Cone-beam CT Reconstruction Research Articles

Model-based perfusion reconstruction with time separation technique in cone-beam CT dynamic liver perfusion imaging.

The success of embolization, a minimally invasive treatment of liver cancer, could be evaluated in the operational room with cone-beam CT by acquiring a dynamic perfusion scan to inspect the contrast agentflow. The reconstruction algorithm must address the issues of low temporal sampling and higher noise levels inherent in cone-beam CT systems, compared to conventional CT. Therefore, a model-based perfusion reconstruction based on the time separation technique (TST) was applied. TST uses basis functions to model time attenuation curves. These functions are either analytical or based on prior knowledge (PK), extracted using singular value decomposition of the classical CT perfusion data of animal subjects. To explore how well the PK can model perfusion dynamics and what the potential limitations are, the dynamic cone-beam CT (CBCT) perfusion scan was simulated from a dynamic CT perfusion scan under different noise levels. The TST method was compared to staticreconstruction. It was demonstrated on this simulated dynamic CBCT perfusion scan that a set consisting of only four basis functions results in perfusion maps that preserve relevant information, denoise the data, and outperform static reconstruction under higher noise levels. TST with PK would not only outperform static reconstruction but also the TST with analytical basis functions. Furthermore, it has been shown that only eight CBCT rotations, unlike previously assumed ten, are sufficient to obtain the perfusion maps comparable to the reference CT perfusion maps. This contributes to saving dose and reconstruction time. The real dynamic CBCT perfusion scan, reconstructed under the same conditions as the simulated scan, shows potential for maintaining the accuracy of the perfusion maps. By visual inspection, the embolized region was matching to that in corresponding CT perfusionmaps. CBCT reconstruction of perfusion scan data using the TST method has shown promising potential, outperforming static reconstructions and potentially saving dose by reducing the necessary number of acquisition sweeps. Further analysis of a larger cohort of patient data is needed to draw final conclusions regarding the expected advantages of theTST.

Real-time CBCT imaging and motion tracking via a single arbitrarily-angled x-ray projection by a joint dynamic reconstruction and motion estimation (DREME) framework

Objective.Real-time cone-beam computed tomography (CBCT) provides instantaneous visualization of patient anatomy for image guidance, motion tracking, and online treatment adaptation in radiotherapy. While many real-time imaging and motion tracking methods leveraged patient-specific prior information to alleviate under-sampling challenges and meet the temporal constraint (<500 ms), the prior information can be outdated and introduce biases, thus compromising the imaging and motion tracking accuracy. To address this challenge, we developed a frameworkdynamicreconstruction andmotionestimation (DREME) for real-time CBCT imaging and motion estimation, without relying on patient-specific prior knowledge.Approach.DREME incorporates a deep learning-based real-time CBCT imaging and motion estimation method into a dynamic CBCT reconstruction framework. The reconstruction framework reconstructs a dynamic sequence of CBCTs in a data-driven manner from a standard pre-treatment scan, without requiring patient-specific prior knowledge. Meanwhile, a convolutional neural network-based motion encoder is jointly trained during the reconstruction to learn motion-related features relevant for real-time motion estimation, based on a single arbitrarily-angled x-ray projection. DREME was tested on digital phantom simulations and real patient studies.Main Results.DREME accurately solved 3D respiration-induced anatomical motion in real time (∼1.5 ms inference time for each x-ray projection). For the digital phantom studies, it achieved an average lung tumor center-of-mass localization error of 1.2 ± 0.9 mm (Mean ± SD). For the patient studies, it achieved a real-time tumor localization accuracy of 1.6 ± 1.6 mm in the projection domain.Significance.DREME achieves CBCT and volumetric motion estimation in real time from a single x-ray projection at arbitrary angles, paving the way for future clinical applications in intra-fractional motion management. In addition, it can be used for dose tracking and treatment assessment, when combined with real-time dose calculation.

CBCT-Based Analysis of Factors Influencing the Quality of New Bone Formation Following Mandibular Distraction Osteogenesis in Children With Pierre Robin Sequence.

The aim of this study was to explore the factors influencing the quality of new bone formation after distraction osteogenesis in children with Pierre Robin sequence (PRS). Using cone-beam computed tomography (CBCT), bone density relative grayscale values of the region of new bone formation before and 3 to 4 months after mandibular distraction osteogenesis (MDO) were measured in 80 children with PRS, and correlation analysis was conducted with the potential clinical influencing factors of the children. CBCT reconstruction of the panoramic film showed that the new bone formation was good at 3 to 4 months after MDO. There was a statistically significant difference in the gray value of cancellous bone before and after the operation (P<0.01). The gray values of bilateral mandibular new bone after MDO were related to cleft palate, preoperative weight, preoperative body mass index (BMI), and distraction length. Finally, the variables included in the multiple linear regression model were cleft palate and preoperative BMI. At 3 to 4 months after MDO, the mineralization degree of cancellous bone in the central region of the bilateral mandibular new bone formation area was lower. The presence of cleft palate and preoperative BMI were identified as the main factors influencing the new bone formation in bilateral mandibles after MDO. This may be attributed to the catch-up nutritional acquisition and growth promotion in children, which facilitates new bone formation, along with greater chewing muscle strength to prevent mineral loss from bones.

Evaluation of CBCT reconstructed tooth models at different thresholds and voxels and their accuracy in fusion with IOS data: an in vitro validation study

BackgroundThis study aims to evaluate the impact of different thresholds and voxel sizes on the accuracy of Cone-beam computed tomography (CBCT) tooth reconstruction and to assess the accuracy of fused CBCT and intraoral scanning (IOS) tooth models using curvature continuity algorithms under varying thresholds and voxel conditions.MethodsThirty-two isolated teeth were digitized using IOS and CBCT at two voxel sizes and five threshold settings. Crown-root fusion was performed using a curvature continuity algorithm. Volume, surface area, and crown width of tooth models were compared to laser scanning models, and RMS error was measured. Data were analyzed using Wilcoxon signed-rank test, paired t-test, and one-way ANOVA.ResultsVolume amplification errors of CBCT with 0.15 mm and 0.3 mm voxels ranged from 1.22 to 19.07%, surface area errors from 0.18 to 7.78%, crown linearity errors ranged from 2.47 to 7.69%, root linearity errors ranged from − 1.02 to 2.26% and RMS from 0.0691 mm to 0.2408 mm. Crown-root fusion of IOS and CBCT data reduced volume error to -0.90–5.10%, surface area error to -0.66–4.15%, and RMS to 0.0359 mm to 0.0945 mm.ConclusionsVoxel size and threshold settings significantly affect the accuracy of CBCT reconstruction and crown-root fusion. Smaller voxel sizes yield higher reconstruction precision, and different voxel sizes and tooth regions correspond to distinct optimal segmentation thresholds. The validated semi-automated crown-root fusion algorithm significantly enhances overall model accuracy, offering new possibilities for clinical applications.

PE-INeR: prior-embedded implicit neural representation for sparse-view CBCT reconstruction

Conventional methods for reconstructing cone-beam computed tomography (CBCT) often suffer from artifacts and blurring in the presence of missing data, which ultimately hamper the quality of the resulting images. To address this challenge, we propose a neural implicit representation method (PE-INeR) based on prior embedding for sparse-view CBCT reconstruction. In our proposed method, we leverage prior information to guide the reconstruction process. By employing a neural implicit representation network, we capture the intricate features of the image in an implicit manner. Our experimental results underscore the superiority of our approach, demonstrating remarkable reductions in artifacts and substantial enhancements in image quality compared to traditional techniques. Moreover, it is important to emphasize that the PE-INeR outperforms alternative methods in effectively capturing nuanced yet critical image variations, which play a pivotal role in precisely evaluating the advancement of tumors.

Analytic sparse-view CBCT reconstruction using a novel sinogram restoration method based on higher-order Fourier harmonic peaks

This study presents an analytic sparse-view cone-beam computed tomography (CBCT) reconstruction method using a novel sinogram restoration technique based on higher-order Fourier harmonic peaks to obtain CBCT images of reasonable quality with a reduced radiation dose. The proposed method involves four main steps: (1) acquiring sparse-view (P144) sinogram from a CBCT system, (2) upsampling (P720) the original sparse-view sinogram with equally spaced zero padding, (3) separating higher-order Fourier harmonic peaks of the upsampled sinogram using a band-pass filter, and (4) restoring the sinogram of the higher-order harmonic peaks by taking its magnitude, followed by computationally cost-efficient filtered-backprojection (FBP)-based CBCT reconstruction and denoising processes. The notation P144 indicates that the number of projections is 144. To verify the efficacy of the proposed method, an experiment was performed on a chest phantom using a prototype CBCT system comprising an X-ray tube (90 kV p and 40 mA), a large-area flat-panel detector (388 μm pixel size), and a rotational gantry. The quality of the restored sparse-view CBCT images was quantitatively evaluated in terms of the image intensity profile, universal quality index (UQI), and peak signal-to-noise ratio (PSNR). The results indicate that the proposed sinogram restoration method effectively recovered zero-padded areas in the original upsampled sinogram with image intensities nearly identical to those of the reference dense-view (P720) sinogram, which demonstrates the efficacy of the proposed sinogram restoration method. The quality of the sparse-view CBCT image restored with higher-order harmonic peaks was close to that of the reference dense-view CBCT image, maintaining the image quality. The UQI and PSNR values of the restored sparse-view CBCT image were 0.93 and 34.84, respectively, which are approximately 1.3 and 1.2 times greater than those of the original sparse-view CBCT image, respectively, improving the image quality. Consequently, the proposed method has the potential to resolve the issues caused by sparse-view sampling in FBP-based CBCT.

Eliminating metal artifacts in dental computed tomography using an elaborate sinogram normalization interpolation method with CNR-based metal segmentation

Metal artifact reduction (MAR) in dental cone-beam computed tomography (CBCT) is critical for improving its clinical usefulness and remains a challenging problem. This study presents an elaborate sinogram normalization interpolation (SNI) method with contrast-to-noise ratio (CNR)-based metal segmentation to eliminate metal artifacts in dental CBCT. The proposed MAR method involves three main steps: (1) CNR-based segmentation of a metal trace in the sinogram domain; (2) generation of a residual artifact-reduced prior sinogram; and (3) sinogram completion using the prior sinogram, followed by filtered-backprojection (FBP)-based CBCT reconstruction and metal insertion processes. To verify the efficacy of the proposed method, simulations and experiments were performed using numerical and physical dental phantoms with several metal inserts. The quality of the resulting CBCT images was quantitatively evaluated using the structural similarity (SSIM) metric and compared to those obtained with other interpolation-based MAR methods. A tabletop setup for the micro-CT system was used in the experiment, which comprised an X-ray tube operated under tube conditions of 70 kV p and 5 mA and a flat-panel detector with a pixel size of 198 μm. Our results indicate that the CNR-based metal segmentation method precisely identified traces of metallic objects on the sinogram, and thereby, the proposed SNI-based MAR method considerably reduced metal artifacts in dental CBCT images without introducing any contrast anomalies. The SSIM values of the CBCT images obtained with the proposed MAR method were 0.99 and 0.95 for the simulation and experiment, respectively, which are approximately 11% and 9% greater than those with a simple threshold-based linear interpolation method, respectively, improving the image quality. The proposed MAR method can be applied to reduce metal artifacts in real-world dental CBCT systems.

The Accuracy of the CBCT-Based 3-Dimensional Replica of the Donor Tooth in Autotransplantation.

This study evaluated the accuracy of the CBCT reconstruction model compared to the natural tooth and the accuracy of the replica tooth compared to the natural tooth. The hypothesis was that a replica tooth could be used as a surgical guide in autotransplantation. Three teeth were chosen and a CBCT reconstruction model was formed from each tooth. STL-data was transferred to a milling machine and replica teeth were milled from PEEK. A digitized surface model was prepared from the natural and the replica teeth by a stereophotogrammetry scanner. The surface model from the optical scan of the natural tooth was compared to the CBCT reconstruction model and the surface model of the replica tooth. The models were matched on each other, and surface-based rigid registration was performed between the surface models. Distances were calculated and visualized by MATLAB. The CBCT reconstruction model and the natural tooth were compared. The largest euclidean distance was found at the root tip in the premolar (0.93 mm) and at the furcation area in the molar (2.3 mm). When the natural tooth and the replica tooth were compared, the largest euclidean distance was found at the root tip in the premolar (1.5 mm) and at the furcation area in the molar (1.9 mm). A CBCT scan maintains sufficient image quality for tooth autotransplantation planning. The replica tooth corresponded in size and shape to the natural tooth in terms of clinically expected need of precision.

A dual-domain cone beam computed tomography sparse-view reconstruction method based on generative projection interpolation

To propose a dual-domain CBCT reconstruction framework (DualSFR-Net) based on generative projection interpolation to reduce artifacts in sparse-view cone beam computed tomography (CBCT) reconstruction. The proposed method DualSFR-Net consists of a generative projection interpolation module, a domain transformation module, and an image restoration module. The generative projection interpolation module includes a sparse projection interpolation network (SPINet) based on a generative adversarial network and a full-view projection restoration network (FPRNet). SPINet performs projection interpolation to synthesize full-view projection data from the sparse-view projection data, while FPRNet further restores the synthesized full-view projection data. The domain transformation module introduces the FDK reconstruction and forward projection operators to complete the forward and gradient backpropagation processes. The image restoration module includes an image restoration network FIRNet that fine-tunes the domain-transformed images to eliminate residual artifacts and noise. Validation experiments conducted on a dental CT dataset demonstrated that DualSFR-Net was capable to reconstruct high-quality CBCT images under sparse-view sampling protocols. Quantitatively, compared to the current best methods, the DualSFR-Net method improved the PSNR by 0.6615 and 0.7658 and increased the SSIM by 0.0053 and 0.0134 under 2-fold and 4-fold sparse protocols, respectively. The proposed generative projection interpolation-based dual-domain CBCT sparse-view reconstruction method can effectively reduce stripe artifacts to improve image quality and enables efficient joint training for dual-domain imaging networks in sparse-view CBCT reconstruction.

Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstruction.

Cone Beam Computed Tomography (CBCT) plays a vital role in clinical imaging. Traditional methods typically require hundreds of 2D X-ray projections to reconstruct a high-quality 3D CBCT image, leading to considerable radiation exposure. This has led to a growing interest in sparse-view CBCT reconstruction to reduce radiation doses. While recent advances, including deep learning and neural rendering algorithms, have made strides in this area, these methods either produce unsatisfactory results or suffer from time inefficiency of individual optimization. In this paper, we introduce a novel geometry-aware encoder-decoder framework to solve this problem. Our framework starts by encoding multi-view 2D features from various 2D X-ray projections with a 2D CNN encoder. Leveraging the geometry of CBCT scanning, it then back-projects the multi-view 2D features into the 3D space to formulate a comprehensive volumetric feature map, followed by a 3D CNN decoder to recover 3D CBCT image. Importantly, our approach respects the geometric relationship between 3D CBCT image and its 2D X-ray projections during feature back projection stage, and enjoys the prior knowledge learned from the data population. This ensures its adaptability in dealing with extremely sparse view inputs without individual training, such as scenarios with only 5 or 10 X-ray projections. Extensive evaluations on two simulated datasets and one real-world dataset demonstrate exceptional reconstruction quality and time efficiency of our method.

Effective and efficient coded aperture cone-beam computed tomography via generative adversarial U-Net

Coded aperture cone-beam computed tomography (CBCT) represents a crucial method for acquiring high-fidelity three-dimensional (3D) tomographic images while reducing radiation exposure. However, projections are non-uniformly and discontinuously sampled with the coded apertures placed in front of the x-ray source, leading to very small reconstruction scale and time-intensive iterations. In this study, an alternative approach to reconstruct coded aperture CBCT based on generative adversarial U-net is proposed to effectively and efficiently reconstruct large scale 3D CBCT images. Our method entails predicting complete and uniform projections from incomplete and non-uniform coded projections, enabling the requirement of continuity for the use of analytical algorithms in 3D image reconstruction. This novel technique effectively mitigates the traditional trade-off between image fidelity and computational complexity inherent in conventional coded aperture CBCT reconstruction methods. Our experimental results, conducted using clinical datasets comprising CBCT images from 102 patients at Nanjing Medical University, demonstrate that high-quality CBCT images with voxel dimensions of 400 × 400 × 400 can be reconstructed within 35 s, even when 95% of projections are blocked, yielding images with PSNR values exceeding 25dB and SSIM values surpassing 0.85.

StarAN: A star attention network utilizing inter-view and intra-view correlations for sparse-view cone-beam computed tomography reconstruction

Sparse sampling can reduce the radiation dose and accelerate the scanning speed of cone-beam computed tomography (CBCT) by increasing the angular interval between projections. However, this approach can compromise the completeness of the projection data, leading to severe artifacts in the reconstructed images. CBCT projection sequences exhibit correlations, and repairing disrupted correlations in sparse-view CBCT projection sequences contributes to improving the quality of the reconstructed images. In this paper, a star attention network (StarAN) based on star attention and a sinogram projection-based cascaded restoration strategy (SPCRS) are proposed. First, missing projection data in the sparse-view projection sequences are filled using interpolation. Second, based on Res-U-Net, the StarAN network is designed with star attention guidance, which integrates channel attention and spatial attention to form a “star-shaped” three-dimensional receptive field, fully capturing the correlations between any pixel in the feature maps and all pixels in the same row or column within the same channel, as well as in corresponding spatial positions across different channels, ensuring the accuracy of the restored projection data. Additionally, an SPCRS is presented that employs two cascaded StarAN networks to sequentially restore projection sequences, further repairing the correlations in the sinogram and projection domains. Finally, the filtered back-projection method is used to reconstruct the restored projection sequences. Experiments conducted on a public walnut dataset and a private dental dataset demonstrate that, compared with state-of-the-art methods, our method achieves superior results, stability, and generalizability.

Technical note: Phantom-based evaluation of CBCT dose calculation accuracy for use in adaptive radiotherapy.

High-quality 3D-anatomy of the day is needed for treatment plan adaptation in radiotherapy. For online x-ray-based CBCT workflows, one approach is to create a synthetic CT or to utilize a fan-beam CT with corresponding registrations. The former potentially introduces uncertainties in the dose calculation if deformable image registration is used. The latter can introduce burden and complexity to the process, the facility, and thepatient. Using the CBCT of the day, acquired on the treatment device, for direct dose calculation and plan adaptation can overcome these limitations. This study aims to assess the accuracy of the calculated dose on the CBCT scans acquired on a Halcyon linear accelerator equipped withHyperSight. HyperSight's new CBCT reconstruction algorithm includes improvements in scatter correction, HU calibration of the imager, and beam shape adaptation. Furthermore, HyperSight introduced a new x-ray detector. To show the effect of the implemented improvements, gamma comparisons of 2%/2mm, 2%/1mm, and 1%/1mm were made between the dose distribution in phantoms calculated on the CBCT reconstructions and the simulation CT scans, considering this the standard of care. The resulting gamma passing rates were compared to those obtained with the Halcyon 3.0 reconstruction and hardware without HyperSight's technologies. Various anatomical phantoms for dosimetric evaluations on brain, head and neck, lung, breast, and prostate cases have been used in thisstudy. The overall results demonstrated that HyperSight outperformed the Halcyon 3.0 version. Based on the gamma analysis, the calculated dose using HyperSight was closer to the CT scan-based doses than the calculated dose using iCBCT Halcyon 3.0 for most cases. Over all plans and gamma criteria, Halcyon 3.0 achieved an average passing rate of 92.9%, whereas HyperSight achieved 98.1%. Using HyperSight CBCT images for direct dose calculation, for example, in (online) plan adaptation, seems feasible for the investigatedcases.

Prior frequency guided diffusion model for limited angle (LA)-CBCT reconstruction

Objective. Cone-beam computed tomography (CBCT) is widely used in image-guided radiotherapy. Reconstructing CBCTs from limited-angle acquisitions (LA-CBCT) is highly desired for improved imaging efficiency, dose reduction, and better mechanical clearance. LA-CBCT reconstruction, however, suffers from severe under-sampling artifacts, making it a highly ill-posed inverse problem. Diffusion models can generate data/images by reversing a data-noising process through learned data distributions; and can be incorporated as a denoiser/regularizer in LA-CBCT reconstruction. In this study, we developed a diffusion model-based framework, prior frequency-guided diffusion model (PFGDM), for robust and structure-preserving LA-CBCT reconstruction. Approach. PFGDM uses a conditioned diffusion model as a regularizer for LA-CBCT reconstruction, and the condition is based on high-frequency information extracted from patient-specific prior CT scans which provides a strong anatomical prior for LA-CBCT reconstruction. Specifically, we developed two variants of PFGDM (PFGDM-A and PFGDM-B) with different conditioning schemes. PFGDM-A applies the high-frequency CT information condition until a pre-optimized iteration step, and drops it afterwards to enable both similar and differing CT/CBCT anatomies to be reconstructed. PFGDM-B, on the other hand, continuously applies the prior CT information condition in every reconstruction step, while with a decaying mechanism, to gradually phase out the reconstruction guidance from the prior CT scans. The two variants of PFGDM were tested and compared with current available LA-CBCT reconstruction solutions, via metrics including peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Main results. PFGDM outperformed all traditional and diffusion model-based methods. The mean(s.d.) PSNR/SSIM were 27.97(3.10)/0.949(0.027), 26.63(2.79)/0.937(0.029), and 23.81(2.25)/0.896(0.036) for PFGDM-A, and 28.20(1.28)/0.954(0.011), 26.68(1.04)/0.941(0.014), and 23.72(1.19)/0.894(0.034) for PFGDM-B, based on 120°, 90°, and 30° orthogonal-view scan angles respectively. In contrast, the PSNR/SSIM was 19.61(2.47)/0.807(0.048) for 30° for DiffusionMBIR, a diffusion-based method without prior CT conditioning. Significance. PFGDM reconstructs high-quality LA-CBCTs under very-limited gantry angles, allowing faster and more flexible CBCT scans with dose reductions.

A dual-domain cone beam computed tomography reconstruction framework with improved differentiable domain transform for cone-angle artifact correction

We propose a dual-domain cone beam computed tomography (CBCT) reconstruction framework DualCBR-Net based on improved differentiable domain transform for cone-angle artifact correction. The proposed CBCT dual-domain reconstruction framework DualCBR-Net consists of 3 individual modules: projection preprocessing, differentiable domain transform, and image post-processing. The projection preprocessing module first extends the original projection data in the row direction to ensure full coverage of the scanned object by X-ray. The differentiable domain transform introduces the FDK reconstruction and forward projection operators to complete the forward and gradient backpropagation processes, where the geometric parameters correspond to the extended data dimension to provide crucial prior information in the forward pass of the network and ensure the accuracy in the gradient backpropagation, thus enabling precise learning of cone-beam region data. The image post-processing module further fine-tunes the domain-transformed image to remove residual artifacts and noises. The results of validation experiments conducted on Mayo's public chest dataset showed that the proposed DualCBR-Net framework was superior to other comparison methods in terms of artifact removal and structural detail preservation. Compared with the latest methods, the DualCBR-Net framework improved the PSNR and SSIM by 0.6479 and 0.0074, respectively. The proposed DualCBR-Net framework for cone-angle artifact correction allows effective joint training of the CBCT dual-domain network and is especially effective for large cone-angle region.

Dynamic CBCT imaging using prior model-free spatiotemporal implicit neural representation (PMF-STINR)

Objective. Dynamic cone-beam computed tomography (CBCT) can capture high-spatial-resolution, time-varying images for motion monitoring, patient setup, and adaptive planning of radiotherapy. However, dynamic CBCT reconstruction is an extremely ill-posed spatiotemporal inverse problem, as each CBCT volume in the dynamic sequence is only captured by one or a few x-ray projections, due to the slow gantry rotation speed and the fast anatomical motion (e.g. breathing). Approach. We developed a machine learning-based technique, prior-model-free spatiotemporal implicit neural representation (PMF-STINR), to reconstruct dynamic CBCTs from sequentially acquired x-ray projections. PMF-STINR employs a joint image reconstruction and registration approach to address the under-sampling challenge, enabling dynamic CBCT reconstruction from singular x-ray projections. Specifically, PMF-STINR uses spatial implicit neural representations to reconstruct a reference CBCT volume, and it applies temporal INR to represent the intra-scan dynamic motion of the reference CBCT to yield dynamic CBCTs. PMF-STINR couples the temporal INR with a learning-based B-spline motion model to capture time-varying deformable motion during the reconstruction. Compared with the previous methods, the spatial INR, the temporal INR, and the B-spline model of PMF-STINR are all learned on the fly during reconstruction in a one-shot fashion, without using any patient-specific prior knowledge or motion sorting/binning. Main results. PMF-STINR was evaluated via digital phantom simulations, physical phantom measurements, and a multi-institutional patient dataset featuring various imaging protocols (half-fan/full-fan, full sampling/sparse sampling, different energy and mAs settings, etc). The results showed that the one-shot learning-based PMF-STINR can accurately and robustly reconstruct dynamic CBCTs and capture highly irregular motion with high temporal (∼ 0.1 s) resolution and sub-millimeter accuracy. Significance. PMF-STINR can reconstruct dynamic CBCTs and solve the intra-scan motion from conventional 3D CBCT scans without using any prior anatomical/motion model or motion sorting/binning. It can be a promising tool for motion management by offering richer motion information than traditional 4D-CBCTs.

Comparison of the accuracy of bracket axial positioning with and without radiographic support and according to practitioner experience: A three-dimensional study

Accurate bracket positioning remains challenging. To avoid angulation errors, some recommend examining the panoramic radiograph during bonding. However, it can cause distortions. Cone-beam computed tomography (CBCT) provides a more precise panoramic reconstruction but with higher radiation doses. The main objective of this study is to compare the accuracy of axial positioning between direct bonding without radiography, with conventional panoramic radiograph, and with panoramic reconstruction from CBCT. The secondary objectives are to evaluate positioning accuracy of each tooth and to assess the influence of practitioner level of experience. Thirty practitioners, divided into two groups based on their experience performed direct bonding on a model thrice: without radiography, then with the conventional panoramic radiograph, then with the panoramic reconstruction from CBCT. Models were scanned, and angulation errors were measured using OrthoAnalyzer. Values were compared using the Friedman's test followed by the Bonferroni correction for multiple comparisons (P-value = 0.05). For the low level of experience group, angulation errors were significantly greater than the accepted limit without radiographic reference, and significantly lower with CBCT reconstruction. For the high level of experience group, angulation errors were significantly lower than the accepted limit for the three bonding methods. For every tooth, using the panoramic reconstruction from CBCT as a reference, was the most accurate method, regardless of the level of experience. More experienced practitioners made fewer errors for the three methods. Panoramic reconstruction from CBCT is the most accurate method to limit angulation errors during direct bonding. Conventional panoramic radiography remains a reliable tool if used with caution. Bonding without any radiographic reference should be avoided especially for less experienced practitioners.

Effect of scatter suppression with 2D antiscatter grids in photon counting compact CBCT.

Energy sensitive and photon counting detectors can provide improved tissue visualization and material quantification capabilities in Cone Beam Computed Tomography (CBCT) systems. However, their implementation in CBCT systems is more challenging, which is in part due to high fluence of scattered X-rays in wide cone angle CBCT geometry. Specifically, high scatter contamination in lower energy spectrum challenges reconstruction of high fidelity CBCT images by using lower energy X-rays. To address this problem, we investigated a robust scatter rejection with 2D antiscatter grids in a benchtop photon counting and compact CBCT system. The benchtop system employs a 35 cm wide CdTe photon counting detector with two energy thresholds. To reject scatter, a dedicated 2D antiscatter grid (2D grid) prototype made from tungsten was developed and mounted directly on the detector. To correct residual scatter not stopped by the 2D grid, a measurement-based scatter correction method, referred to as Grid-based Scatter Sampling (GSS), was utilized. Without 2D grid, scatter to primary ratio (SPR) reached 2.3 in the 15-40 keV energy bin. SPR was factor of 3 higher in the lowest energy bin when compared to the highest energy bin (90-120 keV). With the 2D grid, SPR was reduced below 0.14, and SPR values were more homogenous across the energy spectrum. CT number nonuniformity was factor of 3 lower in both low and high energy bin CBCT reconstructions. Improvement in contrast to noise ratio and contrast was more pronounced in the low energy bin CBCT images. This work indicates that 2D grids can significantly reduce spectral contamination caused by scatter in photon counting compact CBCT, and potentially enable higher fidelity CBCT image reconstructions.

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.

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.