CT Reconstruction Method Research Articles

An efficient dual-domain deep learning network for sparse-view CT reconstruction

Background and ObjectiveWe develop an efficient deep-learning based dual-domain reconstruction method for sparse-view CT reconstruction with small training parameters and comparable running time. We aim to investigate the model's capability and its clinical value by performing objective and subjective quality assessments using clinical CT projection data acquired on commercial scanners. MethodsWe designed two lightweight networks, namely Sino-Net and Img-Net, to restore the projection and image signal from the DD-Net reconstructed images in the projection and image domains, respectively. The proposed network has small training parameters and comparable running time among dual-domain based reconstruction networks and is easy to train (end-to-end). We prospectively collected clinical thoraco-abdominal CT projection data acquired on a Siemens Biograph 128 Edge CT scanner to train and validate the proposed network. Further, we quantitatively evaluated the CT Hounsfield unit (HU) values on 21 organs and anatomic structures, such as the liver, aorta, and ribcage. We also analyzed the noise properties and compared the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) of the reconstructed images. Besides, two radiologists conducted the subjective qualitative evaluation including the confidence and conspicuity of anatomic structures, and the overall image quality using a 1–5 likert scoring system. ResultsObjective and subjective evaluation showed that the proposed algorithm achieves competitive results in eliminating noise and artifacts, restoring fine structure details, and recovering edges and contours of anatomic structures using 384 views (1/6 sparse rate). The proposed method exhibited good computational cost performance on clinical projection data. ConclusionThis work presents an efficient dual-domain learning network for sparse-view CT reconstruction on raw projection data from a commercial scanner. The study also provides insights for designing an organ-based image quality assessment pipeline for sparse-view reconstruction tasks, potentially benefiting organ-specific dose reduction by sparse-view imaging.

Two-view topogram-based anatomy-guided CT reconstruction for prospective risk minimization

To facilitate a prospective estimation of the effective dose of an CT scan prior to the actual scanning in order to use sophisticated patient risk minimizing methods, a prospective spatial dose estimation and the known anatomical structures are required. To this end, a CT reconstruction method is required to reconstruct CT volumes from as few projections as possible, i.e. by using the topograms, with anatomical structures as correct as possible. In this work, an optimized CT reconstruction model based on a generative adversarial network (GAN) is proposed. The GAN is trained to reconstruct 3D volumes from an anterior-posterior and a lateral CT projection. To enhance anatomical structures, a pre-trained organ segmentation network and the 3D perceptual loss are applied during the training phase, so that the model can then generate both organ-enhanced CT volume and organ segmentation masks. The proposed method can reconstruct CT volumes with PSNR of 26.49, RMSE of 196.17, and SSIM of 0.64, compared to 26.21, 201.55 and 0.63 using the baseline method. In terms of the anatomical structure, the proposed method effectively enhances the organ shapes and boundaries and allows for a straight-forward identification of the relevant anatomical structures. We note that conventional reconstruction metrics fail to indicate the enhancement of anatomical structures. In addition to such metrics, the evaluation is expanded with assessing the organ segmentation performance. The average organ dice of the proposed method is 0.71 compared with 0.63 for the baseline model, indicating the enhancement of anatomical structures.

Low-dose CT reconstruction based on high-dimensional partial differential equation projection recovery

We propose a low-dose CT reconstruction method using partial differential equation (PDE) denoising under high-dimensional constraints. The projection data were mapped into a high-dimensional space to construct a high-dimensional representation of the data, which were updated by moving the points in the high-dimensional space. The data were denoised using partial differential equations and the CT image was reconstructed using the FBP algorithm. Compared with those by FBP, PWLS-QM and TGV-WLS methods, the relative root mean square error of the Shepp-Logan image reconstructed by the proposed method were reduced by 68.87%, 50.15% and 27.36%, the structural similarity values were increased by 23.50%, 8.83% and 1.62%, and the feature similarity values were increased by 17.30%, 2.71% and 2.82%, respectively. For clinical image reconstruction, the proposed method, as compared with FBP, PWLS-QM and TGV-WLS methods, resulted in reduction of the relative root mean square error by 42.09%, 31.04% and 21.93%, increased the structural similarity values by 18.33%, 13.45% and 4.63%, and increased the feature similarity values by 3.13%, 1.46% and 1.10%, respectively. The new method can effectively reduce the streak artifacts and noises while maintaining the spatial resolution in reconstructed low-dose CT images.

Sparse-view CT reconstruction based on group-based sparse representation using weighted guided image filtering.

In the past, guided image filtering (GIF)-based methods often utilized total variation (TV)-based methods to reconstruct guidance images. And they failed to reconstruct the intricate details of complex clinical images accurately. Toaddress these problems, we propose a new sparse-view CT reconstruction method based on group-based sparse representation using weighted guided image filtering. In each iteration of the proposed algorithm, the result constrained by the group-based sparse representation (GSR) is used as the guidance image. Then, the weighted guided image filtering (WGIF) was used to transfer the important features from the guidance image to the reconstruction of the SART method. Three representative slices were tested under 64 projection views, and the proposed method yielded the best visual effect. For the shoulder case, the PSNR can achieve 48.82, which is far superior to other methods. The experimental results demonstrate that our method is more effective in preserving structures, suppressing noise, and reducing artifacts compared to other methods.

A fast regularized CT reconstruction method based on a preconditioning matrix for limited-angle fan-beam CT

This paper proposes a fast regularized method for limited angle CT, the preconditioning matrix, which includes a weighting matrix and a filtering matrix, is utilized to improve the convergence speed. The projection and back-projection in the iteration process are replaced by the analytic operators to accelerate the computation speed. In addition, a Hamming window function is added to further suppress the noise and artifacts. Real data experiments show that the proposed method outperforms the other three methods in preserving the edge, convergence speed and computing speed.

Analysis of the influence of 2D and 3D void distribution characteristics of matrix skeleton on the mechanical properties of semi-flexible pavement: industrial CT scanning and 3D reconstruction

ABSTRACT This study aims to clarify the specific impact of the void distribution characteristics of the matrix skeleton on the mechanical properties of semi-flexible pavement (SFP) and form a targeted control method for the mechanical properties of SFP based on the gradation design of the matrix skeleton. Therefore, based on industrial CT and 3D reconstruction methods, this study comprehensively analyzed the matrix skeleton's two-dimensional and three-dimensional void distribution characteristics and conducted correlation analysis with the mechanical properties such as compression characteristics, tensile characteristics, dynamic modulus characteristics, and cracking characteristics of SFP. Research has found that the strength and rigidity of SFP are mainly positively correlated with the porosity and void's aperture of the matrix skeleton. The toughness of SFP is mainly positively correlated with the uniformity of void distribution and the three-dimensional void relative surface area index. The three-dimensional void relative surface area index is the most critical indicator affecting the mechanical properties of SFP, followed by the uniformity of void distribution, porosity, and the void's aperture.

Sparse-view CT reconstruction method for in-situ non-destructive testing of reinforced concrete

ABSTRACT X-ray CT is an important in-situ non-destructive testing method that plays an important role in both production and materials research. In-situ CT of concrete can acquire images of concrete slices in the state of holding force loading, which is meaningful for the study of concrete crack development characteristics. However, existing X-ray CT equipment either cannot penetrate large reinforced concrete due to energy limitations or does not address the extremely sparse angular reconstruction problem associated with in-situ testing. In this paper, a sinogram Transformer method based on a priori projection data is proposed to solve this extremely sparse data complementation problem on a large-scale electron accelerator X-ray in-situ CT system. The simulated concrete sparse-view projection data and the full projection data are used as the input and label of the proposed method in order to experiment the method. The results demonstrate the feasibility of using a priori projected sine diagrams with ultra-sparse sine diagram residuals to track sine diagram variation, and verify that structures such as cracks and reinforcement, which dominate the deformation residuals of sinograms in concrete sections, can also be successfully recovered using Transformer in the ultra-sparse view projection of views 4–8.

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

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

Sparse angle CT reconstruction based on group sparse representation.

In order to solve the problem of image quality degradation of CT reconstruction under sparse angle projection, we propose to develop and test a new sparse angle CT reconstruction method based on group sparse. In this method, the group-based sparse representation is introduced into the statistical iterative reconstruction framework as a regularization term to construct the objective function. The group-based sparse representation no longer takes a single patch as the minimum unit of sparse representation, while it uses Euclidean distance as a similarity measure, thus it divides similar patch into groups as basic units for sparse representation. This method fully considers the local sparsity and non-local self-similarity of image. The proposed method is compared with several commonly used CT image reconstruction methods including FBP, SART, SART-TV and GSR-SART with experiments carried out on Sheep_Logan phantom and abdominal and pelvic images. In three experiments, the visual effect of the proposed method is the best. Under 64 projection angles, the lowest RMSE is 0.004776 and the highest VIF is 0.948724. FSIM and SSIM are all higher than 0.98. Under 50 projection angles, the index of the proposed method remains achieving the best image quality. Qualitative and quantitative results of this study demonstrate that this new proposed method can not only remove strip artifacts, but also effectively protect image details.

Texture-aware dual domain mapping model for low-dose CT reconstruction.

Remarkable progress has been made for low-dose computed tomography (CT) reconstruction tasks by applying deep learning techniques. However, establishing an intrinsic link between deep learning techniques and CT texture preservation is still one of the significant challenges for researchers to further improve the effect of low-dose CT (LDCT) reconstruction. Most of the existing deep learning-based LDCT reconstruction methods are derived from popular frameworks, and most models focus on the image domain. Even few existing methods start with dual domains (sinogram and image) by considering the processing of the data itself, the final performances are limited due to the lack of perception of textures. With this in mind, we propose a method for texture perception on dual domains, so that the reconstruction process can be uniformly driven by visualeffects. The proposed method involves the processing of two domains: the sinogram domain and the image domain. For the sinogram domain, we have designed a novel dilated residual network (S-DRN) which aims to increase the receptive field to obtain multiscale information. For the image domain, we propose a self-attention (SA) residual encoder & decoder network (SRED-Net) as the denoising network for obtaining much acceptable edges and textures. In addition, the composite loss function composed of the feature loss constructed by the proposed boundary and texture feature-aware network (BTFAN) and the mean square error (MSE) can obtain a higher image quality while retaining more details and fewer artifacts, thereby obtaining better visual image quality. The proposed method was validated using both the American association of physicists in medicine (AAPM)-Mayo clinic LDCT data sets and a real clinic data. Experimental results demonstrated that the new method has achieved the state-of-the-art performance on objective indicators and visual metrics in terms of denoising and texture restoration. Compared with single-domain or existing dual-domain processing strategies, the proposed texture-aware dual domain mapping network (TADDM-Net) can much better improve the visual effect of reconstructed CT images. Meantime, we also provide much intuitive evidence in terms of modelinterpretability.

Analytical covariance estimation for iterative CT reconstruction methods

Covariance of reconstruction images are useful to analyze the magnitude and correlation of noise in the evaluation of systems and reconstruction algorithms. The covariance estimation requires a big number of image samples that are hard to acquire in reality. A covariance propagation method from projection by a few noisy realizations is studied in this work. Based on the property of convergent points of cost funtions, the proposed method is composed of three steps, (1) construct a relationship between the covariance of projection and corresponding reconstruction from cost functions at its convergent point, (2) simplify the covariance relationship constructed in (1) by introducing an approximate gradient of penalties, and (3) obtain an analytical covariance estimation according to the simplified relationship in (2). Three approximation methods for step (2) are studied: the linear approximation of the gradient of penalties (LAM), the Taylor apprximation (TAM), and the mixture of LAM and TAM (MAM). TV and qGGMRF penalized weighted least square methods are experimented on. Results from statistical methods are used as reference. Under the condition of unstable 2nd derivative of penalties such as TV, the covariance image estimated by LAM accords to reference well but of smaller values, while the covarianc estimation by TAM is quite off. Under the conditon of relatively stable 2nd derivative of penalties such as qGGMRF, TAM performs well and LAM is again with a negative bias in magnitude. MAM gives a best performance under both conditions by combining LAM and TAM. Results also show that only one noise realization is enough to obtain reasonable covariance estimation analytically, which is important for practical usage. This work suggests the necessity and a new way to estimate the covariance for non-quadratically penalized reconstructions. Currently, the proposed method is computationally expensive for large size reconstructions.Computational efficiency is our future work to focus.

Technical note: Evaluation of a V-Net autosegmentation algorithm for pediatric CT scans: Performance, generalizability, and application to patient-specific CT dosimetry.

This study developed and evaluated a fully convolutional network (FCN) for pediatric CT organ segmentation and investigated the generalizability of the FCN across image heterogeneities such as CT scanner model protocols and patient age. We also evaluated the autosegmentation models as part of a software tool for patient-specific CT dose estimation. A collection of 359 pediatric CT datasets with expert organ contours were used for model development and evaluation. Autosegmentation models were trained for each organ using a modified FCN 3D V-Net. An independent test set of 60 patients was withheld for testing. To evaluate the impact of CT scanner model protocol and patient age heterogeneities, separate models were trained using a subset of scanner model protocols and pediatric age groups. Train and test sets were split to answer questions about the generalizability of pediatric FCN autosegmentation models to unseen age groups and scanner model protocols, as well as the merit of scanner model protocol or age-group-specific models. Finally, the organ contours resulting from the autosegmentation models were applied to patient-specific dose maps to evaluate the impact of segmentation errors on organ doseestimation. Results demonstrate that the autosegmentation models generalize to CT scanner acquisition and reconstruction methods which were not present in the training dataset. While models are not equally generalizable across age groups, age-group-specific models do not hold any advantage over combining heterogeneous age groups into a single training set. Dice similarity coefficient (DSC) and mean surface distance results are presented for 19 organ structures, for example, median DSC of 0.52 (duodenum), 0.74 (pancreas), 0.92 (stomach), and 0.96 (heart). The FCN models achieve a mean dose error within 5% of expert segmentations for all 19 organs except for the spinal canal, where the mean error was 6.31%. Overall, these results are promising for the adoption of FCN autosegmentation models for pediatric CT, including applications for patient-specific CT doseestimation.

A bilateral optimization iteration CT reconstruction method for incomplete projections

A new incomplete projections CT reconstruction method was proposed by considering the key points of iteration process. The limitations of two key points in iteration method were discussed. The new method was obtained by discussing the iteration process of the two methods, which was called bilateral optimization iteration reconstruction method. By providing more restrain equations of projection in iteration, the sinogram TVM process could optimize the ART process to obtain more accurate reconstruction results. And by providing more accurate initial value for iteration, the ART method could optimize the sinogram repairment by TVM process to obtain more accurate projection information as initial values. A series of numerical reconstruction experiments were conducted to validate the new method. The overall and detailed quality of model image reconstructed by new method was better than that of conventional methods. And the overall and detailed quality of actual experimental images reconstructed by the new method was also better than conventional methods.

4D CT reconstruction method for periodically moving objects in a high-speed camera CT system

Purpose of this study is to enable 4D CT reconstruction of objects in periodic motion by using a high-speed CT system. 4D CT reconstruction is a promising technique for dynamic internal inspection of products. In general, 4D CT reconstruction means that a volume is generated at each time frame from projection images scanned while an object is in motion. Proposed method in this study is phase sorting reconstruction using motion period of object. Proposed method was validated using simulated and real data. In addition, this study explored and validated several methods for sorting projections by motion phase.

Low-dose CT reconstruction with Noise2Noise network and testing-time fine-tuning.

Deep learning-based image denoising and reconstruction methods demonstrated promising performance on low-dose CT imaging in recent years. However, most existing deep learning-based low-dose CT reconstruction methods require normal-dose images for training. Sometimes such clean images do not exist such as for dynamic CT imaging or very large patients. The purpose of this work is to develop a low-dose CT image reconstruction algorithm based on deep learning which does not need clean images for training. In this paper, we proposed a novel reconstruction algorithm where the image prior was expressed via the Noise2Noise network, whose weights were fine-tuned along with the image during the iterative reconstruction. The Noise2Noise network built a self-consistent loss by projection data splitting and mapping the corresponding filtered backprojection (FBP) results to each other with a deep neural network. Besides, the network weights are optimized along with the image to be reconstructed under an alternating optimization scheme. In the proposed method, no clean image is needed for network training and the testing-time fine-tuning leads to optimization for each reconstruction. We used the 2016 Low-dose CT Challenge dataset to validate the feasibility of the proposed method. We compared its performance to several existing iterative reconstruction algorithms that do not need clean training data, including total variation, non-local mean, convolutional sparse coding, and Noise2Noise denoising. It was demonstrated that the proposed Noise2Noise reconstruction achieved better RMSE, SSIM and texture preservation compared to the other methods. The performance is also robust against the different noise levels, hyperparameters, and network structures used in the reconstruction. Furthermore, we also demonstrated that the proposed methods achieved competitive results without any pre-training of the network at all, that is, using randomly initialized network weights during testing. The proposed iterative reconstruction algorithm also has empirical convergence with and without network pre-training. The proposed Noise2Noise reconstruction method can achieve promising image quality in low-dose CT image reconstruction. The method works both with and without pre-training, and only noisy data are required for pre-training.

A CT reconstruction method based on constrained data fidelity range estimation.

For the CT iterative reconstruction, choosing the parameters of different regularization terms has been a difficult problem. Transforming the reconstruction problem into constrained optimization can solve this problem, but determining the constraint range and accurately solving it remains a challenge. This paper proposes a CT reconstruction method based on constrained data fidelity term, which estimates the distribution of the constraint function by Taylor expansion to determine the constraint range. We respectively use Douglas-Rachford splitting (DRS) and Projection-based primal-dual algorithm (PPD) to split the reconstruction problem and solve the data fidelity subproblem. This method can accurately estimate the constrained range of data fidelity terms to ensure reconstruction accuracy and use different regularization terms for reconstruction without parameter adjustment. Three regularization terms are used for reconstruction experiments, and simulation results show that the proposed method can converge stably, and its reconstruction quality is better than the filtered back-projection.

A Novel Experimental Strategy for In situ SR-CT Mechanical Loading Test

In synchrotron radiation computed tomography (SR-CT) of mechanical loading process, accurate reconstruction of finite angle projection information shielded by mechanical loading devices is a bottleneck problem. For accurate characterization, an experimental strategy for in situ SR-CT mechanical loading test was proposed in this manuscript, which was called "finite angle CT reconstruction method based on tilted sampling information recovery" or TSIR-CT. Effectively recovering the information lost in finite angle CT is a fundamental solution. From the new perspective of the frequency domain, the tilt sampled projection information was utilized to recover the missing angle information. A series of numerical reconstruction experiments were carried out to verify the new method. It was indicated that the reconstruction results of TSIR-CT method have been improved quantitatively comparing with filtered back projection (FBP) and algebraic reconstruction technique (ART) method. For the 506th layer from top end of the 3D test model, the standardized covariance criterion for evaluating similarity was increased from 0.8400 of FBP and 0.9148 of ART to 0.9357, respectively. Finally, this new method was validated using actual SR-CT data and demonstrated effective improvement of reconstruction quality. This new method is suitable for in situ SR-CT mechanical loading test.

Research on High Sparse Sampling CT Image Reconstruction Algorithm Based on Convolutional Neural Networks

CT plays an important role in medical diagnosis and industrial nondestructive testing. How to reduce the radiation to patients in the process of CT scanning has become a hot spot. An effective way is to use sparse sampling projection data to reconstruct the CT image. However, the image obtained by using the traditional CT reconstruction algorithm to deal with the sparse projection data has serious image distortion and artifact. This paper presents a CT reconstruction method based on CNN. This method learns the mapping relationship between the sparse projection data and the complete projection data in the training database, and uses the learned result to process the sparse sampling projection data. FBP algorithm is used to get the high resolution CT image. This paper uses CNN to carry out an end-to-end learning to repair the projection sinusoidal image with missing angle and improve the quality of CT image reconstruction.

A deep learning-based lung nodule density classification and segmentation method and its effectiveness under different CT reconstruction algorithms

Objective: To evaluate the diagnostic value of the lung nodule classification and segmentation algorithm based on deep learning among different CT reconstruction algorithms. Methods: Chest CT of 363 patients from June 2019 to September 2019 in Radiology Department of Tianjin Medical University Chu Hsien-I Memorial Hospital were retrospectively collected in this study, each of which consisted of images by three different reconstruction methods (lung reconstruction, mediastinal reconstruction, bone reconstruction).These collected data were used as testing set and a total of 4 185 Chest CTs including the public data set and the constructed private data set were used as the training set. A model combines 3D deep convolutional neural network and recurrent neural network under a multi-task joint learning algorithm for lung nodule classification and segmentation were constructed. The well-trained method was tested on 363 test cases using two metrics, i.e., the accuracy of the density classification and the Dice coefficient of nodule segmentation. The performances under three reconstruction methods were statistically analyzed according to the variance analysis among three different reconstruction methods. Results: The average classification accuracies of the nodule under three reconstruction methods were 98.67%±5.70%, 98.38%±6.61% and 97.89%±7.32%. Specifically, the accuracies of the solid nodules under three reconstruction methods were 98.79%±5.58%, 98.49%±6.89% and 97.90%±7.41% and the accuracies of the sub-solid nodules were 97.57%±10.19%, 98.52%±7.77% and 98.52%±7.77%. There was no significant difference in the classification accuracy of pulmonary nodules under three different reconstruction algorithms (all P>0.05). The average Dice coefficients of nodule segmentation was 79.87%±5.78%, 79.02%±6.04% and 79.31%±5.95%. There was no significant difference in the average Dice coefficients of nodule segmentation under three different reconstruction algorithms (all P>0.05). Conclusion: Deep learning algorithm which combined with 3D convolutional neural network and recurrent neural network has demonstrated relatively stable in classification and segmentation of lung nodules under different CT reconstruction method.

Low-dose cone-beam CT (LD-CBCT) reconstruction for image-guided radiation therapy (IGRT) by three-dimensional dual-dictionary learning

BackgroundTo develop a low-dose cone beam CT (LD-CBCT) reconstruction method named simultaneous algebraic reconstruction technique and dual-dictionary learning (SART-DDL) joint algorithm for image guided radiation therapy (IGRT) and evaluate its imaging quality and clinical application ability.MethodsIn this retrospective study, 62 CBCT image sets from February 2018 to July 2018 at west china hospital were randomly collected from 42 head and neck patients (mean [standard deviation] age, 49.7 [11.4] years, 12 females and 30 males). All image sets were retrospectively reconstructed by SART-DDL (resultant D-CBCT image sets) with 18% less clinical raw projections. Reconstruction quality was evaluated by quantitative parameters compared with SART and Total Variation minimization (SART-TV) joint reconstruction algorithm with paired t test. Five-grade subjective grading evaluations were done by two oncologists in a blind manner compared with clinically used Feldkamp-Davis-Kress algorithm CBCT images (resultant F-CBCT image sets) and the grading results were compared by paired Wilcoxon rank test. Registration results between D-CBCT and F-CBCT were compared. D-CBCT image geometry fidelity was tested.ResultsThe mean peak signal to noise ratio of D-CBCT was 1.7 dB higher than SART-TV reconstructions (P < .001, SART-DDL vs SART-TV, 36.36 ± 0.55 dB vs 34.68 ± 0.28 dB). All D-CBCT images were recognized as clinically acceptable without significant difference with F-CBCT in subjective grading (P > .05). In clinical registration, the maximum translational and rotational difference was 1.8 mm and 1.7 degree respectively. The horizontal, vertical and sagittal geometry fidelity of D-CBCT were acceptable.ConclusionsThe image quality, geometry fidelity and clinical application ability of D-CBCT are comparable to that of the F-CBCT for head-and-neck patients with 18% less projections by SART-DDL.