4D cone beam CT via spatiotemporal tensor framelet.
Ontology highlight
ABSTRACT:
SUBMITTER: Gao H
PROVIDER: S-EPMC3494730 | biostudies-other | 2012 Nov
REPOSITORIES: biostudies-other
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Project description:Background & purposeFour dimensional Cone beam CT (CBCT) has many potential benefits for radiotherapy but suffers from poor image quality, long acquisition times, and/or long reconstruction times. In this work we present a fast iterative reconstruction algorithm for 4D reconstruction of fast acquisition cone beam CT, as well as a new method for temporal regularization and compare to state of the art methods for 4D CBCT.Materials & methodsRegularization parameters for the iterative algorithms were found automatically via computer optimization on 60 s acquisitions using the XCAT phantom. Nineteen lung cancer patients were scanned with 60 s arcs using the onboard image on a Varian trilogy linear accelerator. Images were reconstructed using an accelerated ordered subset algorithm. A frequency based temporal regularization algorithm was developed and compared to the McKinnon-Bates algorithm, 4D total variation and prior images compressed sensing (PICCS).ResultsAll reconstructions were completed in 60 s or less. The proposed method provided a structural similarity of 0.915, compared with 0.786 for the classic McKinnon-bates method. For the patient study, it provided fewer image artefacts than PICCS, and better spatial resolution than 4D TV.ConclusionFour dimensional iterative CBCT reconstruction was done in less than 60 s, demonstrating the clinical feasibility. The frequency based method outperformed 4D total variation and PICCS on the simulated data, and for patients allowed for tumor location based on 60 s acquisitions, even for slowly breathing patients. It should thus be suitable for routine clinical use.
| S-EPMC7807579 | biostudies-literature
Project description:PurposeTo provide a proof of concept validation of a novel 4D cone-beam CT (4DCBCT) reconstruction algorithm and to determine the best methods to train and optimize the algorithm.MethodsThe algorithm animates a patient fan-beam CT (FBCT) with a patient specific parametric motion model in order to generate a time series of deformed CTs (the reconstructed 4DCBCT) that track the motion of the patient anatomy on a voxel by voxel scale. The motion model is constrained by requiring that projections cast through the deformed CT time series match the projections of the raw patient 4DCBCT. The motion model uses a basis of eigenvectors that are generated via principal component analysis (PCA) of a training set of displacement vector fields (DVFs) that approximate patient motion. The eigenvectors are weighted by a parameterized function of the patient breathing trace recorded during 4DCBCT. The algorithm is demonstrated and tested via numerical simulation.ResultsThe algorithm is shown to produce accurate reconstruction results for the most complicated simulated motion, in which voxels move with a pseudo-periodic pattern and relative phase shifts exist between voxels. The tests show that principal component eigenvectors trained on DVFs from a novel 2D/3D registration method give substantially better results than eigenvectors trained on DVFs obtained by conventionally registering 4DCBCT phases reconstructed via filtered backprojection.ConclusionsProof of concept testing has validated the 4DCBCT reconstruction approach for the types of simulated data considered. In addition, the authors found the 2D/3D registration approach to be our best choice for generating the DVF training set, and the Nelder-Mead simplex algorithm the most robust optimization routine.
| S-EPMC3247929 | biostudies-literature
Project description:PurposeCurrently, four-dimensional (4D) cone-beam computed tomography (CBCT) requires a 3-4 min full-fan scan to ensure usable image quality. Recent advancements in sparse-view 4D-CBCT reconstruction have opened the possibility to reduce scan time and dose. The aim of this study is to provide a common framework for systematically evaluating algorithms for 4D-CBCT reconstruction from a 1-min scan. Using this framework, the AAPM-sponsored SPARE Challenge was conducted in 2018 to identify and compare state-of-the-art algorithms.MethodsA clinically realistic CBCT dataset was simulated using patient CT volumes from the 4D-Lung database. The selected patients had multiple 4D-CT sessions, where the first 4D-CT was used as the prior CT, and the rest were used as the ground truth volumes for simulating CBCT projections. A GPU-based Monte Carlo tool was used to simulate the primary, scatter, and quantum noise signals. A total of 32 CBCT scans of nine patients were generated. Additional qualitative analysis was performed on a clinical Varian and clinical Elekta dataset to validate the simulation study. Participants were blinded from the ground truth, and were given 3 months to apply their reconstruction algorithms to the projection data. The submitted reconstructions were analyzed in terms of root-mean-squared-error (RMSE) and structural similarity index (SSIM) with the ground truth within four different region-of-interests (ROI) - patient body, lungs, planning target volume (PTV), and bony anatomy. Geometric accuracy was quantified as the alignment error of the PTV.ResultsTwenty teams participated in the challenge, with five teams completing the challenge. Techniques involved in the five methods included iterative optimization, motion-compensation, and deformation of the prior 4D-CT. All five methods rendered significant reduction in noise and streaking artifacts when compared to the conventional Feldkamp-Davis-Kress (FDK) algorithm. The RMS of the three-dimensional (3D) target registration error of the five methods ranged from 1.79 to 3.00 mm. Qualitative observations from the Varian and Elekta datasets mostly concur with those from the simulation dataset. Each of the methods was found to have its own strengths and weaknesses. Overall, the MA-ROOSTER method, which utilizes a 4D-CT motion model for temporal regularization, had the best and most consistent image quality and accuracy.ConclusionThe SPARE Challenge represents the first framework for systematically evaluating state-of-the-art algorithms for 4D-CBCT reconstruction from a 1-min scan. Results suggest the potential for reducing scan time and dose for 4D-CBCT. The challenge dataset and analysis framework are publicly available for benchmarking future reconstruction algorithms.
| S-EPMC6739166 | biostudies-literature
Project description:PurposeTo provide initial assessment of image quality and dose for a cone-beam computed tomographic (CT) scanner dedicated to extremity imaging.Materials and methodsA prototype cone-beam CT scanner has been developed for imaging the extremities, including the weight-bearing lower extremities. Initial technical assessment included evaluation of radiation dose measured as a function of kilovolt peak and tube output (in milliampere seconds), contrast resolution assessed in terms of the signal difference-to-noise ratio (SDNR), spatial resolution semiquantitatively assessed by using a line-pair module from a phantom, and qualitative evaluation of cadaver images for potential diagnostic value and image artifacts by an expert CT observer (musculoskeletal radiologist).ResultsThe dose for a nominal scan protocol (80 kVp, 108 mAs) was 9 mGy (absolute dose measured at the center of a CT dose index phantom). SDNR was maximized with the 80-kVp scan technique, and contrast resolution was sufficient for visualization of muscle, fat, ligaments and/or tendons, cartilage joint space, and bone. Spatial resolution in the axial plane exceeded 15 line pairs per centimeter. Streaks associated with x-ray scatter (in thicker regions of the patient--eg, the knee), beam hardening (about cortical bone--eg, the femoral shaft), and cone-beam artifacts (at joint space surfaces oriented along the scanning plane--eg, the interphalangeal joints) presented a slight impediment to visualization. Cadaver images (elbow, hand, knee, and foot) demonstrated excellent visibility of bone detail and good soft-tissue visibility suitable to a broad spectrum of musculoskeletal indications.ConclusionA dedicated extremity cone-beam CT scanner capable of imaging upper and lower extremities (including weight-bearing examinations) provides sufficient image quality and favorable dose characteristics to warrant further evaluation for clinical use.
| S-EPMC4263642 | biostudies-literature
Project description:PurposeTo improve the accuracy of liver tumor localization, this study tests a biomechanical modeling-guided liver cone-beam CT (CBCT) estimation (Bio-CBCT-est) technique, which generates new CBCTs by deforming a prior high-quality CT or CBCT image using deformation vector fields (DVFs). The DVFs can be used to propagate tumor contours from the prior image to new CBCTs for automatic 4D tumor localization.Methods/materialsTo solve the DVFs, the Bio-CBCT-est technique employs an iterative scheme that alternates between intensity-driven 2D-3D deformation and biomechanical modeling-guided DVF regularization and optimization. The 2D-3D deformation step solves DVFs by matching digitally reconstructed radiographs of the 3D deformed prior image to 2D phase-sorted on-board projections according to imaging intensities. This step's accuracy is limited at low-contrast intra-liver regions without sufficient intensity variations. To boost the DVF accuracy in these regions, we use the intensity-driven DVFs solved at higher-contrast liver boundaries to fine-tune the intra-liver DVFs by finite element analysis-based biomechanical modeling. We evaluated Bio-CBCT-est's accuracy with seven liver cancer patient cases. For each patient, we simulated 4D cone-beam projections from 4D-CT images, and used these projections for Bio-CBCT-est based image estimations. After Bio-CBCT-est, the DVF-propagated liver tumor/cyst contours were quantitatively compared with the manual contours on the original 4D-CT 'reference' images, using the DICE similarity index, the center-of-mass-error (COME), the Hausdorff distance (HD) and the voxel-wise cross-correlation (CC) metrics. In addition to simulation, we also performed a preliminary study to qualitatively evaluate the Bio-CBCT-est technique via clinically acquired cone beam projections. A quantitative study using an in-house deformable liver phantom was also performed.ResultsUsing 20 projections for image estimation, the average (±s.d.) DICE index increased from 0.48 ± 0.13 (by 2D-3D deformation) to 0.77 ± 0.08 (by Bio-CBCT-est), the average COME decreased from 7.7 ± 1.5 mm to 2.2 ± 1.2 mm, the average HD decreased from 10.6 ± 2.2 mm to 5.9 ± 2.0 mm, and the average CC increased from -0.004 ± 0.216 to 0.422 ± 0.206. The tumor/cyst trajectory solved by Bio-CBCT-est matched well with that manually obtained from 4D-CT reference images.ConclusionsBio-CBCT-est substantially improves the accuracy of 4D liver tumor localization via cone-beam projections and a biomechanical model.
| S-EPMC6445758 | biostudies-literature
Project description:To demonstrate the feasibility of reconstructing a cone-beam CT (CBCT) image by deformably altering a prior fan-beam CT (FBCT) image such that it matches the anatomy portrayed in the CBCT projection data set.A prior FBCT image of the patient is assumed to be available as a source image. A CBCT projection data set is obtained and used as a target image set. A parametrized deformation model is applied to the source FBCT image, digitally reconstructed radiographs (DRRs) that emulate the CBCT projection image geometry are calculated and compared to the target CBCT projection data, and the deformation model parameters are adjusted iteratively until the DRRs optimally match the CBCT projection data set. The resulting deformed FBCT image is hypothesized to be an accurate representation of the patient's anatomy imaged by the CBCT system. The process is demonstrated via numerical simulation. A known deformation is applied to a prior FBCT image and used to create a synthetic set of CBCT target projections. The iterative projection matching process is then applied to reconstruct the deformation represented in the synthetic target projections; the reconstructed deformation is then compared to the known deformation. The sensitivity of the process to the number of projections and the DRR/CBCT projection mismatch is explored by systematically adding noise to and perturbing the contrast of the target projections relative to the iterated source DRRs and by reducing the number of projections.When there is no noise or contrast mismatch in the CBCT projection images, a set of 64 projections allows the known deformed CT image to be reconstructed to within a nRMS error of 1% and the known deformation to within a nRMS error of 7%. A CT image nRMS error of less than 4% is maintained at noise levels up to 3% of the mean projection intensity, at which the deformation error is 13%. At 1% noise level, the number of projections can be reduced to 8 while maintaining CT image and deformation errors of less than 4% and 13%, respectively. The method is sensitive to contrast mismatch between the simulated projections and the target projections when the soft-tissue contrast in the projections is low.By using prior knowledge available in a FBCT image, the authors show that a CBCT image can be iteratively reconstructed from a comparatively small number of projection images, thus saving acquisition time and reducing imaging dose. This will enable more frequent daily imaging during radiation therapy. Because the process preserves the CT numbers of the FBCT image, the resulting 3D image intensities will be more accurate than a CBCT image reconstructed via conventional backprojection methods. Reconstruction errors are insensitive to noise at levels beyond what would typically be found in CBCT projection data, but are sensitive to contrast mismatch errors between the CBCT projection data and the DRRs.
| S-EPMC2997812 | biostudies-other
Project description:Background and purposeThe limited image quality in Cone Beam CT (CBCT) stemming primarily from scattered radiation hinders accurate CBCT based dose calculation in radiotherapy. We investigated the use of a stoichiometric calibration for dose calculation on CBCT images of lung cancer patients.Materials and methodsCBCT calibrations were performed with thorax scan protocols, using a phantom with approximately the diameter of an average human thorax and a central cavity simulating the thoracic cavity. Thus scatter conditions resembling those in clinical thorax CBCT scans were simulated. A published stoichiometric parametrization was used. A treatment plan was simulated on CBCT and CT scans of an anthropomorphic phantom, the dose distributions were calculated, and clinically relevant DVH parameters were compared. Twelve lung cancer patients had surveillance CT scans (s-CT) taken twice during their treatment course in addition to daily setup CBCTs. Dose calculations were performed on the s-CTs and the corresponding CBCTs taken on the same day, and DVH parameters were compared.ResultsEighty percent of CBCT DVH parameters found for the phantom were within ±1% of CT doses, and 98% were within ±3%. For patients, the median CT/CBCT dose difference was within ±2%, and 98% of DVH parameters were within ±4%. Minimum dose to the tumor was underestimated (median 1.9%) on CBCT, while maximum doses to most organs at risk were slightly overestimated.ConclusionDirect dose calculations on CBCTs of lung cancer patients were feasible within ∼4% accuracy using a simple calibration method, which is easily implemented in a clinical setting.
| S-EPMC7807659 | biostudies-literature
Project description:BackgroundMinimally invasive surgery (MIS) for evacuation of spontaneous intracerebral hemorrhage (ICH) has shown promise but there remains a need for intraoperative performance assessment considering the wide range of evacuation effectiveness. In this feasibility study, we analyzed the benefit of intraoperative 3-dimensional imaging during navigated endoscopy-assisted ICH evacuation by mechanical clot fragmentation and aspiration.Methods18 patients with superficial or deep supratentorial ICH underwent MIS for clot evacuation followed by intraoperative computerized tomography (iCT) or cone-beam CT (CBCT) imaging. Eligibility for MIS required (a) availability of intraoperative iCT or CBCT, (b) spontaneous lobar or deep ICH without vascular pathology, (c) a stable ICH volume (20-90 ml), (d) a reduced level of consciousness (GCS 5-14), and (e) a premorbid mRS ≤ 1. Demographic, clinical, and radiographic patient data were analyzed by two independent observers.ResultsNine female and 9 male patients with a median age of 76 years (42-85) presented with an ICH score of 3 (1-4), GCS of 10 (5-14) and ICH volume of 54 ± 26 ml. Clot fragmentation and aspiration was feasible in all cases and intraoperative imaging determined an overall evacuation rate of 80 ± 19% (residual hematoma volume: 13 ± 17 ml; p < 0.0001 vs. Pre-OP). Based on the intraoperative imaging results, 1/3rd of all patients underwent an immediate re-aspiration attempt. No patient experienced hemorrhagic complications or required conversion to open craniotomy. However, routine postoperative CT imaging revealed early hematoma re-expansion with an adjusted evacuation rate of 59 ± 30% (residual hematoma volume: 26 ± 37 ml; p < 0.001 vs. Pre-OP).ConclusionsRoutine utilization of iCT or CBCT imaging in MIS for ICH permits direct surgical performance assessment and the chance for immediate re-aspiration, which may optimize targeting of an ideal residual hematoma volume and reduce secondary revision rates.
| S-EPMC7593312 | biostudies-literature
Project description:In computer vision, reference datasets from simulation and real outdoor scenes have been highly successful in promoting algorithmic development in stereo reconstruction. Endoscopic stereo reconstruction for surgical scenes gives rise to specific problems, including the lack of clear corner features, highly specular surface properties and the presence of blood and smoke. These issues present difficulties for both stereo reconstruction itself and also for standardised dataset production. Previous datasets have been produced using computed tomography (CT) or structured light reconstruction on phantom or ex vivo models. We present a stereo-endoscopic reconstruction validation dataset based on cone-beam CT (SERV-CT). Two ex vivo small porcine full torso cadavers were placed within the view of the endoscope with both the endoscope and target anatomy visible in the CT scan. Subsequent orientation of the endoscope was manually aligned to match the stereoscopic view and benchmark disparities, depths and occlusions are calculated. The requirement of a CT scan limited the number of stereo pairs to 8 from each ex vivo sample. For the second sample an RGB surface was acquired to aid alignment of smooth, featureless surfaces. Repeated manual alignments showed an RMS disparity accuracy of around 2 pixels and a depth accuracy of about 2 mm. A simplified reference dataset is provided consisting of endoscope image pairs with corresponding calibration, disparities, depths and occlusions covering the majority of the endoscopic image and a range of tissue types, including smooth specular surfaces, as well as significant variation of depth. We assessed the performance of various stereo algorithms from online available repositories. There is a significant variation between algorithms, highlighting some of the challenges of surgical endoscopic images. The SERV-CT dataset provides an easy to use stereoscopic validation for surgical applications with smooth reference disparities and depths covering the majority of the endoscopic image. This complements existing resources well and we hope will aid the development of surgical endoscopic anatomical reconstruction algorithms.
| S-EPMC8961000 | biostudies-literature
Project description:BackgroundMotion-compensated (MoCo) reconstruction shows great promise in improving four-dimensional cone-beam computed tomography (4D-CBCT) image quality. MoCo reconstruction for a 4D-CBCT could be more accurate using motion information at the CBCT imaging time than that obtained from previous 4D-CT scans. However, such data-driven approaches are hampered by the quality of initial 4D-CBCT images used for motion modeling.PurposeThis study aims to develop a deep-learning method to generate high-quality motion models for MoCo reconstruction to improve the quality of final 4D-CBCT images.MethodsA 3D artifact-reduction convolutional neural network (CNN) was proposed to improve conventional phase-correlated Feldkamp-Davis-Kress (PCF) reconstructions by reducing undersampling-induced streaking artifacts while maintaining motion information. The CNN-generated artifact-mitigated 4D-CBCT images (CNN enhanced) were then used to build a motion model which was used by MoCo reconstruction (CNN+MoCo). The proposed procedure was evaluated using in-vivo patient datasets, an extended cardiac-torso (XCAT) phantom, and the public SPARE challenge datasets. The quality of reconstructed images for XCAT phantom and SPARE datasets was quantitatively assessed using root-mean-square-error (RMSE) and normalized cross-correlation (NCC).ResultsThe trained CNN effectively reduced the streaking artifacts of PCF CBCT images for all datasets. More detailed structures can be recovered using the proposed CNN+MoCo reconstruction procedure. XCAT phantom experiments showed that the accuracy of estimated motion model using CNN enhanced images was greatly improved over PCF. CNN+MoCo showed lower RMSE and higher NCC compared to PCF, CNN enhanced and conventional MoCo. For the SPARE datasets, the average (± standard deviation) RMSE in mm-1 for body region of PCF, CNN enhanced, conventional MoCo and CNN+MoCo were 0.0040 ± 0.0009, 0.0029 ± 0.0002, 0.0024 ± 0.0003 and 0.0021 ± 0.0003. Corresponding NCC were 0.84 ± 0.05, 0.91 ± 0.05, 0.91 ± 0.05 and 0.93 ± 0.04.ConclusionsCNN-based artifact reduction can substantially reduce the artifacts in the initial 4D-CBCT images. The improved images could be used to enhance the motion modeling and ultimately improve the quality of the final 4D-CBCT images reconstructed using MoCo.
| S-EPMC10278033 | biostudies-literature