Cone-beam Computed Tomography Projection Data Research Articles

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.

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

This work aims to improve limited-angle (LA) cone beam computed tomography (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. In radiation therapy (RT), CBCT is routinely used as the on-board imaging modality for patient setup. Compared to diagnostic CT, CBCT has a long acquisition time, e.g., 60 seconds for a full 360° rotation, which is subject to the motion artifact. Therefore, the LA-CBCT, if achievable, is of the great interest for the purpose of RT, for its proportionally reduced scanning time in addition to the radiation dose. However, LA-CBCT suffers from severe wedge artifacts and image distortions. 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.

Deformable motion compensation for interventional cone-beam CT

Image-guided therapies in the abdomen and pelvis are often hindered by motion artifacts in cone-beam CT (CBCT) arising from complex, non-periodic, deformable organ motion during long scan times (5–30 s). We propose a deformable image-based motion compensation method to address these challenges and improve CBCT guidance. Motion compensation is achieved by selecting a set of small regions of interest in the uncompensated image to minimize a cost function consisting of an autofocus objective and spatiotemporal regularization penalties. Motion trajectories are estimated using an iterative optimization algorithm (CMA-ES) and used to interpolate a 4D spatiotemporal motion vector field. The motion-compensated image is reconstructed using a modified filtered backprojection approach. Being image-based, the method does not require additional input besides the raw CBCT projection data and system geometry that are used for image reconstruction. Experimental studies investigated: (1) various autofocus objective functions, analyzed using a digital phantom with a range of sinusoidal motion magnitude (4, 8, 12, 16, 20 mm); (2) spatiotemporal regularization, studied using a CT dataset from The Cancer Imaging Archive with deformable sinusoidal motion of variable magnitude (10, 15, 20, 25 mm); and (3) performance in complex anatomy, evaluated in cadavers undergoing simple and complex motion imaged on a CBCT-capable mobile C-arm system (Cios Spin 3D, Siemens Healthineers, Forchheim, Germany). Gradient entropy was found to be the best autofocus objective for soft-tissue CBCT, increasing structural similarity (SSIM) by 42%–92% over the range of motion magnitudes investigated. The optimal temporal regularization strength was found to vary widely (0.5–5 mm−2) over the range of motion magnitudes investigated, whereas optimal spatial regularization strength was relatively constant (0.1). In cadaver studies, deformable motion compensation was shown to improve local SSIM by ∼17% for simple motion and ∼21% for complex motion and provided strong visual improvement of motion artifacts (reduction of blurring and streaks and improved visibility of soft-tissue edges). The studies demonstrate the robustness of deformable motion compensation to a range of motion magnitudes, frequencies, and other factors (e.g. truncation and scatter).

Technical Note: 4D cone-beam CT reconstruction from sparse-view CBCT data for daily motion assessment in pencil beam scanned proton therapy (PBS-PT).

PurposeThe number of pencil beam scanned proton therapy (PBS‐PT) facilities equipped with cone‐beam computed tomography (CBCT) imaging treating thoracic indications is constantly rising. To enable daily internal motion monitoring during PBS‐PT treatments of thoracic tumors, we assess the performance of Motion‐Aware RecOnstructiOn method using Spatial and Temporal Regularization (MA‐ROOSTER) four‐dimensional CBCT (4DCBCT) reconstruction for sparse‐view CBCT data and a realistic data set of patients treated with proton therapy.MethodsDaily CBCT projection data for nine non‐small cell lung cancer (NSCLC) patients and one SCLC patient were acquired at a proton gantry system (IBA Proteus® One). Four‐dimensional CBCT images were reconstructed applying the MA‐ROOSTER and the conventional phase‐correlated Feldkamp‐Davis‐Kress (PC‐FDK) method. Image quality was assessed by visual inspection, contrast‐to‐noise ratio (CNR), signal‐to‐noise ratio (SNR), and the structural similarity index measure (SSIM). Furthermore, gross tumor volume (GTV) centroid motion amplitudes were evaluated.ResultsImage quality for the 4DCBCT reconstructions using MA‐ROOSTER was superior to the PC‐FDK reconstructions and close to FDK images (median CNR: 1.23 [PC‐FDK], 1.98 [MA‐ROOSTER], and 1.98 [FDK]; median SNR: 2.56 [PC‐FDK], 4.76 [MA‐ROOSTER], and 5.02 [FDK]; median SSIM: 0.18 [PC‐FDK vs FDK], 0.31 [MA‐ROOSTER vs FDK]). The improved image quality of MA‐ROOSTER facilitated GTV contour warping and realistic motion monitoring for most of the reconstructions.ConclusionMA‐ROOSTER based 4DCBCTs performed well in terms of image quality and appear to be promising for daily internal motion monitoring in PBS‐PT treatments of (N)SCLC patients.

A Convolutional Neural Network-Based Anthropomorphic Model Observer for Signal Detection in Breast CT Images Without Human-Labeled Data

Various imaging parameters in X-ray computed tomography (CT) should be examined and optimized by task-based assessment of human observer performance. Recently, convolutional neural networks (CNNs) have been introduced as anthropomorphic model observers. However, when human-labeled data are not available or limited, CNNs with an existing training strategy do not produce good performance agreement with human observers. The purpose of this study is to propose new training strategies for a CNN-based anthropomorphic model observer without human-labeled data for signal-known-exactly and background-known-statistically detection tasks. We acquired cone-beam CT projection data of breast background volume and reconstructed the projection data using the Feldkamp-Davis-Kress algorithm with 12 different imaging conditions including viewing image planes. Training data for the CNN were labeled utilizing conventional model observers. We employed an early stopping rule to reflect internal noise during the CNN training. To examine the CNN performance, we used three different training-testing schemes. The performance agreement between the human and model observers was measured via a Bland-Altman plot, the root-mean-squared error (RMSE), and the Pearson's correlation coefficient ($r$ ) of their proportion correct values. Throughout the three different training-testing schemes, CNNs with the proposed training strategies yielded narrower limits of agreements (with a bias lower than 0.03) and higher scores in both RMSE and $r$ than the conventional model observers. This indicates the proposed training strategies enable the CNN-based anthropomorphic model observer to have good performance agreement with human observers and generalize better to different imaging conditions than conventional anthropomorphic model observers.

Shading correction assisted iterative cone-beam CT reconstruction

Recent advances in total variation (TV) technology enable accurate CT image reconstruction from highly under-sampled and noisy projection data. The standard iterative reconstruction algorithms, which work well in conventional CT imaging, fail to perform as expected in cone beam CT (CBCT) applications, wherein the non-ideal physics issues, including scatter and beam hardening, are more severe. These physics issues result in large areas of shading artifacts and cause deterioration to the piecewise constant property assumed in reconstructed images. To overcome this obstacle, we incorporate a shading correction scheme into low-dose CBCT reconstruction and propose a clinically acceptable and stable three-dimensional iterative reconstruction method that is referred to as the shading correction assisted iterative reconstruction. In the proposed method, we modify the TV regularization term by adding a shading compensation image to the reconstructed image to compensate for the shading artifacts while leaving the data fidelity term intact. This compensation image is generated empirically, using image segmentation and low-pass filtering, and updated in the iterative process whenever necessary. When the compensation image is determined, the objective function is minimized using the fast iterative shrinkage-thresholding algorithm accelerated on a graphic processing unit. The proposed method is evaluated using CBCT projection data of the Catphan© 600 phantom and two pelvis patients. Compared with the iterative reconstruction without shading correction, the proposed method reduces the overall CT number error from around 200 HU to be around 25 HU and increases the spatial uniformity by a factor of 20 percent, given the same number of sparsely sampled projections. A clinically acceptable and stable iterative reconstruction algorithm for CBCT is proposed in this paper. Differing from the existing algorithms, this algorithm incorporates a shading correction scheme into the low-dose CBCT reconstruction and achieves more stable optimization path and more clinically acceptable reconstructed image. The method proposed by us does not rely on prior information and thus is practically attractive to the applications of low-dose CBCT imaging in the clinic.

Convolution-based scatter correction usingkernels combining measurements andMonte Carlo simulations.

One of the well-recognized challenge of Cone-Beam Computed Tomography (CBCT) is scatter contamination within the projection images. Scatter degrades the image quality by decreasing the contrast, introducing cupping and shading artifacts and thus leading to inaccuracies in the reconstructed values. The higher scatter to primary ratio experienced in industrial applications leads to even more important artifacts. Various strategies have been investigated to manage the scatter signal in CBCT projection data. One of these strategies is to calculate the scatter intensity by deconvolution of primary intensity using Scatter Kernel Superposition (SKS). In this paper, we present an approach combining experimental measurements and Monte Carlo simulations to estimate the scatter kernels for industrial applications based on the continuously thickness-adapted kernels strategy with a four-Gaussian modeling of kernels. We compare this approach with an experimental technique based on a two-Gaussian modeling of the kernels. The results obtained prove the superiority of a four-Gaussian model to effectively take into account both the contribution of object and detector scattering as compared to a two-Gaussian approach. We also present the parameterisation of the scatter kernels with respect to object to detector distance. This approach facilitates the use of a single geometry for calculation of scatter kernels over the whole magnification range of the acquisition setup.

Analysis of Dose Calculation Accuracy in Cone Beam Computed Tomography with Various Amount of Scattered Photon Contamination

Cone-beam computed tomography (CBCT) images have inaccurate CT numbers because of scattered photons. Thus, quantitative analysis of scattered photons that affect an electron density (ED) curve and calculated doses may be effective information to achieve CBCT-based radiation treatment planning. We quantitatively evaluated the effect of scattered photons on the accuracy of dose calculations from a lung image. The Monte Carlo method was used to calculate CBCT projection data, and we made two calibration curves for conditions with or without scattered photons. Moreover, we applied cupping artifact correction and evaluated the effects on image uniformity and dose calculation accuracy. Dose deviations were compared with those of conventional CT in conventional and volumetric intensity modulated arc therapy (VMAT) planning by using γ analysis and dose volume histogram (DVH) analysis. We found that cupping artifacts contaminated the scattered photons, and the γ analysis showed that the dose distribution was most decreased for a scattered photon ratio of 40%. Cupping artifact correction significantly improved image uniformity; therefore, ED curves were near ideal, and the pass rate results were significantly higher than those associated with the scattered photon effect in 65.1% and 78.4% without correction, 99.5% and 97.7% with correction, in conventional and VMAT planning, respectively. In the DVH analysis, all organ dose indexes were reduced in the scattered photon images, but dose index error rates with cupping artifact correction were improved within approximately 10%. CBCT image quality was strongly affected by scattered photons, and the dose calculation accuracy based on the CBCT image was improved by removing cupping artifacts caused by the scattered photons.

MO-DE-207A-08: Four-Dimensional Cone-Beam CT Iterative Reconstruction with Time-Ordered Chain Graph Model for Non-Periodic Organ Motion and Deformation

Purpose: The purpose of this study is to propose a new concept of four-dimensional (4D) cone-beam CT (CBCT) reconstruction for non-periodic organ motion using the Time-ordered Chain Graph Model (TCGM), and to compare the reconstructed results with the previously proposed methods, the total variation-based compressed sensing (TVCS) and prior-image constrained compressed sensing (PICCS). Methods: CBCT reconstruction method introduced in this study consisted of maximum a posteriori (MAP) iterative reconstruction combined with a regularization term derived from a concept of TCGM, which includes a constraint coming from the images of neighbouring time-phases. The time-ordered image series were concurrently reconstructed in the MAP iterative reconstruction framework. Angular range of projections for each time-phase was 90 degrees for TCGM and PICCS, and 200 degrees for TVCS. Two kinds of projection data, an elliptic-cylindrical digital phantom data and two clinical patients’ data, were used for reconstruction. The digital phantom contained an air sphere moving 3 cm along longitudinal axis, and temporal resolution of each method was evaluated by measuring the penumbral width of reconstructed moving air sphere. The clinical feasibility of non-periodic time-ordered 4D CBCT reconstruction was also examined using projection data of prostate cancer patients. Results: The results of reconstructed digital phantom shows that the penumbral widths of TCGM yielded the narrowest result; PICCS and TCGM were 10.6% and 17.4% narrower than that of TVCS, respectively. This suggests that the TCGM has the better temporal resolution than the others. Patients’ CBCT projection data were also reconstructed and all three reconstructed results showed motion of rectal gas and stool. The result of TCGM provided visually clearer and less blurring images. Conclusion: The present study demonstrates that the new concept for 4D CBCT reconstruction, TCGM, combined with MAP iterative reconstruction framework enables time-ordered image reconstruction with narrower time-window.

Cone Beam CT Measures Abdominal Tumor Motion Better Than 4DCT and Equal to On-table Fluoroscopy

Although 4DCT is most often used for motion management, research shows it does not adequately predict on-table tumor motion for abdominal radiation therapy. In this study, we demonstrated the use of Cone Beam CT (CBCT) projection data for predicting intrafraction tumor motion and compared our results to pre and post treatment fluoroscopy (FL). For 31 patients with abdominal tumors and implanted fiducial markers, tumor motion was measured with CBCT and fluoroscopy for 202 treatment fractions and compared to the planned tumor motion from 4DCT. We processed the CBCT projections using an in-house fiducial tracking algorithm and used maximum-likelihood to calculate the fiducial trajectories. The trajectories were also measured by pre and post treatment orthogonal fluoroscopy. The daily required internal margin (IM) was calculated for CBCT and fluoroscopy as the fifth through 95th percentiles of motion in each left-right (LR), superior-inferior (SI) and anterior-posterior (AP) direction. The planning IM4DCT (the range of fiducial motion on 4DCT) was determined to be adequate when it was within ±1.2 mm (AP, LR) and ±3 mm (SI) of the daily IMCBCT, IMpre-fluoro or IMfluoro (combined pre and post treatment fluoroscopy). To validate the CBCT technique as a predictive measure of intrafraction motion, we compared IMCBCT to IMpre-fluoro, which is our institutional standard for measuring daily intrafractional motion. Finally, we compared IMCBCT to IMfluoro to identify patients who could benefit from imaging during treatment. The planning 4DCT could not accurately predict the intrafractional tumor motion observed during CBCT in ≥80% of fractions for 97% of patients. As measured by IMpre-fluoro and IMfluoro, 4DCT failed to predict intrafractional tumor motion in ≥80% of fractions for 94% and 100% of patients, respectively. Comparing CBCT to fluoroscopy, IMCBCT was in agreement with or larger than IMpre-fluoro for 96.0%, 91.3% and 93.4% of fractions in LR, SI and AP directions, respectively. For conventionally fractionated patients (median treatment time, t = 8.8 min), IMCBCT was in agreement with or larger than IMfluoro for 97.3% of fractions (in SI direction); this decreased to 82.3% for SBRT patients (t = 17 min) demonstrating the need for imaging during treatment for patients with longer treatment times. Tumor motion determined from 4DCT-simulation does not accurately predict the daily motion observed on CBCT or fluoroscopy. Fiducial motion derived from CBCT projection data accurately reflects the motion observed on pre and posttreatment fluoroscopy; CBCT could replace fluoroscopy for pretreatment internal margin verification, potentially reducing patient setup time and imaging dose. For patients with longer treatment times, there is a need for imaging during treatment to verify internal margin accuracy.

Implementation of an efficient Monte Carlo calculation for CBCT scatter correction: phantom study.

Cone‐beam computed tomography (CBCT) images suffer from poor image quality, in a large part due to contamination from scattered X‐rays. In this work, a Monte Carlo (MC)‐based iterative scatter correction algorithm was implemented on measured phantom data acquired from a clinical on‐board CBCT scanner. An efficient EGSnrc user code (egs_cbct) was used to transport photons through an uncorrected CBCT scan of a Catphan 600 phantom. From the simulation output, the contribution from primary and scattered photons was estimated in each projection image. From these estimates, an iterative scatter correction was performed on the raw CBCT projection data. The results of the scatter correction were compared with the default vendor reconstruction. The scatter correction was found to reduce the error in CT number for selected regions of interest, while improving contrast‐to‐noise ratio (CNR) by 18%. These results demonstrate the performance of the proposed scatter correction algorithm in improving image quality for clinical CBCT images.PACS numbers: 87.10.Rt, 87.57.Q‐

SU-E-J-56: Static Gantry Digital Tomosynthesis From the Beam's-Eye-View

Purpose We have designed a novel TumoTrak™ x-ray system that delivers 19 distinct kV views with the linac gantry stationary. It images MV treatment beam above and below the patient with a kV tomosysthesis slice image from the therapy beam’s-eye-view. Results will be high quality images without MLC shadowing for notable improvements relative to conventional fluoroscopic MV imaging and fluoroscopic kV imaging. Methods A complete design has a kV electron beam multisource X-ray tube that fits around the MV treatment beam path, with little interference with normal radiotherapy and unblocked by the multi-leaf-collimator. To simulate digital tomosynthesis, we used cone-beam CT projection data from a lung SBRT patient. These data were acquired at 125 kVp and 11 fps (0.4 mAs per projection). We chose 19 projections evenly spaced over 27° around one of the treatment angles (240°). Digital tomosynthesis reconstruction of a slice through the tumor was performed using iterative reconstruction. The visibility of the lesion was assessed for the reconstructed digital tomosynthesis (DTS), using fluoroscopy MV images acquired during radiation therapy, and a kV single projection image acquired at the same angle as the treatment field (240°). Results The fluoroscopic DTS images provide the best tumor contrast, surpassing themore » conventional radiographic and the in-treatment MV portal images. The electron beam multisource X-ray tube design has been completed and the tube is being fabricated. The estimated time to cycle through all 19 projections is 700 ms, enabling high frame-rate imaging. While the initial proposed use case is for image guided and gated treatment delivery, the enhanced imaging will also deliver superior radiographic images for patient setup. Conclusion The proposed device will deliver high quality planar images from the beam’s-eye-view without MLC obstruction. The prototype has been designed and is being assembled with first imaging scheduled for May 2015. L. Partain, J. Kwon, D. Boyd: NIH/SBIR R43CA192489-01. J. Rottmann, G. Zentai, R. Berbeco: NIH/NCI 1R01CA188446-01. R. Berbeco: E. Research Grant, Varian Medical Systems.« less

GPU-accelerated regularized iterative reconstruction for few-view cone beam CT.

The present work proposes an iterative reconstruction technique designed for x-ray transmission computed tomography (CT). The main objective is to provide a model-based solution to the cone-beam CT reconstruction problem, yielding accurate low-dose images via few-views acquisitions in clinically acceptable time frames. The proposed technique combines a modified ordered subsets convex (OSC) algorithm and the total variation minimization (TV) regularization technique and is called OSC-TV. The number of subsets of each OSC iteration follows a reduction pattern in order to ensure the best performance of the regularization method. Considering the high computational cost of the algorithm, it is implemented on a graphics processing unit, using parallelization to accelerate computations. The reconstructions were performed on computer-simulated as well as human pelvic cone-beam CT projection data and image quality was assessed. In terms of convergence and image quality, OSC-TV performs well in reconstruction of low-dose cone-beam CT data obtained via a few-view acquisition protocol. It compares favorably to the few-view TV-regularized projections onto convex sets (POCS-TV) algorithm. It also appears to be a viable alternative to full-dataset filtered backprojection. Execution times are of 1-2 min and are compatible with the typical clinical workflow for nonreal-time applications. Considering the image quality and execution times, this method may be useful for reconstruction of low-dose clinical acquisitions. It may be of particular benefit to patients who undergo multiple acquisitions by reducing the overall imaging radiation dose and associated risks.

Pseudo-CT generation from multiple MR images for small animal irradiation

Introduction. Computed tomography (CT) is the standard imaging modality for radiation therapy treatment planning (RTTP) because of its ability to provide information on electron density. However, magnetic resonance (MR) imaging provides superior soft tissue contrast, especially in small animal imaging, facilitating the precise selection of the target volume. This makes the technique interesting for irradiation of brain tumors. The aim of this study was to present an MR-only based workflow for RTTP on a small animal radiation research platform (SARRP) by investigating the potential of probabilistic classification of voxels using multiple MR sequences. Methods. Six female Fisher rats were anesthetized using isoflurane and individually fixed on an in-house made multimodality bed before starting MR and CT acquisitions. MR measurements were performed on a 7-Tesla system using a rat brain volume coil. Four different MR sequences were acquired for each animal, including a T1-weighted (MDEFT) sequence, a T2-weighted (RARE) sequence, an ultra-short echo time sequence with 20 μs echo time (UTE1) and an ultra- short echo time sequence with 2 ms echo time (UTE2). UTE offers the opportunity to acquire images from proton-poor structures with very short transverse relaxation times, such as bone, by using a rapid readout of the fast decaying signal. Following MR, the animals were moved to the SARRP to start a cone-beam CT (CB-CT) by acquiring 720 projections over 360°. Cone-beam CT projection data were reconstructed by filtered back-projection to obtain the standard-CT for RTTP. Then the images were bias field corrected and manually co-registered to the CB-CT. After that, images were segmented in three tissue classes (air, soft tissue and bone) with k-means for the CB-CT and fuzzy c-means segmentation algorithm (FCM) for the MR images with multiple MR images as input. The membership probability can be between 0 and 1, with one indicating 100% probability and zero indicating 0% probability to belong to a specific tissue class. To obtain a pseudo-CT image, voxels were assigned to the tissue class having the highest membership probability. The dice coefficient was used to evaluate the correctness of the segmentation for soft-tissue and bone. The pseudo-CT images with the highest similarity index were used for further radiotherapy treatment planning (RTTP), in addition, to the standard UTE1-UTE2. The target of the RTTP that was selected in the primary cortex (M1) and three different beam arrangements were investigated to compare CB-CT and MR-based dose calculations. The dose plans were a single static beam of 3x3 mm, using a single arc (3x3 mm beam size, 120° arc, couch at 0°), and three non-coplanar arcs (3x3 mm beam size, 120° arc, couch at 0°, 45° and 90°). Dose distributions were calculated using the TPS of the SARRP and cumulative dose volume histograms (DVHs) of the target and normal brain tissue were obtained for the three dose plans. Results The highest dice coefficient was obtained for the T1-UTE1-T2 combination, which was used for further RTTP. The contribution of bone to the total dice coefficient did not exceed 27%. However, bone accounts for only 2% of the image, therefore a misclassified bone pixel has a bigger effect in the dice coefficient than a misclassified soft tissue pixel. Using only 1 beam, both MR combinations underestimate the dose to be delivered to the target. When more complex beam configurations were used to irradiate the target, very small differences were observed between CB-CT and MR based dose calculations.

TH‐A‐18C‐03: Noise Correlation in CBCT Projection Data and Its Application for Noise Reduction in Low‐Dose CBCT

Purpose:To study the noise correlation properties of cone‐beam CT (CBCT) projection data and to incorporate the noise correlation information to a statistics‐based projection restoration algorithm for noise reduction in low‐dose CBCT.Methods:In this study, we systematically investigated the noise correlation properties among detector bins of CBCT projection data by analyzing repeated projection measurements. The measurements were performed on a TrueBeam on‐board CBCT imaging system with a 4030CB flat panel detector. An anthropomorphic male pelvis phantom was used to acquire 500 repeated projection data at six different dose levels from 0.1 mAs to 1.6 mAs per projection at three fixed angles. To minimize the influence of the lag effect, lag correction was performed on the consecutively acquired projection data. The noise correlation coefficient between detector bin pairs was calculated from the corrected projection data. The noise correlation among CBCT projection data was then incorporated into the covariance matrix of the penalized weighted least‐squares (PWLS) criterion for noise reduction of low‐dose CBCT.Results:The analyses of the repeated measurements show that noise correlation coefficients are non‐zero between the nearest neighboring bins of CBCT projection data. The average noise correlation coefficients for the first‐ and second‐ order neighbors are about 0.20 and 0.06, respectively. The noise correlation coefficients are independent of the dose level. Reconstruction of the pelvis phantom shows that the PWLS criterion with consideration of noise correlation (PWLS‐Cor) results in a lower noise level as compared to the PWLS criterion without considering the noise correlation (PWLS‐Dia) at the matched resolution.Conclusion:Noise is correlated among nearest neighboring detector bins of CBCT projection data. An accurate noise model of CBCT projection data can improve the performance of the statistics‐based projection restoration algorithm for low‐dose CBCT.

SU‐E‐J‐10: A Moving‐Blocker‐Based Strategy for Simultaneous Megavoltage and Kilovoltage Scatter Correction in Cone‐Beam Computed Tomography Image Acquired During Volumetric Modulated Arc Therapy

Purpose:To evaluate a moving‐blocker‐based approach in estimating and correcting megavoltage (MV) and kilovoltage (kV) scatter contamination in kV cone‐beam computed tomography (CBCT) acquired during volumetric modulated arc therapy (VMAT).Methods:XML code was generated to enable concurrent CBCT acquisition and VMAT delivery in Varian TrueBeam developer mode. A physical attenuator (i.e., “blocker”) consisting of equal spaced lead strips (3.2mm strip width and 3.2mm gap in between) was mounted between the x‐ray source and patient at a source to blocker distance of 232mm. The blocker was simulated to be moving back and forth along the gantry rotation axis during the CBCT acquisition. Both MV and kV scatter signal were estimated simultaneously from the blocked regions of the imaging panel, and interpolated into the un‐blocked regions. Scatter corrected CBCT was then reconstructed from un‐blocked projections after scatter subtraction using an iterative image reconstruction algorithm based on constraint optimization. Experimental studies were performed on a Catphan 600 phantom and an anthropomorphic pelvis phantom to demonstrate the feasibility of using moving blocker for MV‐kV scatter correction.Results:MV scatter greatly degrades the CBCT image quality by increasing the CT number inaccuracy and decreasing the image contrast, in addition to the shading artifacts caused by kV scatter. The artifacts were substantially reduced in the moving blocker corrected CBCT images in both Catphan and pelvis phantoms. Quantitatively, CT number error in selected regions of interest reduced from 377 in the kV‐MV contaminated CBCT image to 38 for the Catphan phantom.Conclusions:The moving‐blockerbased strategy can successfully correct MV and kV scatter simultaneously in CBCT projection data acquired with concurrent VMAT delivery.This work was supported in part by a grant from the Cancer Prevention and Research Institute of Texas (RP130109) and a grant from the American Cancer Society (RSG‐13‐326‐01‐CCE).

SU-D-BRF-04: Digital Tomosynthesis for Improved Daily Setup in Treatment of Liver Lesions

Purpose: Daily localization of liver lesions with cone-beam CT (CBCT) is difficult due to poor image quality caused by scatter, respiratory motion, and the lack of radiographic contrast between the liver parenchyma and the lesion(s). Digital tomosynthesis (DTS) is investigated as a modality to improve liver visualization and lesion/parenchyma contrast for daily setup. Methods: An in-house tool was developed to generate DTS images using a point-by-point filtered back-projection method from on-board CBCT projection data. DTS image planes are generated in a user defined orientation to visualize the anatomy at various depths. Reference DTS images are obtained from forward projection of the planning CT dataset at each projection angle. The CBCT DTS image set can then be registered to the reference DTS image set as a means for localization. Contour data from the planning CT's associate RT Structure file and forward projected similarly to the planning CT data. DTS images are created for each contoured structure, which can then be overlaid onto the DTS images for organ volume visualization. Results: High resolution DTS images generated from CBCT projections show fine anatomical detail, including small blood vessels, within the patient. However, the reference DTS images generated from forward projection of the planningmore » CT lacks this level of detail due to the low resolution of the CT voxels as compared to the pixel size in the projection images; typically 1mm-by-1mm-by-3mm (lat, vrt, lng) for the planning CT vs. 0.4mm-by-0.4mm for CBCT projections. Overlaying of the contours onto the DTS image allows for visualization of structures of interest. Conclusion: The ability to generate DTS images over a limited range of projection angles allows for reduction in the amount of respiratory motion within each acquisition. DTS may provide improved visualization of structures and lesions as compared to CBCT for highly mobile tumors.« less

Noise correlation in CBCT projection data and its application for noise reduction in low‐dose CBCT

To study the noise correlation properties of cone-beam CT (CBCT) projection data and to incorporate the noise correlation information to a statistics-based projection restoration algorithm for noise reduction in low-dose CBCT. In this study, the authors systematically investigated the noise correlation properties among detector bins of CBCT projection data by analyzing repeated projection measurements. The measurements were performed on a TrueBeam onboard CBCT imaging system with a 4030CB flat panel detector. An anthropomorphic male pelvis phantom was used to acquire 500 repeated projection data at six different dose levels from 0.1 to 1.6 mAs per projection at three fixed angles. To minimize the influence of the lag effect, lag correction was performed on the consecutively acquired projection data. The noise correlation coefficient between detector bin pairs was calculated from the corrected projection data. The noise correlation among CBCT projection data was then incorporated into the covariance matrix of the penalized weighted least-squares (PWLS) criterion for noise reduction of low-dose CBCT. The analyses of the repeated measurements show that noise correlation coefficients are nonzero between the nearest neighboring bins of CBCT projection data. The average noise correlation coefficients for the first- and second-order neighbors are 0.20 and 0.06, respectively. The noise correlation coefficients are independent of the dose level. Reconstruction of the pelvis phantom shows that the PWLS criterion with consideration of noise correlation (PWLS-Cor) results in a lower noise level as compared to the PWLS criterion without considering the noise correlation (PWLS-Dia) at the matched resolution. At the 2.0 mm resolution level in the axial-plane noise resolution tradeoff analysis, the noise level of the PWLS-Cor reconstruction is 6.3% lower than that of the PWLS-Dia reconstruction. Noise is correlated among nearest neighboring detector bins of CBCT projection data. An accurate noise model of CBCT projection data can improve the performance of the statistics-based projection restoration algorithm for low-dose CBCT.

Spatial frequency spectrum of the x‐ray scatter distribution in CBCT projections

X-ray scatter is a source of significant image quality loss in cone-beam computed tomography (CBCT). The use of Monte Carlo (MC) simulations separating primary and scattered photons has allowed the structure and nature of the scatter distribution in CBCT to become better elucidated. This work seeks to quantify the structure and determine a suitable basis function for the scatter distribution by examining its spectral components using Fourier analysis. The scatter distribution projection data were simulated using a CBCT MC model based on the EGSnrc code. CBCT projection data, with separated primary and scatter signal, were generated for a 30.6 cm diameter water cylinder [single angle projection with varying axis-to-detector distance (ADD) and bowtie filters] and two anthropomorphic phantoms (head and pelvis, 360 projections sampled every 1°, with and without a compensator). The Fourier transform of the resulting scatter distributions was computed and analyzed both qualitatively and quantitatively. A novel metric called the scatter frequency width (SFW) is introduced to determine the scatter distribution's frequency content. The frequency content results are used to determine a set basis functions, consisting of low-frequency sine and cosine functions, to fit and denoise the scatter distribution generated from MC simulations using a reduced number of photons and projections. The signal recovery is implemented using Fourier filtering (low-pass Butterworth filter) and interpolation. Estimates of the scatter distribution are used to correct and reconstruct simulated projections. The spatial and angular frequencies are contained within a maximum frequency of 0.1 cm(-1) and 7/(2π) rad(-1) for the imaging scenarios examined, with these values varying depending on the object and imaging setup (e.g., ADD and compensator). These data indicate spatial and angular sampling every 5 cm and π/7 rad (~25°) can be used to properly capture the scatter distribution, with reduced sampling possible depending on the imaging scenario. Using a low-pass Butterworth filter, tuned with the SFW values, to denoise the scatter projection data generated from MC simulations using 10(6) photons resulted in an error reduction of greater than 85% for the estimating scatter in single and multiple projections. Analysis showed that the use of a compensator helped reduce the error in estimating the scatter distribution from limited photon simulations by more than 37% when compared to the case without a compensator for the head and pelvis phantoms. Reconstructions of simulated head phantom projections corrected by the filtered and interpolated scatter estimates showed improvements in overall image quality. The spatial frequency content of the scatter distribution in CBCT is found to be contained within the low frequency domain. The frequency content is modulated both by object and imaging parameters (ADD and compensator). The low-frequency nature of the scatter distribution allows for a limited set of sine and cosine basis functions to be used to accurately represent the scatter signal in the presence of noise and reduced data sampling decreasing MC based scatter estimation time. Compensator induced modulation of the scatter distribution reduces the frequency content and improves the fitting results.

Extracting respiratory signals from thoracic cone beam CT projections

The patient respiratory signal associated with the cone beam CT (CBCT) projections is important for lung cancer radiotherapy. In contrast to monitoring an external surrogate of respiration, such a signal can be extracted directly from the CBCT projections. In this paper, we propose a novel local principal component analysis (LPCA) method to extract the respiratory signal by distinguishing the respiration motion-induced content change from the gantry rotation-induced content change in the CBCT projections. The LPCA method is evaluated by comparing with three state-of-the-art projection-based methods, namely the Amsterdam Shroud method, the intensity analysis method and the Fourier-transform-based phase analysis method. The clinical CBCT projection data of eight patients, acquired under various clinical scenarios, were used to investigate the performance of each method. We found that the proposed LPCA method has demonstrated the best overall performance for cases tested and thus is a promising technique for extracting a respiratory signal. We also identified the applicability of each existing method.