Luo Ouyang

Effects of the penalty on the penalized weighted least-squares image reconstruction for low-dose CBCT

Statistical iterative reconstruction (SIR) algorithms have shown potential to substantially improve low-dose cone-beam CT (CBCT) image quality. The penalty term plays an important role in determining the performance of SIR algorithms. In this work, we quantitatively evaluate the impact of the penalties on the performance of a statistics-based penalized weighted least-squares (PWLS) iterative reconstruction algorithm for improving the image quality of low-dose CBCT. Three different edge-preserving penalty terms, exponential form anisotropic quadratic (AQ) penalty (PWLS-Exp), inverse square form AQ penalty (PWLS-InverseSqr) and total variation penalty (PWLS-TV), were compared against the conventional isotropic quadratic form penalty (PWLS-Iso) using both computer simulation and experimental studies. Noise in low-dose CBCT can be substantially suppressed by the PWLS reconstruction algorithm and edges are well preserved by both AQ- and TV-based penalty terms. The noise-resolution tradeoff measurement shows that the PWLS-Exp exhibits the best spatial resolution of all the three anisotropic penalty terms at matched noise level for reconstructing high-contrast objects. For the reconstruction of low-contrast objects, the TV-based penalty outperforms the AQ-based one with better resolution preservation at matched noise levels. Different penalty terms may be used for better edge preservation at different targeted contrast levels.

A practical cone-beam CT scatter correction method with optimized Monte Carlo simulations for image-guided radiation therapy

Cone-beam CT (CBCT) has become the standard image guidance tool for patient setup in image-guided radiation therapy. However, due to its large illumination field, scattered photons severely degrade its image quality. While kernel-based scatter correction methods have been used routinely in the clinic, it is still desirable to develop Monte Carlo (MC) simulation-based methods due to their accuracy. However, the high computational burden of the MC method has prevented routine clinical application. This paper reports our recent development of a practical method of MC-based scatter estimation and removal for CBCT. In contrast with conventional MC approaches that estimate scatter signals using a scatter-contaminated CBCT image, our method used a planning CT image for MC simulation, which has the advantages of accurate image intensity and absence of image truncation. In our method, the planning CT was first rigidly registered with the CBCT. Scatter signals were then estimated via MC simulation. After scatter signals were removed from the raw CBCT projections, a corrected CBCT image was reconstructed. The entire workflow was implemented on a GPU platform for high computational efficiency. Strategies such as projection denoising, CT image downsampling, and interpolation along the angular direction were employed to further enhance the calculation speed. We studied the impact of key parameters in the workflow on the resulting accuracy and efficiency, based on which the optimal parameter values were determined. Our method was evaluated in numerical simulation, phantom, and real patient cases. In the simulation cases, our method reduced mean HU errors from 44 to 3 HU and from 78 to 9 HU in the full-fan and the half-fan cases, respectively. In both the phantom and the patient cases, image artifacts caused by scatter, such as ring artifacts around the bowtie area, were reduced. With all the techniques employed, we achieved computation time of less than 30 s including the time for both the scatter estimation and CBCT reconstruction steps. The efficacy of our method and its high computational efficiency make our method attractive for clinical use.

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, 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. RESULTS 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. CONCLUSIONS 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.

Noise reduction in low-dose cone beam CT by incorporating prior volumetric image information

PURPOSE Repeated use of cone beam CT (CBCT) in radiotherapy introduces extra imaging dose to patients. In this work, the authors propose a method to effectively reduce the imaging dose of on-treatment CBCT by incorporating previously acquired CBCT. METHODS The Karhunen-Loève (KL) transform was used to consider the correlation among the on-treatment low-dose CBCT and prior CBCTs in the projection domain. Following the KL transform, the selected CBCT projection data were decomposed into uncorrelated, ordered principal components. Then, a penalized weighted least-squares (PWLS) criterion was applied to restore each KL component using different penalty strengths, where the penalty parameter was inversely proportional to its corresponding KL eigenvalue. Following the inverse KL transform on the processed data, the FDK algorithm was used to reconstruct the on-treatment CBCT image. The proposed algorithm was evaluated using both phantom and patient data. RESULTS The proposed algorithm demonstrated the ability to suppress noise while preserving edge information effectively. This new strategy outperforms the PWLS algorithm without considering prior information based on the noise-resolution tradeoff measurement and analyze of the reconstructed small objects. CONCLUSIONS Information extracted from previously acquired CBCT can be effectively utilized to suppress noise in on-treatment low-dose CBCT. The presented strategy can significantly lower the patient CBCT radiation dose.

Few-view cone-beam CT reconstruction with deformed prior image.

PURPOSE Prior images can be incorporated into the image reconstruction process to improve the quality of subsequent cone-beam CT (CBCT) images from sparse-view or low-dose projections. The purpose of this work is to develop a deformed prior image-based reconstruction (DPIR) strategy to mitigate the deformation between the prior image and the target image. METHODS The deformed prior image is obtained by a projection-based registration approach. Specifically, the deformation vector fields used to deform the prior image are estimated through iteratively matching the forward projection of the deformed prior image and the measured on-treatment projections. The deformed prior image is then used as the prior image in the standard prior image constrained compressed sensing (PICCS) algorithm. A simulation study on an XCAT phantom and a clinical study on a head-and-neck cancer patient were conducted to evaluate the performance of the proposed DPIR strategy. RESULTS The deformed prior image matches the geometry of the on-treatment CBCT more closely as compared to the original prior image. Consequently, the performance of the DPIR strategy from few-view projections is improved in comparison to the standard PICCS algorithm, based on both visual inspection and quantitative measures. In the XCAT phantom study using 20 projections, the average root mean squared error is reduced from 14% in PICCS to 10% in DPIR, and the average universal quality index increases from 0.88 in PICCS to 0.92 in DPIR. CONCLUSIONS The present DPIR approach provides a practical solution to the mismatch problem between the prior image and target image, which improves the performance of the original PICCS algorithm for CBCT reconstruction from few-view or low-dose projections.

Breaking bad IMRT QA practice

Agreement between planned and delivered dose distributions for patient‐specific quality assurance in routine clinical practice is predominantly assessed utilizing the gamma index method. Several reports, however, fundamentally question current IMRT QA practice due to poor sensitivity and specificity of the standard gamma index implementation. An alternative is to employ dose volume histogram (DVH)‐based metrics. An analysis based on the AAPM TG 53 and ESTRO booklet No.7 recommendations for QA of treatment planning systems reveals deficiencies in the current “state of the art” IMRT QA, no matter which metric is selected. The set of IMRT benchmark plans were planned, delivered, and analyzed by following guidance of the AAPM TG 119 report. The recommended point dose and planar dose measurements were obtained using a PinPoint ionization chamber, EDR2 radiographic film, and a 2D ionization chamber array. Gamma index criteria {3%(global),3 mm} and {3%(global),3 mm} were used to assess the agreement between calculated and delivered planar dose distributions. Next, the AAPM TG 53 and ESTRO booklet No.7 recommendations were followed by dividing dose distributions into four distinct regions: the high‐dose (HD) or umbra region, the high‐gradient (HG) or penumbra region, the medium‐dose (MD) region, and the low‐dose (LD) region. A different gamma passing criteria was defined for each region, i.e., a “divide and conquer” (D&C) gamma method was utilized. The D&C gamma analysis was subsequently tested on 50 datasets of previously treated patients. Measured point dose and planar dose distributions compared favorably with TG 119 benchmark data. For all complex tests, the percentage of points passing the conventional {3%(global),3 mm} gamma criteria was 97.2%±3.2% and 95.7%±1.2% for film and 2D ionization chamber array, respectively. By dividing 2D ionization chamber array dose measurements into regions and applying 3 mm isodose point distance and variable local point dose difference criteria of 7%, 15%, 25%, and 40% for HD, HG, MD, and LD regions, respectively, a 93.4%±2.3% gamma passing rate was obtained. Identical criteria applied using the D&C gamma technique on 50 clinical treatment plans resulted in a 97.9%±2.3% gamma passing score. Based on the TG 119 standard, meeting or exceeding the benchmark results would indicate an exemplary IMRT QA program. In contrast to TG 119 analysis, a different scrutiny on the same set of data, which follows the AAPM TG 53 and ESTRO booklet No.7 guidelines, reveals a much poorer agreement between calculated and measured dose distributions with large local point dose differences within different dose regions. This observation may challenge the conventional wisdom that an IMRT QA program is producing acceptable results. PACS number: 87.55.Qr

Optimization of the geometry and speed of a moving blocker system for cone-beam computed tomography scatter correction

Purpose X‐ray scatter is a significant barrier to image quality improvements in cone‐beam computed tomography (CBCT). A moving blocker‐based strategy was previously proposed to simultaneously estimate scatter and reconstruct the complete volume within the field of view (FOV) from a single CBCT scan. A blocker consisting of lead stripes is inserted between the X‐ray source and the imaging object, and moves back and forth along the rotation axis during gantry rotation. While promising results were obtained in our previous studies, the geometric design and moving speed of the blocker were set empirically. The goal of this work is to optimize the geometry and speed of the moving block system. Methods Performance of the blocker was examined through Monte Carlo (MC) simulation and experimental studies with various geometry designs and moving speeds. All hypothetical designs employed an anthropomorphic pelvic phantom. The scatter estimation accuracy was quantified by using lead stripes ranging from 5 to 100 pixels on the detector plane. An iterative reconstruction based on total variation minimization was used to reconstruct CBCT images from unblocked projection data after scatter correction. The reconstructed image was evaluated under various combinations of lead strip width and interspace (ranging from 10 to 60 pixels) and different moving speed (ranging from 1 to 30 pixels per projection). Results MC simulation showed that the scatter estimation error varied from 0.8% to 5.8%. Phantom experiment showed that CT number error in the reconstructed CBCT images varied from 13 to 35. Highest reconstruction accuracy was achieved when the strip width was 20 pixels and interspace was 60 pixels and the moving speed was 15 pixels per projection. Conclusions Scatter estimation can be achieved in a large range of lead strip width and interspace combinations. The moving speed does not have a very strong effect on reconstruction result if it is above 5 pixels per projection. Geometry design of the blocker affected image reconstruction accuracy more. The optimal geometry of the blocker has a strip width of 20 pixels and an interspace three times the strip width, which means 25% detector is covered by the blocker, while the optimal moving speed is 15 pixels per projection.

SU-D-12A-07: Optimization of a Moving Blocker System for Cone-Beam Computed Tomography Scatter Correction

PURPOSE A moving blocker based strategy has shown promising results for scatter correction in cone-beam computed tomography (CBCT). Different parameters of the system design affect its performance in scatter estimation and image reconstruction accuracy. The goal of this work is to optimize the geometric design of the moving block system. METHODS In the moving blocker system, a blocker consisting of lead strips is inserted between the x-ray source and imaging object and moving back and forth along rotation axis during CBCT acquisition. CT image of an anthropomorphic pelvic phantom was used in the simulation study. Scatter signal was simulated by Monte Carlo calculation with various combinations of the lead strip width and the gap between neighboring lead strips, ranging from 4 mm to 80 mm (projected at the detector plane). Scatter signal in the unblocked region was estimated by cubic B-spline interpolation from the blocked region. Scatter estimation accuracy was quantified as relative root mean squared error by comparing the interpolated scatter to the Monte Carlo simulated scatter. CBCT was reconstructed by total variation minimization from the unblocked region, under various combinations of the lead strip width and gap. Reconstruction accuracy in each condition is quantified by CT number error as comparing to a CBCT reconstructed from unblocked full projection data. RESULTS Scatter estimation error varied from 0.5% to 2.6% as the lead strip width and the gap varied from 4mm to 80mm. CT number error in the reconstructed CBCT images varied from 12 to 44. Highest reconstruction accuracy is achieved when the blocker lead strip width is 8 mm and the gap is 48 mm. CONCLUSIONS Accurate scatter estimation can be achieved in large range of combinations of lead strip width and gap. However, image reconstruction accuracy is greatly affected by the geometry design of the blocker.

TU‐G‐141‐06: Deformation Vector Fields (DVF)‐Driven Image Reconstruction for 4D‐CBCT

PURPOSE High-quality four dimensional cone-beam CT (4D-CBCT) can be obtained by deforming the planning CT in radiotherapy, where the deformation vector fields (DVF) is estimated by matching the forward projection of the deformed planning CT and measured 4D-CBCT projections. Due to the presence of scatter signal in CBCT projection images, the accuracy of DVF estimation is degraded when the sum of squared intensity difference (SSID) is used as the matching criterion. The goal of this work is to improve the estimation accuracy of DVF by using normalized correlation coefficient (NCC) as the matching criterion to obtain high quality on-treatment 4D-CBCT of lung cancer patients in radiation therapy. METHODS The DVF used to deform the planning CT is estimated by maximizing the NCC between the projections of on-treatment 4D-CBCT and the forward projection of the deformed planning CT. A non-linear conjugate gradient (NLCG) algorithm was used for the optimization. To obtain better initial DVF for NLCG optimization, demons registration was first performed between planning CT and 4D-CBCT reconstructed by total variation (TV) minimization. A 4D NCAT phantom was used to quantitatively evaluate the performance of the algorithm, where the reconstruction error is calculated as the sum of squared difference between the reconstructed 4D-CBCT and the phantom image. RESULTS NCC based DVF estimation improves the image reconstruction accuracy as compared to the results obtained using SSID criterion. The image reconstruction error of 4D-CBCT obtained by NCC criterion is 61.5% smaller than that is obtained by SSID criterion. CONCLUSION The accuracy DVF estimation and image reconstruction is substantially improved by using the NCC criterion, as compared to the results obtained by SSID criterion. High quality 4D-CBCT can be a valuable tool in image-guided radiation therapy for lung cancer patients. Cancer Prevention and Research Institute of Texas (RP110562).

SU‐E‐I‐33: Low‐Dose CBCT by Iterative Image Reconstruction Using Non‐Local Edge‐Preserving Prior

Purpose: Statistical iterative image reconstruction (SIR) algorithms are effective for reconstructing high‐quality low‐dose cone beam computed tomography(CBCT). The performance of SIR strongly depends on the prior/penalty term. In this work, we develop and evaluate an edge‐preserving penalty term in SIR that incorporates high‐order neighbors. Methods: CBCT projection data of a CatPhan 600 phantom was acquired on a Varian Acuity simulator (Varian Medical Systems, Palo Alto, CA) using two protocols: 80mA/12ms/projeciton for high dose and 10mA/10ms/projection for low dose. Low‐dose CBCTimages were reconstructed by minimizing an objective function based on the penalized weighted least‐squares (PWLS) criterion. Conventionally, the penalty term is used to enforce the smoothness constraint within a local neighborhood (e.g. up to third‐order neighbors). In this work, we propose a non‐local anisotropic quadratic penalty term that incorporates higher‐order neighbors. The relative contribution from different neighbors is controlled by the absolute difference between the selected neighbors. For neighbors associated with larger differences, equivalence between them will be discouraged. Contrast‐to‐noise ratio (CNR) at different regions of interest (ROIs) is calculated to quantitatively evaluate the proposed high‐order neighborhood penalty. Results: Noise is substantially reduced in low‐dose CBCTreconstructed using the iterative PWLS algorithm, while the edges are well‐preserved. The CNRs in selected ROIs in PWLS‐reconstructed images are higher than that of the corresponding FDK reconstructed high‐dose images. Using the higher‐order neighbor penalty, the average CNR of the selected ROIs is 24% higher than the imagereconstructed by PWLS using the penalty with the conventional local neighbor. Conclusions: A higher‐order neighborhood methodology is proposed for the penalty term in PWLS iterative image reconstruction for enhancement of low‐dose CBCTimage quality. PWLS coupled with higher‐order neighbor penalty outperforms the PWLS with conventional local neighbor penalty. This work was partially supported by American Cancer Society Institutional Research Grant (ACS‐IRG‐02‐196).