CBCT Projections Research Articles

Investigating the characteristics of the mandibular canal in cone beam CT

CONTEXT: The mandibular canal (MC) is an essential landmark that should be considered before any surgeries. Therefore, accurately assessing the location and characteristics of the MC in cone beam computed tomography (CBCT) imaging is very important. AIMS: To determine the characteristics of the MC in relation to adjacent anatomical structures in CBCT projections. SETTINGS AND DESIGN: The convenience sampling method. METHODS AND MATERIAL: This was a retrospective study of 112 CBCT images of Vietnamese patients aged 18 to 69 years, taken for clinical indications between 2018 and 2023. The evaluation was carried out by comparing and arranging the anatomical structures of different planes in three-dimensional space to assess and measure relevant dimensions. STATISTICAL ANALYSIS USED: Independent samples T-test. RESULTS: The average diameter of the MC from the apex of the second premolar to the distal apex of the second molar, if there were no missing teeth in this segment, was 2.58 ± 0.52 mm (right) and 2.55 ± 0.54 mm (left). If there were a missing tooth in this segment, the measurements were 2.51 ± 0.79 mm (right) and 2.47 ± 0.45 mm (left). The difference between the two sides regarding the presence or absence of a missing tooth was not statistically significant. CONCLUSIONS: The precise localization of the MC related to the tooth apex and the diameter of the MC can vary in each person. CBCT indications should be considered when establishing treatment planning to avoid damaging the inferior alveolar nerve in the MC.

Non-coplanar CBCT image reconstruction using a generative adversarial network for non-coplanar radiotherapy.

To develop a non-coplanar cone-beam computed tomography (CBCT) image reconstruction method using projections within a limited angle range for non-coplanar radiotherapy. A generative adversarial network (GAN) was utilized to reconstruct non-coplanar CBCT images. Data from 40 patients with brain tumors and two head phantoms were used in this study. In the training stage, the generator of the GAN used coplanar CBCT and non-coplanar projections as the input, and an encoder with a dual-branch structure was utilized to extract features from the coplanar CBCT and non-coplanar projections separately. Non-coplanar CBCT images were then reconstructed using a decoder by combining the extracted features. To improve the reconstruction accuracy of the image details, the generator was adversarially trained using a patch-based convolutional neural network as the discriminator. A newly designed joint loss was used to improve the global structure consistency rather than the conventional GAN loss. The proposed model was evaluated using data from eight patients and two phantoms at four couch angles (±45°,±90°) that are most commonly used for brain non-coplanar radiotherapy in our department. The reconstructed accuracy was evaluated by calculating the root mean square error (RMSE) and an overall registration error ε, computed by integrating the rigid transformation parameters. In both patient data and phantom data studies, the qualitative and quantitative metrics results indicated that±45° couch angle models performed better than±90° couch angle models and had statistical differences. In the patient data study, the mean RMSE and ε values of couch angle at 45°, -45°, 90°, and -90° were 58.5 HU and 0.42mm, 56.8 HU and 0.41mm, 73.6 HU and 0.48mm, and 65.3 HU and 0.46mm, respectively. In the phantom data study, the mean RMSE and ε values of couch angle at 45°, -45°, 90°, and -90° were 91.2 HU and 0.46mm, 95.0 HU and 0.45mm, 114.6 HU and 0.58mm, and 102.9 HU and 0.52mm, respectively. The results show that the reconstructed non-coplanar CBCT images can potentially enable intra-treatment three-dimensional position verification for non-coplanar radiotherapy.

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.

Spatiotemporal correlation enhanced real-time 4D-CBCT imaging using convolutional LSTM networks.

To enhance the accuracy of real-time four-dimensional cone beam CT (4D-CBCT) imaging by incorporating spatiotemporal correlation from the sequential projection image into the single projection-based 4D-CBCT estimation process. We first derived 4D deformation vector fields (DVFs) from patient 4D-CT. Principal component analysis (PCA) was then employed to extract distinctive feature labels for each DVF, focusing on the first three PCA coefficients. To simulate a wide range of respiratory motion, we expanded the motion amplitude and used random sampling to generate approximately 900 sets of PCA labels. These labels were used to produce 900 simulated 4D-DVFs, which in turn deformed the 0% phase 4D-CT to obtain 900 CBCT volumes with continuous motion amplitudes. Following this, the forward projection was performed at one angle to get all of the digital reconstructed radiographs (DRRs). These DRRs and the PCA labels were used as the training data set. To capture the spatiotemporal correlation in the projections, we propose to use the convolutional LSTM (ConvLSTM) network for PCA coefficient estimation. For network testing, when several online CBCT projections (with different motion amplitudes that cover the full respiration range) are acquired and sent into the network, the corresponding 4D-PCA coefficients will be obtained and finally lead to a full online 4D-CBCT prediction. A phantom experiment is first performed with the XCAT phantom; then, a pilot clinical evaluation is further conducted. Results on the XCAT phantom and the patient data show that the proposed approach outperformed other networks in terms of visual inspection and quantitative metrics. For the XCAT phantom experiment, ConvLSTM achieves the highest quantification accuracy with MAPE(Mean Absolute Percentage Error), PSNR (Peak Signal-to-Noise Ratio), and RMSE(Root Mean Squared Error) of 0.0459, 64.6742, and 0.0011, respectively. For the patient pilot clinical experiment, ConvLSTM also achieves the best quantification accuracy with that of 0.0934, 63.7294, and 0.0019, respectively. The quantification evaluation labels that we used are 1) the Mean Absolute Error (MAE), 2) the Normalized Cross Correlation (NCC), 3)the Structural Similarity Index Measurement(SSIM), 4)the Peak Signal-to-Noise Ratio (PSNR), 5)the Root Mean Squared Error(RMSE), and 6) the Absolute Percentage Error (MAPE). The spatiotemporal correlation-based respiration motion modeling supplied a potential solution for accurate real-time 4D-CBCT reconstruction.

1618: Setup validation for respiratory gated liver proton therapy by detailed analysis of CBCT projections

1618: Setup validation for respiratory gated liver proton therapy by detailed analysis of CBCT projections

Feasibility of bone marrow edema detection using dual-energy cone-beam computed tomography.

Dual-energy (DE) detection of bone marrow edema (BME) would be a valuable new diagnostic capability for the emerging orthopedic cone-beam computed tomography (CBCT) systems. However, this imaging task is inherently challenging because of the narrow energy separation between water (edematous fluid) and fat (health yellow marrow), requiring precise artifact correction and dedicated material decomposition approaches. We investigate the feasibility of BME assessment using kV-switching DE CBCT with a comprehensive CBCT artifact correction framework and a two-stage projection- and image-domain three-material decomposition algorithm. DE CBCT projections of quantitative BME phantoms (water containers 100-165mm in size with inserts presenting various degrees of edema) and an animal cadaver model of BME were acquired on a CBCT test bench emulating the standard wrist imaging configuration of a Multitom Rax twin robotic x-ray system. The slow kV-switching scan protocol involved a 60kV low energy (LE) beam and a 120kV high energy (HE) beam switched every 0.5° over a 200° angular span. The DE CBCT data preprocessing and artifact correction framework consisted of (i) projection interpolation onto matched LE and HE projections views, (ii) lag and glare deconvolutions, and (iii) efficient Monte Carlo (MC)-based scatter correction. Virtual non-calcium (VNCa) images for BME detection were then generated by projection-domain decomposition into an Aluminium (Al) and polyethylene basis set (to remove beam hardening) followed by three-material image-domain decomposition into water, Ca, and fat. Feasibility of BME detection was quantified in terms of VNCa image contrast and receiver operating characteristic (ROC) curves. Robustness to object size, position in the field of view (FOV) and beam collimation (varied 20-160mm) was investigated. The MC-based scatter correction delivered>69% reduction of cupping artifacts for moderate to wide collimations (>80mm beam width), which was essential to achieve accurate DE material decomposition. In a forearm-sized object, a 20% increase in water concentration (edema) of a trabecular bone-mimicking mixture presented as ∼15 HU VNCa contrast using 80-160mm beam collimations. The variability with respect to object position in the FOV was modest (<15% coefficient of variation). The areas under the ROC curve were>0.9. A femur-sized object presented a somewhat more challenging task, resulting in increased sensitivity to object positioning at 160mm collimation. In animal cadaver specimens, areas of VNCa enhancement consistent with BME were observed in DE CBCT images in regions of MRI-confirmed edema. Our results indicate that the proposed artifact correction and material decomposition pipeline can overcome the challenges of scatter and limited spectral separation to achieve relatively accurate and sensitive BME detection in DE CBCT. This study provides an important baseline for clinical translation of musculoskeletal DE CBCT to quantitative, point-of-care bone health assessment.

Super Resolution Dual-Energy Cone-Beam CT Imaging With Dual-Layer Flat-Panel Detector.

In flat-panel detector (FPD) based cone-beam computed tomography (CBCT) imaging, the native receptor array is usually binned into a smaller matrix size. By doing so, the signal readout speed could be increased by 4-9 times at the expense of a spatial resolution loss of 50%-67%. Clearly, such manipulation poses a key bottleneck in generating high spatial and high temporal resolution CBCT images at the same time. In addition, the conventional FPD is also difficult in generating dual-energy CBCT images. In this paper, we propose an innovative super resolution dual-energy CBCT imaging method, named as suRi, based on dual-layer FPD (DL-FPD) to overcome these aforementioned difficulties at once. With suRi, specifically, a 1D or 2D sub-pixel (half pixel in this study) shifted binning is applied instead of the conventionally aligned binning to double the spatial sampling rate during the dual-energy data acquisition. As a result, the suRi approach provides a new strategy to enable high spatial resolution CBCT imaging while at high readout speed. Moreover, a penalized likelihood material decomposition algorithm is developed to directly reconstruct the high resolution bases from these dual-energy CBCT projections containing sub-pixel shifts. Numerical and physical experiments are performed to validate this newly developed suRi method with phantoms and biological specimen. Results demonstrate that suRi can significantly improve the spatial resolution of the CBCT image. We believe this developed suRi method would greatly enhance the imaging performance of the DL-FPD based dual-energy CBCT systems in future.

Simulation-driven training of vision transformers enables metal artifact reduction of highly truncated CBCT scans.

Due to the high attenuation of metals, severe artifacts occur in cone beam computed tomography (CBCT). The metal segmentation in CBCT projections usually serves as a prerequisite for metal artifact reduction (MAR)algorithms. The occurrence of truncation caused by the limited detector size leads to the incomplete acquisition of metal masks from the threshold-based method in CBCT volume. Therefore, segmenting metal directly in CBCT projections is pursued in thiswork. Since the generation of high quality clinical training data is a constant challenge, this study proposes to generate simulated digital radiographs (dataI) based on real CT data combined with self-designed computer aided design (CAD) implants. In addition to the simulated projections generated from 3D volumes, 2D x-ray images combined with projections of implants serve as the complementary data set (dataII) to improve the network performance. In this work, SwinConvUNet consisting of shift window (Swin) vision transformers (ViTs) with patch merging as encoder is proposed for metalsegmentation. The model's performance is evaluated on accurately labeled test datasets obtained from cadaver scans as well as the unlabeled clinical projections. When trained on the dataI only, the convolutional neural network (CNN) encoder-based networks UNet and TransUNet achieve only limited performance on the cadaver test data, with an average dice score of 0.821 and 0.850. After using both dataII and dataI during training, the average dice scores for the two models increase to 0.906 and 0.919, respectively. By replacing the CNN encoder with Swin transformer, the proposed SwinConvUNet reaches an average dice score of 0.933 for cadaver projections when only trained on the dataI. Furthermore, SwinConvUNet has the largest average dice score of 0.953 for cadaver projections when trained on the combined dataset. Our experiments quantitatively demonstrate the effectiveness of the combination of the projections simulated under two pathways for network training. Besides, the proposed SwinConvUNet trained on the simulated projections performs state-of-the-art, robust metal segmentation as demonstrated on experiments on cadaver and clinical data sets. With the accurate segmentations from the proposed model, MAR can be conducted even for highly truncated CBCTscans.

Real Time 4D-Cone Beam CT via Single-Angle Projection with Dual Attention Mechanism

The hypothesis is that dual attention integrated Residual Network (DA-ResNet) together with a Principal Component Analysis (PCA) model accurately maps the measured on-treatment CBCT projections from different phases to each phase's 3D-CBCT real timely. Finally, it achieves real time 4D-CBCT. The DA-ResNet contains a CBAM (Convolutional Block Attention Module) and a SPA (Spatial Pyramid Structure). Hundreds of forward projections from one view of patient 4D-CT were simulated to train DA-ResNet. The extracted motion features were expressed as PCA coefficients. To test DA-ResNet performance, several single measured on-treatment projection is sent into it and the network output several groups of PCA coefficients real timely. Each group of PCA coefficient stands for the corresponding deformation field between the 0% phase and the predicted phase that the PCA label stands for. In labels such as Mean Absolute Error (MAE), R square, Normalized Cross Correlation (NCC), Structural Similarity Index Measure (SSIM) were used to evaluate DA-ResNet performance with other state-of-the-art networks such as CNN/Unet/ResNet. DA-ResNet outperforms other popular networks on all of the quantification labels, with MAE (14.41 ± 8.85), R square (0.95 ± 0.07) in PCA coefficient prediction, and NCC (0.9981 ± 0.0006), SSIM (0.98 ± 0.04) in image reconstruction. Compared with conventional ResNet, DA-ResNet reduced MAE by 79%. Meanwhile R square rises from 0.88 to 0.95, NCC rises from 0.996 to 0.998, SSIM rises from 0.94 to 0.98. DA-ResNet offers a solution for real time on-treatment 4D-CBCT image guidance by using single 2D on-line projections. It offers better motion prediction and image reconstruction accuracy compared with traditional deep learning networks (e.g., CNN/Unet/ResNet). The initial results are promising. The prospect of clinical application needs further clinical verification.

SEA-Net: Structure-Enhanced Attention Network for Limited-Angle CBCT Reconstruction of Clinical Projection Data

Limited-angle CBCT (LA-CBCT) is of great clinical interest, because the scanning time and the patient dose are proportional to the scanning range of gantry rotation angles of CBCT. However, the image reconstruction for LA-CBCT remains technically challenging, which suffers from severe wedge artifacts and image distortions. This work aims to improve LA-CBCT by developing deep learning (DL) methods for real clinical CBCT projection data, which is the first feasibility study of clinical-projection-data-based LA-CBCT, to the best of our knowledge. Targeting at real clinical projection data, we have explored various DL methods such as image/data/hybrid-domain methods and finally developed a so-called Structure-Enhanced Attention Network (SEA-Net) method that has the best image quality from clinical projection data among the DL methods we have implemented. Specifically, the proposed SEA-Net employs a specialized structure enhancement sub-network to promote texture preservation. Based on the observation that the distribution of wedge artifacts in reconstruction images is non-uniform, the spatial attention module is utilized to emphasize the relevant regions while ignores the irrelevant ones, which leads to more accurate texture restoration. SEA-Net was validated in comparison with analytic (FDK), iterative (TV), image-domain DL (DDNet and FED-INet, data-domain DL (DCAR), dual-domain DL (Sam'Net), and various unrolling DL (hdNet, CTNet, FSR-Net, CasRedSCAN) methods. Among all methods, the SEA-Net had the best image reconstruction quality as quantified by root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), for various LA-CBCT problems of 90°-180° projection data. In addition, LA-CBCT via SEA-Net provided comparable accuracy for both patient setup (quantified by image registration accuracy from planning CT (pCT) to CBCT) and dose calculation (see the table), with full-view CBCT. We explored various DL methods and developed an image-domain-based method termed SEA-Net that provided the best image quality for clinical projection data. To the best of our knowledge, this is the first feasibility study of the real clinical-projection-data-based LA-CBCT. Moreover, LA-CBCT via SEA-Net can potentially provide comparable accuracy for patient setup and dose calculation, with full-view CBCT.

2V-CBCT: Two-Orthogonal-Projection Based CBCT Reconstruction and Dose Calculation from Real CBCT Projection Data

Not all radiation therapy (RT) treatments/fractions have CBCT acquired, but two orthogonal projections (i.e., KV radiography) are always available. This work demonstrates the feasibility of two-orthogonal-projection-based CBCT (2V-CBCT) reconstruction and dose calculation for RT from real CBCT projection data, which is the first 2V-CBCT feasibility study using real projection data, to the best of our knowledge. 2V-CBCT is a severely ill-posed inverse problem for which we propose a coarse-to-fine learning strategy. First, a 3D deep neural network that can extract and exploit the inter-slice and intra-slice information is adopted to predict the initial 3D volumes. Then, a 2D deep neural network is utilized to fine-tune the initial 3D volumes slice-by-slice. During the fine-tuning stage, a perceptual loss based on multi-frequency features is employed to enhance the image reconstruction. Dose calculation results from both photon and proton RT demonstrate that 2V-CBCT provides comparable accuracy with full-view CBCT based on real projection data. The proposed method was evaluated on real HN data acquired from on-board CBCT scanners rather than the low-resolution simulated data or down-sampled data. Both visual assessment and quantitative analysis demonstrate that the proposed coarse-to-fine learning strategy has the potential to produce satisfactory volumetric images from two orthogonal projections. Furthermore, we assessed the utility of 2V-CBCT in RT. The results show that the dose distribution maps, dose-volume histograms, and dose parameters calculated using 2V-CBCT have comparable accuracy with the counterparts calculated using the corresponding full-view CBCT for both photon and proton RT. In the table, the methods under comparison are pCT (planning CT), FV-CBCT (CBCT reconstructed with full-view projection data), and 2V-CBCT (CBCT reconstructed with two orthogonal projections). A new effective 2V-CBCT reconstruction method is proposed and validated using real CBCT projection data, which can potentially provide comparable dose calculation accuracy for both photon and proton RT.

Improved compact representation method for Monte Carlo phase spaces

Phase space files used in particle Monte Carlo simulations can be large, and thereby awkward to distribute and slow to load into memory. To circumvent this, compact source models and phase space representations have been developed. In this study, the aim is to increase the versatility of a previously presented method, based on principal component analysis whitening, by evaluating several modifications to the method on simulated cone-beam computed tomography projection images. Scaling the phase space components before whitening was seen to give the largest improvement and allowed for accurate modelling of phase spaces with a shifted detector and/or a bowtie filter present, configurations where the original method struggled. The modified method reproduced results using the original phase space to within a few percent both in energy fluence on the detector and in simulated projection and scatter images of a patient pelvis, without obvious position-dependence of the errors. Hence, the modified method was deemed sufficiently accurate for most applications relying on cone-beam computed tomography simulations. The final aim is to use the more versatile method for a broader range of phase space-based applications such as linear accelerator beam simulations and room shielding calculations.

An unsupervised dual contrastive learning framework for scatter correction in cone-beam CT image

Purpose:Cone-beam computed tomography (CBCT) is widely utilized in modern radiotherapy; however, CBCT images exhibit increased scatter artifacts compared to planning CT (pCT), compromising image quality and limiting further applications. Scatter correction is thus crucial for improving CBCT image quality. Methods:In this study, we proposed an unsupervised contrastive learning method for CBCT scatter correction. Initially, we transformed low-quality CBCT into high-quality synthetic pCT (spCT) and generated forward projections of CBCT and spCT. By computing the difference between these projections, we obtained a residual image containing image details and scatter artifacts. Image details primarily comprise high-frequency signals, while scatter artifacts consist mainly of low-frequency signals. We extracted the scatter projection signal by applying a low-pass filter to remove image details. The corrected CBCT (cCBCT) projection signal was obtained by subtracting the scatter artifacts projection signal from the original CBCT projection. Finally, we employed the FDK reconstruction algorithm to generate the cCBCT image. Results:To evaluate cCBCT image quality, we aligned the CBCT and pCT of six patients. In comparison to CBCT, cCBCT maintains anatomical consistency and significantly enhances CT number, spatial homogeneity, and artifact suppression. The mean absolute error (MAE) of the test data decreased from 88.0623 ± 26.6700 HU to 17.5086 ± 3.1785 HU. The MAE of fat regions of interest (ROIs) declined from 370.2980 ± 64.9730 HU to 8.5149 ± 1.8265 HU, and the error between their maximum and minimum CT numbers decreased from 572.7528 HU to 132.4648 HU. The MAE of muscle ROIs reduced from 354.7689 ± 25.0139 HU to 16.4475 ± 3.6812 HU. We also compared our proposed method with several conventional unsupervised synthetic image generation techniques, demonstrating superior performance. Conclusions:Our approach effectively enhances CBCT image quality and shows promising potential for future clinical adoption.

Development of a prediction model for target positioning by using diaphragm waveforms extracted from CBCT projection images.

To develop a prediction model (PM) for target positioning using diaphragm waveforms extracted from CBCT projection images. Nineteen patients with lung cancer underwent orthogonal rotational kV x-ray imaging lasting 70 s. IR markers placed on their abdominal surfaces and an implanted gold marker located nearest to the tumor were considered as external surrogates and the target, respectively. Four different types of regression-based PM were trained using surrogate motions and target positions for the first 60 s, as follows: Scenario A: Based on the clinical scenario, 3D target positions extracted from projection images were used as they were (PMCL ). Scenario B: The short-arc 4D-CBCT waveform exhibiting eight target positions was obtained by averaging the target positions in Scenario A. The waveform was repeated for 60 s (W4D-CBCT ) by adapting to the respiratory phase of the external surrogate. W4D-CBCT was used as the target positions (PM4D-CBCT ). Scenario C: The Amsterdam Shroud (AS) signal, which depicted the diaphragm motion in the superior-inferior direction was extracted from the orthogonal projection images. The amplitude and phase of W4D-CBCT were corrected based on the AS signal. The AS-corrected W4D-CBCT was used as the target positions (PMAS-4D-CBCT ). Scenario D: The AS signal was extracted from single projection images. Other processes were the same as in Scenario C. The prediction errors were calculated for the remaining 10 s. The 3D prediction error within 3mm was 77.3% for PM4D-CBCT , which was 12.8% lower than that for PMCL . Using the diaphragm waveforms, the percentage of errors within 3mm improved by approximately 7% to 84.0%-85.3% for PMAS-4D-CBCT in Scenarios C and D, respectively. Statistically significant differences were observed between the prediction errors of PM4D-CBCT and PMAS-4D-CBCT . PMAS-4D-CBCT outperformed PM4D-CBCT , proving the efficacy of the AS signal-based correction. PMAS-4D-CBCT would make it possible to predict target positions from 4D-CBCT images without gold markers.

Enhancing the target visibility with synthetic target specific digitally reconstructed radiograph for intrafraction motion monitoring: A proof-of-concept study.

Intrafraction motion monitoring in External Beam Radiation Therapy (EBRT) is usually accomplished by establishing a correlation between the tumor and the surrogates such as an external infrared reflector, implanted fiducial markers, or patient skin surface. These techniques either have unstable surrogate-tumor correlation or are invasive. Markerless real-time onboard imaging is a noninvasive alternative that directly images the target motion. However, the low target visibility due to overlapping tissues along the X-ray projection path makes tumor tracking challenging. To enhance the target visibility in projection images, a patient-specific model was trained to synthesize the Target Specific Digitally Reconstructed Radiograph (TS-DRR). Patient-specific models were built using a conditional Generative Adversarial Network (cGAN) to map the onboard projection images to TS-DRR. The standard Pix2Pix network was adopted as our cGAN model. We synthesized the TS-DRR based on the onboard projection images using phantom and patient studies for spine tumors and lung tumors. Using previously acquired CT images, we generated DRR and its corresponding TS-DRR to train the network. For data augmentation, random translations were applied to the CT volume when generating the training images. For the spine, separate models were trained for an anthropomorphic phantom and a patient treated with paraspinal stereotactic body radiation therapy (SBRT). For lung, separate models were trained for a phantom with a spherical tumor insert and a patient treated with free-breathing SBRT. The models were tested using Intrafraction Review Images (IMR) for the spine and CBCT projection images for the lung. The performance of the models was validated using phantom studies with known couch shifts for the spine and known tumor deformation for the lung. Both the patient and phantom studies showed that the proposed method can effectively enhance the target visibility of the projection images by mapping them into synthetic TS-DRR (sTS-DRR). For the spine phantom with known shifts of 1 mm, 2 mm, 3 mm, and 4 mm, the absolute mean errors for tumor tracking were 0.11 ± 0.05 mm in the x direction and 0.25 ± 0.08 mm in the y direction. For the lung phantom with known tumor motion of 1.8 mm, 5.8 mm, and 9 mm superiorly, the absolute mean errors for the registration between the sTS-DRR and ground truth are 0.1 ± 0.3 mm in both the x and y directions. Compared to the projection images, the sTS-DRR has increased the image correlation with the ground truth by around 83% and increased the structural similarity index measure with the ground truth by around 75% for the lung phantom. The sTS-DRR can greatly enhance the target visibility in the onboard projection images for both the spine and lung tumors. The proposed method could be used to improve the markerless tumor tracking accuracy for EBRT.

ScatterNet for projection-based 4D cone-beam computed tomography intensity correction of lung cancer patients.

Background and purpose: In radiotherapy, dose calculations based on 4D cone beam CTs (4DCBCTs) require image intensity corrections. This retrospective study compared the dose calculation accuracy of a deep learning, projection-based scatter correction workflow (ScatterNet), to slower workflows: conventional 4D projection-based scatter correction (CBCTcor) and a deformable image registration (DIR)-based method (4DvCT). Materials and methods: For 26 lung cancer patients, planning CTs (pCTs), 4DCTs and CBCT projections were available. ScatterNet was trained with pairs of raw and corrected CBCT projections. Corrected projections from ScatterNet and the conventional workflow were reconstructed using MA-ROOSTER, yielding 4DCBCTSN and 4DCBCTcor. The 4DvCT was generated by 4DCT to 4DCBCT DIR, as part of the 4DCBCTcor workflow. Robust intensity modulated proton therapy treatment plans were created on free-breathing pCTs. 4DCBCTSN was compared to 4DCBCTcor and the 4DvCT in terms of image quality and dose calculation accuracy (dose-volume-histogram parameters and / gamma analysis). Results: 4DCBCTSN resulted in an average mean absolute error of and when compared to 4DCBCTcor and 4DvCT respectively. High agreement was observed in targets with median dose differences of (4DCBCTSN-4DCBCTcor) and (4DCBCTSN-4DvCT). The gamma analysis showed high average / pass rates of for both 4DCBCTSN vs. 4DCBCTcor and 4DCBCTSN vs. 4DvCT. Conclusions: Accurate 4D dose calculations are feasible for lung cancer patients using ScatterNet for 4DCBCT correction. Average scatter correction times could be reduced from (4DCBCTcor) to , showing the clinical suitability of the proposed deep learning-based method.

Real-Time 4-D-Cone Beam CT Accurate Estimation Based on Single-Angle Projection via Dual-Attention Mechanism Residual Network

Recently rapid and efficient localization of lung tumors based on single cone beam CT (CBCT) projection has attracted great interest in the radiation oncology community. High quality 4D-CBCT benefits accurate monitoring of moving targets during radiation therapy (RT). However, conventional iterative 4D-CBCT reconstruction process is heavily time-consuming. To address these issues, a motion sensitive cascade model was proposed. The proposed cascade model is composed of dual attention mechanism together with residual network (DA-ResNet) and a principal component analysis (PCA) model. It maps single projection from different breathing phase to each phase 3D-CBCT for achieving real time 4D-CBCT. The dual attention mechanism focuses on the motion information of both low-level features and high-level features to improve accuracy and efficiency of the network. The PCA model ensures a real time motion representation scheme. Compared with state-of-the-art networks, the proposed method outperformed them in the quantification labels of mean absolute error (MAE), R-squared (R2), normalized cross correlation (NCC), and structural similarity index measure (SSIM). This experiment was verified both on simulation and clinical data to support the conclusion.

Panoramic dental tomosynthesis imaging by use of CBCT projection data

Dental CBCT and panoramic images are important imaging modalities used in dental diagnosis and treatment planning. In order to acquire a panoramic image without an additional panoramic scan, in this study, we proposed a method of reconstructing a panoramic image by extracting panoramic projection data from dental CBCT projection data. After specifying the patient’s dental arch from the patient’s CBCT image, panoramic projection data are extracted from the CBCT projection data along the appropriate panoramic scan trajectory that fits the dental arch. A total of 40 clinical human datasets and one head phantom dataset were used to test the proposed method. The clinical human dataset used in this study includes cases in which it is difficult to reconstruct panoramic images from CBCT images, such as data with severe metal artifacts or data without teeth. As a result of applying the panoramic image reconstruction method proposed in this study, we were able to successfully acquire panoramic images from the CBCT projection data of various patients. The proposed method acquires a universally applicable panoramic image that is less affected by CBCT image quality and metal artifacts by extracting panoramic projection data from dental CBCT data and reconstructing a panoramic image.

A projection-domain correction method in CBCT reconstruction for head and neck radiotherapy using cycle-GAN and nonlocal means filter.

In radiation treatments for head and neck tumors, cone-beam computed tomography (CBCT) is employed for patient positioning and dose calculation of adaptive radiotherapy. However, the quality of CBCT is degraded by the scatter and noise, majorly impacting the accuracy of patient positioning and dose calculation. To improve the quality of CBCT for patients with head and neck cancer, a projection-domain CBCT correction method was proposed using a cycle-consistent generative adversarial network (cycle-GAN) and a nonlocal means filter based on a reference digitally reconstructed radiograph (DRR). A cycle-GAN was initially trained to learn mapping from CBCT projections to a DRR using the data obtained from 30 patients. For each patient, 671 CBCT projections were measured for CBCT reconstruction. Moreover, 360 Digital Reconstructed Radiographs (DRR) were computed from each patient's planning computed tomography (CT), whose projection angles ranged from 0° to 359° with an interval of 1°. By applying the trained generator of the cycle-GAN to the unseen CBCT projection, a synthetic DRR with considerably less scatter was obtained. However, annular artifacts were observed in the CBCT reconstructed with synthetic DRR. To address this issue, a nonlocal means filter based on reference DRR was used to further correct the synthetic DRR, which corrected the synthetic DRR using the calculated DRR as a reference image. Finally, the CBCT with no annular artifact and little noise was reconstructed with the corrected synthetic DRR. The proposed method was tested using the data of six patients. The corrected synthetic DRR and CBCT were compared with the corresponding real DRR and CT images. The structural preservation ability of the proposed method was evaluated using the Dice coefficients of the automatically extracted nasal cavity. Moreover, The image quality of CBCT corrected with the proposed method was objectively assessed with an five-point human scoring system and compared with CT, original CBCT and CBCT corrected with other strategies. The mean absolute value (MAE) of the relative error between the corrected synthetic and real DRR was <8%. The MAE between the corrected CBCT and corresponding CT was <30 HU. Moreover, the Dice coefficient of nasal cavity between the corrected CBCT image and the original image exceeded 98.8 for all the patients. Last but not least, the objective assessment of image qualityshowed the proposed method had an average score of 4.2 in overall image quality, which was higher than that of the original CBCT, CBCT reconstructed with synthetic DRR, and CBCT reconstructed with projections filtered with NLMF only. The proposed method can considerably improve the CBCT image quality with little anatomical distortion, improving the accuracy of radiotherapy for head and neck patients. This article is protected by copyright. All rights reserved.

Self-supervised denoising of projection data for low-dose cone-beam CT.

Convolutional neural networks(CNNs) have shown promising results in image denoising tasks. While most existing CNN-based methods depend on supervised learning by directly mapping noisy inputs to clean targets, high-quality references are often unavailable for interventional radiology such as cone-beam computed tomography(CBCT). In this paper, we propose a novel self-supervised learning method that reduces noise in projections acquired by ordinary CBCTscans. With a network that partially blinds input, we are able to train the denoising model by mapping the partially blinded projections to the original projections. Additionally, we incorporate noise-to-noise learning into the self-supervised learning by mapping the adjacent projections to the original projections. With standard image reconstruction methods such as FDK-type algorithms, we can reconstruct high-quality CBCT images from the projections denoised by our projection-domain denoising method. In the head phantom study, we measure peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values of the proposed method along with the other denoising methods and uncorrected low-dose CBCT data for a quantitative comparison both in projection and image domains. The PSNR and SSIM values of our self-supervised denoising approach are 27.08 and 0.839, whereas those of uncorrected CBCT images are 15.68 and 0.103, respectively. In the retrospective study, we assess the quality of interventional patient CBCT images to evaluate the projection-domain and image-domain denoising methods. Both qualitative and quantitative results indicate that our approach can effectively produce high-quality CBCT images with low-dose projections in the absence of duplicate clean or noisyreferences. Our self-supervised learning strategy is capable of restoring anatomical information while efficiently removing noise in CBCT projection data.