of several convex HS penalties with orders 1, 2, and $+\infty $ for CBCT reconstruction. We used the majorization-minimization approach with a primal-dual formulation for the corresponding optimization problem. Experiments on two digital phantoms and two physical phantoms demonstrated the proposed penalty family’s outstanding performance over TV in suppressing the staircase effect, and the HS penalty with order 1 had the best performance among the HS penalties tested. [ABSTRACT FROM PUBLISHER]

Accurate sorting of beam projections is important in 4D cone beam computed tomography (4D CBCT) to improve the quality of the reconstructed 4D CBCT image by removing motion-induced artifacts. We propose image registration-based projection binning (IRPB), a novel marker-less binning method for 4D CBCT projections, which combines intensity-based feature point detection and trajectory tracking using random sample consensus. IRPB extracts breathing motion and phases by analyzing tissue feature point trajectories. We conducted experiments with two phantom and six patient datasets, including both regular and irregular respirations. In experiments, we compared the performance of the proposed IRPB, Amsterdam Shroud method (AS), Fourier transform-based method (FT), and local intensity feature tracking method (LIFT). The results showed that the average absolute phase shift of IRPB was 3.74 projections and 0.48 projections less than that of FT and LIFT, respectively. AS lost the most breathing cycles in the respiration extraction for the five patient datasets, so we could not compare the average absolute phase shift between IRPB and AS. Based on the peak signal-to-noise ratio (PSNR) of the reconstructed 4D CBCT images, IRPB had 5.08, 1.05, and 2.90 dB larger PSNR than AS, FT, and LIFT, respectively. The average Structure SIMilarity Index (SSIM) of the 4D CBCT image reconstructed by IRPB, AS, and LIFT were 0.87, 0.74, 0.84, and 0.70, respectively. These results demonstrated that IRPB has superior performance to the other standard methods. [ABSTRACT FROM PUBLISHER]

Two-dimensional-to-three-dimensional (2D-3D) deformation has emerged as a new technique to estimate cone-beam computed tomography (CBCT) images. The technique is based on deforming a prior high-quality 3D CT/CBCT image to form a new CBCT image, guided by limited-view 2D projections. The accuracy of this intensity-based technique, however, is often limited in low-contrast image regions with subtle intensity differences. The solved deformation vector fields (DVFs) can also be biomechanically unrealistic. To address these problems, we have developed a biomechanical modeling guided CBCT estimation technique (Bio-CBCT-est) by combining 2D-3D deformation with finite element analysis (FEA)-based biomechanical modeling of anatomical structures. Specifically, Bio-CBCT-est first extracts the 2D-3D deformation-generated displacement vectors at the high-contrast anatomical structure boundaries. The extracted surface deformation fields are subsequently used as the boundary conditions to drive structure-based FEA to correct and fine-tune the overall deformation fields, especially those at low-contrast regions within the structure. The resulting FEA-corrected deformation fields are then fed back into 2D-3D deformation to form an iterative loop, combining the benefits of intensity-based deformation and biomechanical modeling for CBCT estimation. Using eleven lung cancer patient cases, the accuracy of the Bio-CBCT-est technique has been compared to that of the 2D-3D deformation technique and the traditional CBCT reconstruction techniques. The accuracy was evaluated in the image domain, and also in the DVF domain through clinician-tracked lung landmarks. [ABSTRACT FROM PUBLISHER]

X-ray luminescence computed tomography (XLCT), which aims to achieve molecular and functional imaging by X-rays, has recently been proposed as a new imaging modality. Combining the principles of X-ray excitation of luminescence-based probes and optical signal detection, XLCT naturally fuses functional and anatomical images and provides complementary information for a wide range of applications in biomedical research. In order to improve the data acquisition efficiency of previously developed narrow-beam XLCT, a cone beam XLCT (CB-XLCT) mode is adopted here to take advantage of the useful geometric features of cone beam excitation. Practically, a major hurdle in using cone beam X-ray for XLCT is that the inverse problem here is seriously ill-conditioned, hindering us to achieve good image quality. In this paper, we propose a novel Bayesian method to tackle the bottleneck in CB-XLCT reconstruction. The method utilizes a local regularization strategy based on Gaussian Markov random field to mitigate the ill-conditioness of CB-XLCT. An alternating optimization scheme is then used to automatically calculate all the unknown hyperparameters while an iterative coordinate descent algorithm is adopted to reconstruct the image with a voxel-based closed-form solution. Results of numerical simulations and mouse experiments show that the self-adaptive Bayesian method significantly improves the CB-XLCT image quality as compared with conventional methods. [ABSTRACT FROM PUBLISHER]

We propose a new method to measure directional modulation transfer function (MTF) using sphere phantoms in a cone beam computed tomography (CBCT) system. To measure spatially varying 3-D MTFs, we model FDK reconstruction in local regions and calculate the plane integrals of an ideal sphere phantom and reconstructed sphere phantoms. Then, we modify the Richardson-Lucy (RL) deconvolution method to relax the non-negativity constraint in RL deconvolution and apply it to estimate the directional plane spread functions (PlSFs). Directional MTFs are calculated by taking the modulus of the Fourier transform of the estimated directional PlSFs. To validate the proposed method, we simulate ideal 3-D MTFs and compare them with directional MTFs measured by simulation and experimental data along three major axes. For quantitative evaluation, we compare full-width at half-maximum (FWHM) and full-width at tenth-maximum (FWTM) of measured and ideal directional MTFs. The measured directional MTFs from simulation and experimental data show excellent agreement with the ideal directional MTFs, demonstrating the effectiveness of the proposed method to estimate directional MTFs in a CBCT system. [ABSTRACT FROM PUBLISHER]

Respiration-correlated CBCT, commonly called 4DCBCT, provides respiratory phase-resolved CBCT images. A typical 4DCBCT represents averaged patient images over one breathing cycle and the fourth dimension is actually breathing phase instead of time. In many clinical applications, it is desirable to obtain true 4DCBCT with the fourth dimension being time, i.e., each constituent CBCT image corresponds to an instantaneous projection. Theoretically it is impossible to reconstruct a CBCT image from a single projection. However, if all the constituent CBCT images of a 4DCBCT scan share a lot of redundant information, it might be possible to make a good reconstruction of these images by exploring their sparsity and coherence/redundancy. Though these CBCT images are not completely time resolved, they can exploit both local and global temporal coherence of the patient anatomy automatically and contain much more temporal variation information of the patient geometry than the conventional 4DCBCT. We propose in this work a computational model and algorithms for the reconstruction of this type of semi-time-resolved CBCT, called cine-CBCT, based on low rank approximation that can utilize the underlying temporal coherence both locally and globally, such as slow variation, periodicity or repetition, in those cine-CBCT images. [ABSTRACT FROM AUTHOR]

We present a new method for reconstruction of 4-D cone beam computed tomography from an undersampled set of X-ray projections. The novelty of the proposed method lies in utilizing optical flow based registration to facilitate that each temporal phase is reconstructed from the full set of acquired projections. The reconstruction of each phase thus exhibits limited aliasing despite significant intra-phase undersampling. The method is fully self-contained. Initially an approximate 4-D volume is reconstructed and an inter-phase registration based hereon. A subsequent reconstruction pass integrates the optical flow estimation in a cost function formulation in which the X-ray projections from all temporal phases are considered for the reconstruction of each individual phase. Quantitative and qualitative evaluations were performed through reconstruction of both a numerical phantom and a clinical dataset. The obtained reconstructions are compared to the state-of-the-art alternatives of total variation regularization and prior image constrained compressed sensing. Our studies show that the proposed method is the better overall “compromise” in the depiction of both moving and stationary anatomical structures. [ABSTRACT FROM AUTHOR]

This work is a continuation of our previous work on geometric calibration in the circular cone-beam geometry. It is well known that seven parameters completely describe such a geometry in either flat-panel X-ray computed tomography or single pinhole SPECT imaging. Previously we developed a graphical procedure to determine the detector in-plane rotation angle independently of the other six parameters. Using the discovered geometrical relationships, in this paper we determine the remaining six parameters using the cone-beam projections of a minimum of three point objects. Our method is analytical. It makes use of the parameters of the fitted ellipse from the calibration data. The parameter estimation is accurate in the noise-free case or when there is moderate projection data truncation or shorter calibration scan range (\leq 360^\circ). We perform numerical evaluations to study the robustness of the proposed method under different projection noise levels and using different data acquisition ranges. Using a full 360^\circ scan range, the estimation accuracy and precision of our method are comparable or superior to previous methods. Using a shorter acquisition range, there may be bias in the ellipse parameters obtained by simple algebraic fitting methods. This bias will propagate to the estimated geometric parameters. Such bias can be mostly eliminated by using a more sophisticated fitting algorithm. At the same noise level, the geometric parameter estimation accuracies are comparable, but the estimation precision degrades, as the acquisition range becomes shorter. [ABSTRACT FROM PUBLISHER]

This paper presents a novel method for respiratory motion compensated reconstruction for cone beam computed tomography (CBCT). The reconstruction is based on a time sequence of motion vector fields, which is generated by a dynamic geometrical object shape model. The dynamic model is extracted from the 2D projection images of the CBCT. The process of the motion extraction is converted into an optimal 3D multiple interrelated surface detection problem, which can be solved by computing a maximum flow in a 4D directed graph. The method was tested on 12 mega-voltage (MV) CBCT scans from three patients. Two sets of motion-artifact-free 3D volumes, full exhale (FE) and full inhale (FI) phases, were reconstructed for each daily scan. The reconstruction was compared with three other motion-compensated approaches based on quantification accuracy of motion and size. Contrast-to-noise ratio (CNR) was also quantified for image quality. The proposed approach has the best overall performance, with a relative tumor volume quantification error of 3.39 \pm 3.64% and 8.57 \pm 8.31% for FE and FI phases, respectively. The CNR near the tumor area is 3.85 \pm 0.42 (FE) and 3.58 \pm 3.33 (FI). These results show the clinical feasibility to use the proposed method to reconstruct motion-artifact-free MVCBCT volumes. [ABSTRACT FROM AUTHOR]

Recently, compressive sampling has received significant attention as an emerging technique for rapid volumetric imaging. We have previously investigated volumetric optical coherence tomography (OCT) image acquisition using compressive sampling techniques and showed that it was possible to recover image volumes from a subset of sampled images. Our previous findings used the multidimensional wavelet transform as the domain of sparsification for recovering OCT image volumes. In this report, we analyzed and compared the potential and efficiency of three other image transforms to reconstruct the same volumetric OCT image. Two quantitative measures, the mean square error and the structural similarity index, were applied to compare the quality of the reconstructed volumetric images. We observed that fast Fourier transformation and wavelet both are capable of reconstructing OCT image volumes for the orthogonal sparse sampling masks used in this report, but with different merits. [ABSTRACT FROM PUBLISHER]

A novel online method based on the symmetry property of the sum of projections (SOP) is proposed to obtain the geometric parameters in cone-beam computed tomography (CBCT). This method requires no calibration phantom and can be used in circular trajectory CBCT with arbitrary cone angles. An objective function is deduced to illustrate the dependence of the symmetry of SOP on geometric parameters, which will converge to its minimum when the geometric parameters achieve their true values. Thus, by minimizing the objective function, we can obtain the geometric parameters for image reconstruction. To validate this method, numerical phantom studies with different noise levels are simulated. The results show that our method is insensitive to the noise and can determine the skew (in-plane rotation angle of the detector), the roll (rotation angle around the projection of the rotation axis on the detector), and the rotation axis with high accuracy, while the mid-plane and source-to-detector distance will be obtained with slightly lower accuracy. However, our simulation studies validate that the errors of the latter two parameters brought by our method will hardly degrade the quality of reconstructed images. The small animal studies show that our method is able to deal with arbitrary imaging objects. In addition, the results of the reconstructed images in different slices demonstrate that we have achieved comparable image quality in the reconstructions as some offline methods. [ABSTRACT FROM PUBLISHER]