3D Cone-beam CT Images Research Articles

DPI-MoCo: Deep Prior Image Constrained Motion Compensation Reconstruction for 4D CBCT.

4D cone-beam computed tomography (CBCT) plays a critical role in adaptive radiation therapy for lung cancer. However, extremely sparse sampling projection data will cause severe streak artifacts in 4D CBCT images. Existing deep learning (DL) methods heavily rely on large labeled training datasets which are difficult to obtain in practical scenarios. Restricted by this dilemma, DL models often struggle with simultaneously retaining dynamic motions, removing streak degradations, and recovering fine details. To address the above challenging problem, we introduce a Deep Prior Image Constrained Motion Compensation framework (DPI-MoCo) that decouples the 4D CBCT reconstruction into two sub-tasks including coarse image restoration and structural detail fine-tuning. In the first stage, the proposed DPI-MoCo combines the prior image guidance, generative adversarial network, and contrastive learning to globally suppress the artifacts while maintaining the respiratory movements. After that, to further enhance the local anatomical structures, the motion estimation and compensation technique is adopted. Notably, our framework is performed without the need for paired datasets, ensuring practicality in clinical cases. In the Monte Carlo simulation dataset, the DPI-MoCo achieves competitive quantitative performance compared to the state-of-the-art (SOTA) methods. Furthermore, we test DPI-MoCo in clinical lung cancer datasets, and experiments validate that DPI-MoCo not only restores small anatomical structures and lesions but also preserves motion information.

Artifact Reduction in 3D and 4D Cone-Beam Computed Tomography Images With Deep Learning: A Review

Artifact Reduction in 3D and 4D Cone-Beam Computed Tomography Images With Deep Learning: A Review

Faster and lower dose imaging: evaluating adaptive, constant gantry velocity and angular separation in fast low-dose 4D cone beam CT imaging.

The adoption of four-dimensional cone beam computed tomography (4DCBCT) for image-guided lung cancer radiotherapy is increasing, especially for hypofractionated treatments. However, the drawbacks of 4DCBCT include long scan times (∼240s), inconsistent image quality, higher imaging dose than necessary, and streaking artifacts. With the emergence of linear accelerators that can acquire 4DCBCT scans in a short period of time (9.2s) there is a need to examine the impact that these very fast gantry rotations have on 4DCBCT image quality. This study investigates the impact of gantry velocity and angular separation between x-ray projections on image quality and its implication for fast low-dose 4DCBCT with emerging systems, such as the Varian Halcyon that provide fast gantry rotation and imaging. Large and uneven angular separation between x-ray projections is known to reduce 4DCBCT image quality through increased streaking artifacts. However, it is not known when angular separation starts degrading image quality. The study assesses the impact of constant and adaptive gantry velocity and determines the level when angular gaps impair image quality using state-of-the-art reconstruction methods. This study considers fast low-dose 4DCBCT acquisitions (60-80s, 200-projection scans). To assess the impact of adaptive gantry rotations, the angular position of x-ray projections from adaptive 4DCBCT acquisitions from a 30-patient clinical trial were analyzed (referred to as patient angular gaps). To assess the impact of angular gaps, variable and static angular gaps (20°, 30°, 40°) were introduced into evenly separated 200 projections (ideal angular separation). To simulate fast gantry rotations, which are on emerging linacs, constant gantry velocity acquisitions (9.2s, 60s, 120s, 240s) were simulated by sampling x-ray projections at constant intervals using the patient breathing traces from the ADAPT clinical trial (ACTRN12618001440213). The 4D Extended Cardiac-Torso (XCAT) digital phantom was used to simulate projections to remove patient-specific image quality variables. Image reconstruction was performed using Feldkamp-Davis-Kress (FDK), McKinnon-Bates (MKB), and Motion-Compensated-MKB (MCMKB) algorithms. Image quality was assessed using Structural Similarity-Index-Measure (SSIM), Contrast-to-Noise-Ratio (CNR), Signal-to-Noise-Ratio (SNR), Tissue-Interface-Width-Diaphragm (TIW-D), and Tissue-Interface-Width-Tumor (TIW-T). Patient angular gaps and variable angular gap reconstructions produced similar results to ideal angular separation reconstructions, while static angular gap reconstructions produced lower image quality metrics. For MCMKB-reconstructions, average patient angular gaps produced SSIM-0.98, CNR-13.6, SNR-34.8, TIW-D-1.5mm, and TIW-T-2.0mm, static angular gap 40° produced SSIM-0.92, CNR-6.8, SNR-6.7, TIW-D-5.7mm, and TIW-T-5.9mm and ideal produced SSIM-1.00, CNR-13.6, SNR-34.8, TIW-D-1.5mm, and TIW-T-2.0mm. All constant gantry velocity reconstructions produced lower image quality metrics than ideal angular separation reconstructions regardless of the acquisition time. Motion compensated reconstruction (MCMKB) produced the highest contrast images with low streaking artifacts. Very fast 4DCBCT scans can be acquired provided that the entire scan range is adaptively sampled, and motion-compensated reconstruction is performed. Importantly, the angular separation between x-ray projections within each individual respiratory bin had minimal effect on the image quality of fast low-dose 4DCBCT imaging. The results will assist the development of future 4DCBCT acquisition protocols that can now be achieved in very short time frames with emerging linear accelerators.

Four-Dimensional Cone Beam CT Imaging Using a Single Routine Scan via Deep Learning.

A novel method is proposed to obtain four-dimensional (4D) cone-beam computed tomography (CBCT) images from a routine scan in patients with upper abdominal cancer. The projections are sorted according to the location of the lung diaphragm before being reconstructed to phase-sorted data. A multiscale-discriminator generative adversarial network (MSD-GAN) is proposed to alleviate the severe streaking artifacts in the original images. The MSD-GAN is trained using simulated CBCT datasets from patient planning CT images. The enhanced images are further used to estimate the deformable vector field (DVF) among breathing phases using a deformable image registration method. The estimated DVF is then applied in the motion-compensated ordered-subset simultaneous algebraic reconstruction approach to generate 4D CBCT images. The proposed MSD-GAN is compared with U-Net on the performance of image enhancement. Results show that the proposed method significantly outperforms the total variation regularization-based iterative reconstruction approach and the method using only MSD-GAN to enhance original phase-sorted images in simulation and patient studies on 4D reconstruction quality. The MSD-GAN also shows higher accuracy than the U-Net. The proposed method enables a practical way for 4D-CBCT imaging from a single routine scan in upper abdominal cancer treatment including liver and pancreatic tumors.

Detailed dosimetric evaluation of inter-fraction and respiratory motion in lung stereotactic body radiation therapy based on daily 4D cone beam CT images

Objective. Periodic respiratory motion and inter-fraction variations are sources of geometric uncertainty in stereotactic body radiation therapy (SBRT) of pulmonary lesions. This study extensively evaluates and validates the separate and combined dosimetric effect of both factors using 4D-CT and daily 4D-cone beam CT (CBCT) dose accumulation scenarios. Approach. A first cohort of twenty early stage or metastatic disease lung cancer patients were retrospectively selected to evaluate each scenario. The planned-dose (3DRef) was optimized on a 3D mid-position CT. To estimate the dosimetric impact of respiratory motion (4DRef), inter-fractional variations (3DAcc) and the combined effect of both factors (4DAcc), three dose accumulation scenarios based on 4D-CT, daily mid-cone beam CT (CBCT) position and 4D-CBCT were implemented via CT-CT/CT-CBCT deformable image registration (DIR) techniques. Each scenario was compared to 3DRef. A separate cohort of ten lung SBRT patients was selected to validate DIR techniques. The distance discordance metric (DDM) was implemented per voxel and per patient for tumor and organs at risk (OARs), and the dosimetric impact for CT-CBCT DIR geometric errors was calculated. Main results. Median and interquartile range (IQR) of the dose difference per voxel were 0.05/2.69 Gy and −0.12/2.68 Gy for and For the IQR was considerably smaller −0.15/0.78 Gy. These findings were confirmed by dose volume histogram parameters calculated in tumor and OARs. For CT-CT/CT-CBCT DIR validation, DDM (95th percentile) was highest for heart (6.26 mm)/spinal cord (8.00 mm), and below 3 mm for tumor and the rest of OARs. The dosimetric impact of CT-CBCT DIR errors was below 2 Gy for tumor and OARs. Significance. The dosimetric impact of inter-fraction variations were shown to dominate those of periodic respiration in SBRT for pulmonary lesions. Therefore, treatment evaluation and dose-effect studies would benefit more from dose accumulation focusing on day-to-day changes then those that focus on respiratory motion.

Tooth segmentation in 3D cone-beam CT images using deep convolutional neural network

Segmentation of an individual tooth in dental radiographs has great significance in the process of orthodontics surgeries and dentistry. Machine learning techniques, especially deep convolutional neural networks can play a key role in revolutionizing the way orthodontics surgeons and dentists work. Lately, many researchers have been working on tooth segmentation in 3D volumetric dental scans with a great degree of success, but to the best of our knowledge, there is no pretrained neural network available publicly for performing tooth segmentation in 3D cone-beam dental CT scans. The methods which so far have been proposed by the researchers in this domain are based on complex multistep pipelines. This lack of the availability of a pre-trained model blocks the path for further explorations in this domain. In this research, we have produced a deep learning model for tooth segmentation from CBCT dental radiographs. The proposed model can segment teeth in CBCT scans in a single step. To train the proposed model, we obtained a dataset consisting of 70 3D CBCT volumes from a local health facility. We labeled the ground truth through a semi-automatic method and trained our neural network. The training yielded a validation accuracy of 95.57% on a binary class semantic segmentation of the 3D CBCT volumes. The model is successfully able to segment teeth, regardless of their type from the background in a single step. This eliminates the need of having a complex and lengthy pipeline which many researchers have been proposing. The proposed model can be extended by incorporating labeling schemes. The custom labeling schemes will help healthcare professionals to perform the labeling as per their needs. The produced model can also provide a basis for further research in this domain.

Comparative Assessment of the Efficacy of Two-Dimensional Digital Intraoral Radiography to Three-Dimensional Cone Beam Computed Tomography in the Diagnosis of Periapical Pathologies.

Background:Periapical intraoral radiographs are useful in assessing periapical pathologies; essential information often is not extracted owing to difficult interpretation and two-dimensional presentation of three-dimensional structures focusing on using cone beam CT (CBCT) and other advanced imaging modalities.Aims:The present study was conducted to comparatively assess the digital intraoral radiography to CBCT in the diagnosis of periapical pathologies.Materials and Methods:In 50 teeth, CBCT and digital imaging were compared in assessing periapical pathologies using CBCTPA and six-point scoring. The scores obtained were assessed and compared with statistical evaluation. The data were expressed in percentage and number and mean and standard deviation. The level of significance was kept at P < 0.05.Results:On comparing 3D CBCT and 2D digital imaging for assessing the study parameters, it was seen that resorption was seen in 33, 30, and 9 teeth, respectively, on 3, 2, and 1 rooted tooth on CBCT and 13, 16, and 7 teeth on digital radiography. The numbers of roots and root canals on CBCT were 45, 34, and 18 in 3, 2, and 1 rooted tooth and 50, 32, and 18 on digital imaging. Lesions were detected on CBCT in 16, 18, and 18 teeth on CBCT in 3, 2, and 1 rooted tooth, respectively, and in 14, 14, and 16 teeth on digital imaging. Comparing the five-point scale for lesions and CBPAI index for CBCT and digital imaging, the difference was statistically significant with P = 0.004 and <0.0001, respectively.Conclusion:The present study concludes that 3D CBCT is superior in accuracy to 2D imaging in diagnosing the periapical lesions, especially concerning multirooted teeth. Owing to its high radiation exposure and cost, 2D imaging can be incorporated in assessing periapical pathology for the single-rooted tooth.

On the evaluation of mobile target trajectory between four-dimensional computer tomography and four-dimensional cone-beam computer tomography.

PurposeFor mobile lung tumors, four‐dimensional computer tomography (4D CT) is often used for simulation and treatment planning. Localization accuracy remains a challenge in lung stereotactic body radiation therapy (SBRT) treatments. An attractive image guidance method to increase localization accuracy is 4D cone‐beam CT (CBCT) as it allows for visualization of tumor motion with reduced motion artifacts. However, acquisition and reconstruction of 4D CBCT differ from that of 4D CT. This study evaluates the discrepancies between the reconstructed motion of 4D CBCT and 4D CT imaging over a wide range of sine target motion parameters and patient waveforms.MethodsA thorax motion phantom was used to examine 24 sine motions with varying amplitudes and cycle times and seven patient waveforms. Each programmed motion was imaged using 4D CT and 4D CBCT. The images were processed to auto segment the target. For sine motion, the target centroid at each phase was fitted to a sinusoidal curve to evaluate equivalence in amplitude between the two imaging modalities. The patient waveform motion was evaluated based on the average 4D data sets.ResultsThe mean difference and root‐mean‐square‐error between the two modalities for sine motion were −0.35 ± 0.22 and 0.60 mm, respectively, with 4D CBCT slightly overestimating amplitude compared with 4D CT. The two imaging methods were determined to be significantly equivalent within ±1 mm based on two one‐sided t tests (p < 0.001). For patient‐specific motion, the mean difference was 1.5 ± 2.1 (0.8 ± 0.6 without outlier), 0.4 ± 0.3, and 0.8 ± 0.6 mm for superior/inferior (SI), anterior/posterior (AP), and left/right (LR), respectively.ConclusionIn cases where 4D CT is used to image mobile tumors, 4D CBCT is an attractive localization method due to its assessment of motion with respect to 4D CT, particularly for lung SBRT treatments where accuracy is paramount.

New 3D Cone Beam CT Imaging Parameters to Assist the Dentist in Treating Patients with Osteogenesis Imperfecta.

(1) Background: The aim of the work is to identify some imaging parameters in osteogenesis imperfecta to assist the dentist in the diagnosis, planning, and orthodontic treatment of Osteogenesis Imperfecta (OI) using 3D cone beam Computed Tomography (CBCT) and the Double Energy X-ray Absorptiometry (DEXA) technique. (2) Methods: 14 patients (9 males and 5 females; aged mean ± SD 15 ± 1.5) with a clinical-radiological diagnosis of OI were analyzed and divided into mild and moderate to severe forms. The patients’ samples were compared with a control group of 14 patients (8 males and 6 females; aged mean ± SD 15 ± 1.7), free from osteoporotic pathologies. (3) Results: The statistical analysis allowed us to collect four datasets: in the first dataset (C1 sick population vs. C1 healthy population), the t-test showed a p-value < 0.0001; in the second dataset (C2 sick population vs. C2 healthy population), the t-test showed a p-value < 0.0001; in the third dataset (parameter X of the sick population vs. parameter X of the healthy population), the t-test showed a p-value < 0.0001; in the fourth dataset the bone mineralometry (BMD) value detected by the DEXA technique compared to the C2 value of the OI affected population only) the Welch–Satterthwaite test showed a p-value < 0.0001. (4) Conclusions: The research has produced specific imaging parameters that assist the dentist in making diagnostic decisions in OI patients. This study shows that patients with OI have a characteristic chin-bearing symphysis, thinned, and narrowed towards the center, configuring it with a constant “hourglass” appearance, not reported so far in the literature by any author.

4D-AirNet: a temporally-resolved CBCT slice reconstruction method synergizing analytical and iterative method with deep learning

Four-dimensional (4D) cone-beam CT (CBCT) reconstructs temporally-resolved phases of 3D volumes often with the same amount of projection data that are meant for reconstructing a single 3D volume. 4D CBCT is a sparse-data problem that is very challenging for high-quality 4D CBCT image reconstruction. Here we develop a new method, namely 4D-AirNet, that synergizes analytical and iterative method with deep learning for high-quality temporally-resolved CBCT slice reconstruction. 4D-AirNet is an unrolling method using the optimization framework of fused analytical and iterative reconstruction (AIR), which is based on proximal forward-backward splitting (PFBS). Three different strategies are developed for 4D-AirNet: random-phase (RP), prior-guided (PG), and all-phase (AP). RP-AirNet and PG-AirNet utilize phase-by-phase training and reconstruction, while PG-AirNet also uses a prior image reconstructed with all-phase projection data. Dense connectivity is built into 4D-AirNet networks for improved reconstruction quality. In contrast, AP-AirNet trains and reconstructs all phases simultaneously. In addition, the joint regularization method of DL and conventional spatiotemporal total variation (TV) is investigated. 4D-AirNet methods were evaluated in comparison with conventional iterative (TV) and deep learning (LEARN) methods, using simulated 2D-t CBCT scans from a lung dataset with various sparse-data levels. The reconstruction results suggest 4D-AirNet methods outperform TV and LEARN, and AP-AirNet provides the best reconstruction quality overall.

Alveolar bone loss and mineralization in the pig with experimental periodontal disease

ObjectiveTo address how experimental periodontal disease affects alveolar bone mass and mineral apposition in a young pig model. Materials and methodsSeven three-month-old pigs were periodically inoculated with 4 types of periodontal bacteria, along with a ligature around the last maxillary deciduous molar for 8 weeks to induce periodontal disease (PG). Eight same-aged pigs served as the control (CG). Segmentations of 3D cone-beam CT images were performed to quantify volumes of the total alveolar bone, alveolar ridge, and all roots of the target molar. Calcein and alizarin were administered for labeling mineral apposition before euthanasia. The harvested molar blocks were sectioned and examined under epifluorescence. The inter-label distance between the two vital markers at regional bone surfaces were measured and mineral apposition rate (MAR) was calculated. ResultsA significant reduction of total alveolar bone volume was seen in PG with the major loss at the alveolar ridge. MAR was significantly higher at the root furcation region than those at both buccal and palatal ridges in CG. Compared with CG, PG animals showed more interrupted labeled bands with significantly lower MAR at the furcation region. MARs were positively associated with both the volumes of total alveolar bone and ridge in CG, but only with the total alveolar bone in PG. ConclusionsIn young growing pigs, mineral apposition is region specific. The experimental periodontal disease not only leads to alveolar bone loss, but also perturbs mineral apposition for new bone formation, thus impairing the homeostasis of alveolar bone remodeling.

TH‐EF‐207A‐05: Feasibility of Applying SMEIR Method On Small Animal 4D Cone Beam CT Imaging

Purpose:Small animal cone beam CT imaging has been widely used in preclinical research. Due to the higher respiratory rate and heat beats of small animals, motion blurring is inevitable and needs to be corrected in the reconstruction. Simultaneous motion estimation and image reconstruction (SMEIR) method, which uses projection images of all phases, proved to be effective in motion model estimation and able to reconstruct motion‐compensated images. We demonstrate the application of SMEIR for small animal 4D cone beam CT imaging by computer simulations on a digital rat model.Methods:The small animal CBCT imaging system was simulated with the source‐to‐detector distance of 300 mm and the source‐to‐object distance of 200 mm. A sequence of rat phantom were generated with 0.4 mm3 voxel size. The respiratory cycle was taken as 1.0 second and the motions were simulated with a diaphragm motion of 2.4mm and an anterior‐posterior expansion of 1.6 mm. The projection images were calculated using a ray‐tracing method, and 4D‐CBCT were reconstructed using SMEIR and FDK methods. The SMEIR method iterates over two alternating steps: 1) motion‐compensated iterative image reconstruction by using projections from all respiration phases and 2) motion model estimation from projections directly through a 2D‐3D deformable registration of the image obtained in the first step to projection images of other phases.Results:The images reconstructed using SMEIR method reproduced the features in the original phantom. Projections from the same phase were also reconstructed using FDK method. Compared with the FDK results, the images from SMEIR method substantially improve the image quality with minimum artifacts.Conclusion:We demonstrate that it is viable to apply SMEIR method to reconstruct small animal 4D‐CBCT images.

Preliminary safety and stability assessment of orthodontic miniscrew implantation guided by surgical template based on cone-beam CT images

To develop surgical templates for orthodontic miniscrew implantation based on cone-beam CT(CBCT)three-dimensional(3D)images and to evaluate the safety and stability of implantation guided by the templates. DICOM data obtained in patients who had CBCT scans taken were processed using Mimics software, and 3D images of teeth and maxillary bone were acquired. Meanwhile, 3D images of miniscrews were acquired using Solidworks software and processed with Mimics software. Virtual position of miniscrews was determined based on 3D images of teeth, bone, and miniscrews. 3D virtual templates were designed according to the virtual implantation plans. STL files were output and the real templates were fabricated with stereolithographic appliance(SLA). Postoperative CBCT scans were used to evaluate the implantation safety and the stability of miniscrews were investigated. All the templates were positioned accurately and kept stable throughout the implantation process. No root damage was found. The deviations were(1.73±0.65)mm at the corona, and(1.28±0.82)mm at the apex, respectively. The stability of miniscrews was fairly well. Surgical templates for miniscrew implantation could be acquired based on 3D CBCT images and fabricated with SLA. Implantation guided by these templates was safe and stable.

EP-2081: Impact of baseline shifts on 4D cone-beam CT images using a 4D phantom driven by lung tumor motions

EP-2081: Impact of baseline shifts on 4D cone-beam CT images using a 4D phantom driven by lung tumor motions

Evaluation of Elekta 4D cone beam CT-based automatic image registration for radiation treatment of lung cancer.

The study was aimed to evaluate the precision of Elekta four-dimensional (4D) cone beam CT (CBCT)-based automatic dual-image registrations using different landmarks for clipbox for radiation treatment of lung cancer. 30 4D CBCT scans from 15 patients were studied. 4D CBCT images were registered with reference CT images using dual-image registration: a clipbox registration and a mask registration. The image registrations performed in clinic using a physician-defined clipbox, were reviewed by physicians, and were taken as the standard. Studies were conducted to evaluate the automatic dual registrations using three kinds of landmarks for clipbox: spine, spine plus internal target volume (ITV) and lung (including as much of the lung as possible). Translational table shifts calculated from the automatic registrations were compared with those of the standard. The mean of the table shift differences in the lateral direction were 0.03, 0.03 and 0.03 cm, for clipboxes based on spine, spine plus ITV and lung, respectively. The mean of the shift differences in the longitudinal direction were 0.08, 0.08 and 0.08 cm, respectively. The mean of the shift differences in the vertical direction were 0.03, 0.03 and 0.03 cm, respectively. The automatic registrations using three different landmarks for clipbox showed similar results. One can use any of the three landmarks in 4D CBCT dual-image registration. Advance in knowledge: The study provides knowledge and recommendations for application of Elekta 4D CBCT image registration in radiation therapy of lung cancer.

SU‐E‐J‐153: Reconstructing 4D Cone Beam CT Images for Clinical QA of Lung SABR Treatments

Purpose:To verify that the planned Primary Target Volume (PTV) and Internal Gross Tumor Volume (IGTV) fully enclose a moving lung tumor volume as visualized on a pre‐SABR treatment verification 4D Cone Beam CT.Methods:Daily 3DCBCT image sets were acquired immediately prior to treatment for 10 SABR lung patients using the on‐board imaging system integrated into a Varian TrueBeam (v1.6: no 4DCBCT module available). Respiratory information was acquired during the scan using the Varian RPM system. The CBCT projections were sorted into 8 bins offline, both by breathing phase and amplitude, using in‐house software. An iterative algorithm based on total variation minimization, implemented in the open source reconstruction toolkit (RTK), was used to reconstruct the binned projections into 4DCBCT images. The relative tumor motion was quantified by tracking the centroid of the tumor volume from each 4DCBCT image. Following CT‐CBCT registration, the planning CT volumes were compared to the location of the CBCT tumor volume as it moves along its breathing trajectory. An overlap metric quantified the ability of the planned PTV and IGTV to contain the tumor volume at treatment.Results:The 4DCBCT reconstructed images visibly show the tumor motion. The mean overlap between the planned PTV (IGTV) and the 4DCBCT tumor volumes was 100% (94%), with an uncertainty of 5% from the 4DCBCT tumor volume contours. Examination of the tumor motion and overlap metric verify that the IGTV drawn at the planning stage is a good representation of the tumor location at treatment.Conclusion:It is difficult to compare GTV volumes from a 4DCBCT and a planning CT due to image quality differences. However, it was possible to conclude the GTV remained within the PTV 100% of the time thus giving the treatment staff confidence that SABR lung treatements are being delivered accurately.

IMRT Treatment Planning on 4D Geometries for the Era of Dynamic MLC Tracking

The problem addressed here was to obtain optimal and deliverable dynamic multileaf collimator (MLC) leaf sequences from four-dimensional (4D) geometries for dynamic MLC tracking delivery. The envisaged scenario was where respiratory phase and position information of the target was available during treatment, from which the optimal treatment plan could be further adapted in real time. A tool for 4D treatment plan optimization was developed that integrates a commercially available treatment planning system and a general-purpose optimization system. The 4D planning method was applied to the 4D computed tomography planning scans of three lung cancer patients. The optimization variables were MLC leaf positions as a function of monitor units and respiratory phase. The objective function was the deformable dose-summed 4D treatment plan score. MLC leaf motion was constrained by the maximum leaf velocity between control points in terms of monitor units for tumor motion parallel to the leaf travel direction and between phases for tumor motion parallel to the leaf travel direction. For comparison and a starting point for the 4D optimization, three-dimensional (3D) optimization was performed on each of the phases. The output of the 4D IMRT planning process is a leaf sequence which is a function of both monitor unit and phase, which can be delivered to a patient whose breathing may vary between the imaging and treatment sessions. The 4D treatment plan score improved during 4D optimization by 34%, 4%, and 50% for Patients A, B, and C, respectively, indicating 4D optimization generated a better 4D treatment plan than the deformable sum of individually optimized phase plans. The dose-volume histograms for each phase remained similar, indicating robustness of the 4D treatment plan to respiratory variations expected during treatment delivery. In summary, 4D optimization for respiratory phase-dependent treatment planning with dynamic MLC motion tracking improved the 4D treatment plan score by 4–50% compared with 3D optimization. The 4D treatment plans had leaf sequences that varied from phase to phase to account for anatomic motion, but showed similar target dose distributions in each phase. The current method could in principle be generalized for use in offline replanning between fractions or for online 4D treatment planning based on 4D cone-beam CT images. Computation time remains a challenge.

Digital reconstruction of high-quality daily 4D cone-beam CT images using prior knowledge of anatomy and respiratory motion

Conventional in-room cone-beam computed tomography (CBCT) lacks explicit representation of patient respiratory motion and usually has poor image quality and inaccurate CT numbers for target delineation and/or adaptive treatment planning. In-room four-dimensional (4D) CBCT image acquisition is still time consuming and suffers the same issue of poor image quality. To overcome this limitation, we developed a computational framework to digitally synthesize high-quality daily 4D CBCT images using the prior knowledge of motion and appearance learned from the planning 4D CT dataset. A patient-specific respiratory motion model was first constructed from the planning 4D CT images using principal component analysis of displacement vector fields across different respiratory phases. Subsequently, the respiratory motion model as well as the image content of the planning CT was spatially mapped onto the daily CBCT using deformable image registration. The synthesized 4D images possess explicit patient motion while maintaining the geometric accuracy of patient's anatomy at the time of treatment. We validated our model by quantitatively comparing the synthesized 4D CBCT against the 4D CT dataset acquired in the same day from protocol patients undergoing daily in-room CBCT setup and weekly 4D CT for treatment evaluation. Our preliminary results have demonstrated good agreement of contours in different motion phases between the synthesized and acquired scans. Various imaging artifacts were also suppressed and soft-tissue visibility was enhanced.

A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging.

4D cone beam CT (4D-CBCT) has been utilized in radiation therapy to provide 4D image guidance in lung and upper abdomen area. However, clinical application of 4D-CBCT is currently limited due to the long scan time and low image quality. The purpose of this paper is to develop a new 4D-CBCT reconstruction method that restores volumetric images based on the 1-min scan data acquired with a standard 3D-CBCT protocol. The model optimizes a deformation vector field that deforms a patient-specific planning CT (p-CT), so that the calculated 4D-CBCT projections match measurements. A forward-backward splitting (FBS) method is invented to solve the optimization problem. It splits the original problem into two well-studied subproblems, i.e., image reconstruction and deformable image registration. By iteratively solving the two subproblems, FBS gradually yields correct deformation information, while maintaining high image quality. The whole workflow is implemented on a graphic-processing-unit to improve efficiency. Comprehensive evaluations have been conducted on a moving phantom and three real patient cases regarding the accuracy and quality of the reconstructed images, as well as the algorithm robustness and efficiency. The proposed algorithm reconstructs 4D-CBCT images from highly under-sampled projection data acquired with 1-min scans. Regarding the anatomical structure location accuracy, 0.204 mm average differences and 0.484 mm maximum difference are found for the phantom case, and the maximum differences of 0.3-0.5 mm for patients 1-3 are observed. As for the image quality, intensity errors below 5 and 20 HU compared to the planning CT are achieved for the phantom and the patient cases, respectively. Signal-noise-ratio values are improved by 12.74 and 5.12 times compared to results from FDK algorithm using the 1-min data and 4-min data, respectively. The computation time of the algorithm on a NVIDIA GTX590 card is 1-1.5 min per phase. High-quality 4D-CBCT imaging based on the clinically standard 1-min 3D CBCT scanning protocol is feasible via the proposed hybrid reconstruction algorithm.

SU‐E‐J‐167: Improvement of Time‐Ordered Four Dimensional Cone‐Beam CT; Image Mosaicing with Real and Virtual Projections

Purpose:In the previous study, we developed time‐ordered fourdimensional (4D) cone‐beam CT (CBCT) technique to visualize nonperiodic organ motion, such as peristaltic motion of gastrointestinal organs and adjacent area, using half‐scan reconstruction method. One important obstacle was that truncation of projection was caused by asymmetric location of flat‐panel detector (FPD) in order to cover whole abdomen or pelvis in one rotation. In this study, we propose image mosaicing to extend projection data to make possible to reconstruct full field‐of‐view (FOV) image using half‐scan reconstruction.Methods:The projections of prostate cancer patients were acquired using the X‐ray Volume Imaging system (XVI, version 4.5) on Synergy linear accelerator system (Elekta, UK). The XVI system has three options of FOV, S, M and L, and M FOV was chosen for pelvic CBCT acquisition, with a FPD panel 11.5 cm offset. The method to produce extended projections consists of three main steps: First, normal three‐dimensional (3D) reconstruction which contains whole pelvis was implemented using real projections. Second, virtual projections were produced by reprojection process of the reconstructed 3D image. Third, real and virtual projections in each angle were combined into one extended mosaic projection. Then, 4D CBCT images were reconstructed using our inhouse reconstruction software based on Feldkamp, Davis and Kress algorithm. The angular range of each reconstruction phase in the 4D reconstruction was 180 degrees, and the range moved as time progressed.Results:Projection data were successfully extended without discontinuous boundary between real and virtual projections. Using mosaic projections, 4D CBCT image sets were reconstructed without artifacts caused by the truncation, and thus, whole pelvis was clearly visible.Conclusion:The present method provides extended projections which contain whole pelvis. The presented reconstruction method also enables time‐ordered 4D CBCT reconstruction of organs with non‐periodic motion with full FOV without projection‐truncation artifacts.This work was partly supported by the JSPS Core‐to‐Core Program(No. 23003). This work was partly supported by JSPS KAKENHI 24234567.