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At least 739 records · Page 41
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DOE OSTI2014

TH-E-BRE-05: Analysis of Dosimetric Characteristics in Two Leaf Motion Calculator Algorithms for Sliding Window IMRT

Purpose: The Smart leaf motion calculator (SLMC) in Eclipse treatment planning system is an advanced fluence delivery modeling algorithm as it takes into account fine MLC features including inter-leaf leakage, rounded leaf tips, non-uniform leaf thickness, and the spindle cavity etc. In this study, SLMC and traditional Varian LMC (VLMC) algorithms were investigated, for the first time, in dosimetric characteristics and delivery accuracy of sliding window (SW) IMRT. Methods: The SW IMRT plans of 51 cancer cases were included to evaluate dosimetric characteristics and dose delivery accuracy from leaf motion calculated by SLMC and VLMC, respectively. All plans were delivered using a Varian TrueBeam Linac. The DVH and MUs of the plans were analyzed. Three patient specific QA tools - independent dose calculation software IMSure, Delta4 phantom, and EPID portal dosimetry were also used to measure the delivered dose distribution. Results: Significant differences in the MUs were observed between the two LMCs (p≤0.001).Gamma analysis shows an excellent agreement between the planned dose distribution calculated by both LMC algorithms and delivered dose distribution measured by three QA tools in all plans at 3%/3 mm, leading to a mean pass rate exceeding 97%. The mean fraction of pixels with gamma < 1 of SLMC is slightly lower than that of VLMC in the IMSure and Delta4 results, but higher in portal dosimetry (the highest spatial resolution), especially in complex cases such as nasopharynx. Conclusion: The study suggests that the two LMCs generates the similar target coverage and sparing patterns of critical structures. However, SLMC is modestly more accurate than VLMC in modeling advanced MLC features, which may lead to a more accurate dose delivery in SW IMRT. Current clinical QA tools might not be specific enough to differentiate the dosimetric discrepancies at the millimeter level calculated by these two LMC algorithms. NIH/NIGMS grant U54 GM104944, Lincy Endowed Assistant Professorship.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-C-207-05: A Comparative Study of Noise-Reduction Algorithms for Low-Dose Cone-Beam Computed Tomography

Purpose: To study different noise-reduction algorithms and to improve the image quality of low dose cone beam CT for patient positioning in radiation therapy. Methods: In low-dose cone-beam CT, the reconstructed image is contaminated with excessive quantum noise. In this study, three well-developed noise reduction algorithms namely, a) penalized weighted least square (PWLS) method, b) split-Bregman total variation (TV) method, and c) compressed sensing (CS) method were studied and applied to the images of a computer–simulated “Shepp-Logan” phantom and a physical CATPHAN phantom. Up to 20% additive Gaussian noise was added to the Shepp-Logan phantom. The CATPHAN phantom was scanned by a Varian OBI system with 100 kVp, 4 ms and 20 mA. For comparing the performance of these algorithms, peak signal-to-noise ratio (PSNR) of the denoised images was computed. Results: The algorithms were shown to have the potential in reducing the noise level for low-dose CBCT images. For Shepp-Logan phantom, an improvement of PSNR of 2 dB, 3.1 dB and 4 dB was observed using PWLS, TV and CS respectively, while for CATPHAN, the improvement was 1.2 dB, 1.8 dB and 2.1 dB, respectively. Conclusion: Penalized weighted least square, total variation and compressed sensing methods were studied and compared for reducing the noise on a simulated phantom and a physical phantom scanned by low-dose CBCT. The techniques have shown promising results for noise reduction in terms of PSNR improvement. However, reducing the noise without compromising the smoothness and resolution of the image needs more extensive research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-J-89: Comparative Analysis of MIM and Velocity’s Image Deformation Algorithm Using Simulated KV-CBCT Images for Quality Assurance

Purpose: Deformable image registration (DIR) is used routinely in the clinic without a formalized quality assurance (QA) process. Using simulated deformations to digitally deform images in a known way and comparing to DIR algorithm predictions is a powerful technique for DIR QA. This technique must also simulate realistic image noise and artifacts, especially between modalities. This study developed an algorithm to create simulated daily kV cone-beam computed-tomography (CBCT) images from CT images for DIR QA between these modalities. Methods: A Catphan and physical head-and-neck phantom, with known deformations, were used. CT and kV-CBCT images of the Catphan were utilized to characterize the changes in Hounsfield units, noise, and image cupping that occur between these imaging modalities. The algorithm then imprinted these changes onto a CT image of the deformed head-and-neck phantom, thereby creating a simulated-CBCT image. CT and kV-CBCT images of the undeformed and deformed head-and-neck phantom were also acquired. The Velocity and MIM DIR algorithms were applied between the undeformed CT image and each of the deformed CT, CBCT, and simulated-CBCT images to obtain predicted deformations. The error between the known and predicted deformations was used as a metric to evaluate the quality of the simulated-CBCT image. Ideally, the simulated-CBCT image registration would produce the same accuracy as the deformed CBCT image registration. Results: For Velocity, the mean error was 1.4 mm for the CT-CT registration, 1.7 mm for the CT-CBCT registration, and 1.4 mm for the CT-simulated-CBCT registration. These same numbers were 1.5, 4.5, and 5.9 mm, respectively, for MIM. Conclusion: All cases produced similar accuracy for Velocity. MIM produced similar values of accuracy for CT-CT registration, but was not as accurate for CT-CBCT registrations. The MIM simulated-CBCT registration followed this same trend, but overestimated MIM DIR errors relative to the CT-CBCT registration.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-J-109: Evaluation of Deformable Accumulated Parotid Doses Using Different Registration Algorithms in Adaptive Head and Neck Radiotherapy

Purpose: Three deformable image registration (DIR) algorithms are utilized to perform deformable dose accumulation for head and neck tomotherapy treatment, and the differences of the accumulated doses are evaluated. Methods: Daily MVCT data for 10 patients with pathologically proven nasopharyngeal cancers were analyzed. The data were acquired using tomotherapy (TomoTherapy, Accuray) at the PLA General Hospital. The prescription dose to the primary target was 70Gy in 33 fractions.Three DIR methods (B-spline, Diffeomorphic Demons and MIMvista) were used to propagate parotid structures from planning CTs to the daily CTs and accumulate fractionated dose on the planning CTs. The mean accumulated doses of parotids were quantitatively compared and the uncertainties of the propagated parotid contours were evaluated using Dice similarity index (DSI). Results: The planned mean dose of the ipsilateral parotids (32.42±3.13Gy) was slightly higher than those of the contralateral parotids (31.38±3.19Gy)in 10 patients. The difference between the accumulated mean doses of the ipsilateral parotids in the B-spline, Demons and MIMvista deformation algorithms (36.40±5.78Gy, 34.08±6.72Gy and 33.72±2.63Gy ) were statistically significant (B-spline vs Demons, P<0.0001, B-spline vs MIMvista, p =0.002). And The difference between those of the contralateral parotids in the B-spline, Demons and MIMvista deformation algorithms (34.08±4.82Gy, 32.42±4.80Gy and 33.92±4.65Gy ) were also significant (B-spline vs Demons, p =0.009, B-spline vs MIMvista, p =0.074). For the DSI analysis, the scores of B-spline, Demons and MIMvista DIRs were 0.90, 0.89 and 0.76. Conclusion: Shrinkage of parotid volumes results in the dose increase to the parotid glands in adaptive head and neck radiotherapy. The accumulated doses of parotids show significant difference using the different DIR algorithms between kVCT and MVCT. Therefore, the volume-based criterion (i.e. DSI) as a quantitative evaluation of registration accuracy is essential besides the visual assessment by the treating physician. This work was supported in part by the grant from Chinese Natural Science Foundation (Grant No. 11105225)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-J-115: Correlation of Displacement Vector Fields Calculated by Deformable Image Registration Algorithms with Motion Parameters of CT Images with Well-Defined Targets and Controlled-Motion

Purpose: To investigate correlation of displacement vector fields (DVF) calculated by deformable image registration algorithms with motion parameters in helical axial and cone-beam CT images with motion artifacts. Methods: A mobile thorax phantom with well-known targets with different sizes that were made from water-equivalent material and inserted in foam to simulate lung lesions. The thorax phantom was imaged with helical, axial and cone-beam CT. The phantom was moved with a cyclic motion with different motion amplitudes and frequencies along the superior-inferior direction. Different deformable image registration algorithms including demons, fast demons, Horn-Shunck and iterative-optical-flow from the DIRART software were used to deform CT images for the phantom with different motion patterns. The CT images of the mobile phantom were deformed to CT images of the stationary phantom. Results: The values of displacement vectors calculated by deformable image registration algorithm correlated strongly with motion amplitude where large displacement vectors were calculated for CT images with large motion amplitudes. For example, the maximal displacement vectors were nearly equal to the motion amplitudes (5mm, 10mm or 20mm) at interfaces between the mobile targets lung tissue, while the minimal displacement vectors were nearly equal to negative the motion amplitudes. The maximal and minimal displacement vectors matched with edges of the blurred targets along the Z-axis (motion-direction), while DVF’s were small in the other directions. This indicates that the blurred edges by phantom motion were shifted largely to match with the actual target edge. These shifts were nearly equal to the motion amplitude. Conclusions: The DVF from deformable-image registration algorithms correlated well with motion amplitude of well-defined mobile targets. This can be used to extract motion parameters such as amplitude. However, as motion amplitudes increased, image artifacts increased significantly and that limited image quality and poor correlation between the motion amplitude and DVF was obtained.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-220: Computational Accuracy of Adaptive Convolution (AC) and Collapsed Cone Convolution (CCC) Algorithms in the Presence of Air Gaps

Purpose: To compare the percentage depth dose (PDD) computational accuracy of Adaptive Convolution (AC) and Collapsed Cone Convolution (CCC) algorithms in the presence of air gaps. Methods: A 30×30×30 cm{sup 3} solid water phantom with two 5cm air gaps was scanned with a CT simulator unit and exported into the Phillips Pinnacle™ treatment planning system. PDDs were computed using the AC and CCC algorithms. Photon energy of 6 MV was used with field sizes of 3×3 cm{sup 2}, 5×5 cm{sup 2}, 10×10 cm{sup 2}, 15×15 cm{sup 2}, and 20×20 cm{sup 2}. Ionization chamber readings were taken at different depths in water for all the field sizes. The percentage differences in the PDDs were computed with normalization to the depth of maximum dose (dmax). The calculated PDDs were then compared with measured PDDs. Results: In the first buildup region, both algorithms overpredicted the dose for all field sizes and under-predicted for all other subsequent buildup regions. After dmax in the three water media, AC under-predicted the dose for field sizes 3×3 and 5×5 cm{sup 2} and overpredicted for larger field sizes, whereas CCC under-predicted for all field sizes. Upon traversing the first air gap, AC showed maximum differences of –3.9%, −1.4%, 2.0%, 2.5%, 2.9% and CCC had maximum differences of −3.9%, −3.0%,–3.1%, −2.7%, −1.8% for field sizes 3×3, 5×5, 10×10, 15×15, and 20×20 cm{sup 2} respectively. Conclusion: The effect of air gaps causes a significant difference in the PDDs computed by both the AC and CCC algorithms in secondary build-up regions. AC computed larger values for the PDDs except at smaller field sizes. For CCC, the size of the errors in prediction of the PDDs has an inverse relationship with respect to field size. These effects should be considered in treatment planning where significant air gaps are encountered.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-764: Track Repeating Algorithm for Proton Therapy Applied to Intensity Modulated Proton Therapy for Head-And-Neck Patients

Purpose: To determine the suitability of fast Monte Carlo techniques for dose calculation in particle therapy based on track-repeating algorithm for Intensity Modulated Proton Therapy, IMPT. The application of this technique will make possible detailed retrospective studies of large cohort of patients, which may lead to a better determination of Relative Biological Effects from the analysis of patient data. Methods: A cohort of six head-and-neck patients treated at the University of Texas MD Anderson Cancer Center with IMPT were utilized. The dose distributions were calculated with the standard Treatment Plan System, TPS, MCNPX, GEANT4 and FDC, a fast track-repeating algorithm for proton therapy for the verification and the patient plans. FDC is based on a GEANT4 database of trajectories of protons in a water. The obtained dose distributions were compared to each other utilizing the g-index criteria for 3mm-3% and 2mm-2%, for the maximum spatial and dose differences. The γ-index was calculated for voxels with a dose at least 10% of the maximum delivered dose. Dose Volume Histograms are also calculated for the various dose distributions. Results: Good agreement between GEANT4 and FDC is found with less than 1% of the voxels with a γ-index larger than 1 for 2 mm-2%. The agreement between MCNPX with FDC is within the requirements of clinical standards, even though it is slightly worse than the comparison with GEANT4.The comparison with TPS yielded larger differences, what is also to be expected because pencil beam algorithm do not always performed well in highly inhomogeneous areas like head-and-neck. Conclusion: The good agreement between a track-repeating algorithm and a full Monte Carlo for a large cohort of patients and a challenging, site like head-and-neck, opens the path to systematic and detailed studies of large cohorts, which may yield better understanding of biological effects.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

MO-FG-204-05: Evaluation of a Novel Algorithm for Improved 4DCT Resolution

Purpose: Accurate tumor motion characterization is critical for increasing the therapeutic ratio of radiation therapy. To accommodate the divergent fan-beam geometry of the scanner, the current 4D-CT algorithm utilizes a larger temporal window to ensure that pixel values are valid throughout the entire FOV. To minimize the impact on temporal resolution, a cos{sup 2} weighting is employed. We propose a novel exponential weighting (“exponential”) 4DCT reconstruction algorithm that has a sharper slope and provides a more optimal temporal resolution. Methods: A respiratory motion platform translated a lung-mimicking Styrofoam slab with several high and low-contrast inserts 2 cm in the superior-inferior direction. Breathing rates (10–15 bpm) and couch pitch (0.06–0.1 A.U.) were varied to assess interplay between parameters. Multi-slice helical 4DCTs were acquired with 0.5 sec gantry rotation and data were reconstructed with cos{sup 2} and exponential weighting. Mean and standard deviation were calculated via region of interest analysis. Intensity profiles evaluated object boundaries. Retrospective raw data reconstructions were performed for both 4DCT algorithms for 3 liver and lung cancer patients. Image quality (temporal blurring/sharpness) and subtraction images were compared between reconstructions. Results: In the phantom, profile analysis revealed that sharper boundaries were obtained with exponential reconstructions at transitioning breathing phases (i.e. mid-inhale or mid-exhale). Reductions in full-width half maximum were ∼1 mm in the superior-inferior direction and appreciable sharpness could be observed in difference maps. This reduction also yielded a slight reduction in target volume between reconstruction algorithms. For patient cases, coronal views showed less blurring at object boundaries and local intensity differences near the tumor and diaphragm with exponential weighted reconstruction. Conclusion: Exponential weighted 4DCT offers potential for improving image sharpness in 4DCT. Comparisons with true object extent using static CTs of varied phantom positions are necessary to confirm these findings. Future work with additional patient cases is warranted. Research partially sponsored by Philips Healthcare.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

MO-FG-204-06: A New Algorithm for Gold Nano-Particle Concentration Identification in Dual Energy CT

Purpose: Gold nano-particle (GNP) has recently attracted a lot of attentions due to its potential as an imaging contrast agent and radiotherapy sensitiser. Imaging the GNP at its low contraction is a challenging problem. We propose a new algorithm to improve the identification of GNP based on dual energy CT (DECT). Methods: We consider three base materials: water, bone, and gold. Determining three density images from two images in DECT is an under-determined problem. We propose to solve this problem by exploring image domain sparsity via an optimization approach. The objective function contains four terms. A data-fidelity term ensures the fidelity between the identified material densities and the DECT images, while the other three terms enforces the sparsity in the gradient domain of the three images corresponding to the density of the base materials by using total variation (TV) regularization. A primal-dual algorithm is applied to solve the proposed optimization problem. We have performed simulation studies to test this model. Results: Our digital phantom in the tests contains water, bone regions and gold inserts of different sizes and densities. The gold inserts contain mixed material consisting of water with 1g/cm3 and gold at a certain density. At a low gold density of 0.0008 g/cm3, the insert is hardly visible in DECT images, especially for those with small sizes. Our algorithm is able to decompose the DECT into three density images. Those gold inserts at a low density can be clearly visualized in the density image. Conclusion: We have developed a new algorithm to decompose DECT images into three different material density images, in particular, to retrieve density of gold. Numerical studies showed promising results.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-G-204-09: The Effects of Reduced- Dose Lung Cancer Screening CT On Lung Nodule Detection Using a CAD Algorithm

Purpose: While Lung Cancer Screening CT is being performed at low doses, the purpose of this study was to investigate the effects of further reducing dose on the performance of a CAD nodule-detection algorithm. Methods: We selected 50 cases from our local database of National Lung Screening Trial (NLST) patients for which we had both the image series and the raw CT data from the original scans. All scans were acquired with fixed mAs (25 for standard-sized patients, 40 for large patients) on a 64-slice scanner (Sensation 64, Siemens Healthcare). All images were reconstructed with 1-mm slice thickness, B50 kernel. 10 of the cases had at least one nodule reported on the NLST reader forms. Based on a previously-published technique, we added noise to the raw data to simulate reduced-dose versions of each case at 50% and 25% of the original NLST dose (i.e. approximately 1.0 and 0.5 mGy CTDIvol). For each case at each dose level, the CAD detection algorithm was run and nodules greater than 4 mm in diameter were reported. These CAD results were compared to “truth”, defined as the approximate nodule centroids from the NLST reports. Subject-level mean sensitivities and false-positive rates were calculated for each dose level. Results: The mean sensitivities of the CAD algorithm were 35% at the original dose, 20% at 50% dose, and 42.5% at 25% dose. The false-positive rates, in decreasing-dose order, were 3.7, 2.9, and 10 per case. In certain cases, particularly in larger patients, there were severe photon-starvation artifacts, especially in the apical region due to the high-attenuating shoulders. Conclusion: The detection task was challenging for the CAD algorithm at all dose levels, including the original NLST dose. However, the false-positive rate at 25% dose approximately tripled, suggesting a loss of CAD robustness somewhere between 0.5 and 1.0 mGy. NCI grant U01 CA181156 (Quantitative Imaging Network); Tobacco Related Disease Research Project grant 22RT-0131.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Effective noise-suppressed and artifact-reduced reconstruction of SPECT data using a preconditioned alternating projection algorithm

Purpose: The authors have recently developed a preconditioned alternating projection algorithm (PAPA) with total variation (TV) regularizer for solving the penalized-likelihood optimization model for single-photon emission computed tomography (SPECT) reconstruction. This algorithm belongs to a novel class of fixed-point proximity methods. The goal of this work is to investigate how PAPA performs while dealing with realistic noisy SPECT data, to compare its performance with more conventional methods, and to address issues with TV artifacts by proposing a novel form of the algorithm invoking high-order TV regularization, denoted as HOTV-PAPA, which has been explored and studied extensively in the present work. Methods: Using Monte Carlo methods, the authors simulate noisy SPECT data from two water cylinders; one contains lumpy “warm” background and “hot” lesions of various sizes with Gaussian activity distribution, and the other is a reference cylinder without hot lesions. The authors study the performance of HOTV-PAPA and compare it with PAPA using first-order TV regularization (TV-PAPA), the Panin–Zeng–Gullberg one-step-late method with TV regularization (TV-OSL), and an expectation–maximization algorithm with Gaussian postfilter (GPF-EM). The authors select penalty-weights (hyperparameters) by qualitatively balancing the trade-off between resolution and image noise separately for TV-PAPA and TV-OSL. However, the authors arrived at the same penalty-weight value for both of them. The authors set the first penalty-weight in HOTV-PAPA equal to the optimal penalty-weight found for TV-PAPA. The second penalty-weight needed for HOTV-PAPA is tuned by balancing resolution and the severity of staircase artifacts. The authors adjust the Gaussian postfilter to approximately match the local point spread function of GPF-EM and HOTV-PAPA. The authors examine hot lesion detectability, study local spatial resolution, analyze background noise properties, estimate mean square errors (MSEs), and report the convergence speed and computation time. Results: HOTV-PAPA yields the best signal-to-noise ratio, followed by TV-PAPA and TV-OSL/GPF-EM. The local spatial resolution of HOTV-PAPA is somewhat worse than that of TV-PAPA and TV-OSL. Images reconstructed using HOTV-PAPA have the lowest local noise power spectrum (LNPS) amplitudes, followed by TV-PAPA, TV-OSL, and GPF-EM. The LNPS peak of GPF-EM is shifted toward higher spatial frequencies than those for the three other methods. The PAPA-type methods exhibit much lower ensemble noise, ensemble voxel variance, and image roughness. HOTV-PAPA performs best in these categories. Whereas images reconstructed using both TV-PAPA and TV-OSL are degraded by severe staircase artifacts; HOTV-PAPA substantially reduces such artifacts. It also converges faster than the other three methods and exhibits the lowest overall reconstruction error level, as measured by MSE. Conclusions: For high-noise simulated SPECT data, HOTV-PAPA outperforms TV-PAPA, GPF-EM, and TV-OSL in terms of hot lesion detectability, noise suppression, MSE, and computational efficiency. Unlike TV-PAPA and TV-OSL, HOTV-PAPA does not create sizable staircase artifacts. Moreover, HOTV-PAPA effectively suppresses noise, with only limited loss of local spatial resolution. Of the four methods, HOTV-PAPA shows the best lesion detectability, thanks to its superior noise suppression. HOTV-PAPA shows promise for clinically useful reconstructions of low-dose SPECT data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Technical Note: Improving the VMERGE treatment planning algorithm for rotational radiotherapy

Purpose: The authors revisit the VMERGE treatment planning algorithm by Craft et al. [“Multicriteria VMAT optimization,” Med. Phys. 39, 686–696 (2012)] for arc therapy planning and propose two changes to the method that are aimed at improving the achieved trade-off between treatment time and plan quality at little additional planning time cost, while retaining other desirable properties of the original algorithm. Methods: The original VMERGE algorithm first computes an “ideal,” high quality but also highly time consuming treatment plan that irradiates the patient from all possible angles in a fine angular grid with a highly modulated beam and then makes this plan deliverable within practical treatment time by an iterative fluence map merging and sequencing algorithm. We propose two changes to this method. First, we regularize the ideal plan obtained in the first step by adding an explicit constraint on treatment time. Second, we propose a different merging criterion that comprises of identifying and merging adjacent maps whose merging results in the least degradation of radiation dose. Results: The effect of both suggested modifications is evaluated individually and jointly on clinical prostate and paraspinal cases. Details of the two cases are reported. Conclusions: In the authors’ computational study they found that both proposed modifications, especially the regularization, yield noticeably improved treatment plans for the same treatment times than what can be obtained using the original VMERGE method. The resulting plans match the quality of 20-beam step-and-shoot IMRT plans with a delivery time of approximately 2 min.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-C-BRA-01: Interactive Auto-Segmentation for Bowel in Online Adaptive MRI-Guided Radiation Therapy by Using a Multi-Region Labeling Algorithm

Purpose: In MRI-guided online adaptive radiation therapy, re-contouring of bowel is time-consuming and can impact the overall time of patients on table. The study aims to auto-segment bowel on volumetric MR images by using an interactive multi-region labeling algorithm. Methods: 5 Patients with locally advanced pancreatic cancer underwent fractionated radiotherapy (18–25 fractions each, total 118 fractions) on an MRI-guided radiation therapy system with a 0.35 Tesla magnet and three Co-60 sources. At each fraction, a volumetric MR image of the patient was acquired when the patient was in the treatment position. An interactive two-dimensional multi-region labeling technique based on graph cut solver was applied on several typical MRI images to segment the large bowel and small bowel, followed by a shape based contour interpolation for generating entire bowel contours along all image slices. The resulted contours were compared with the physician’s manual contouring by using metrics of Dice coefficient and Hausdorff distance. Results: Image data sets from the first 5 fractions of each patient were selected (total of 25 image data sets) for the segmentation test. The algorithm segmented the large and small bowel effectively and efficiently. All bowel segments were successfully identified, auto-contoured and matched with manual contours. The time cost by the algorithm for each image slice was within 30 seconds. For large bowel, the calculated Dice coefficients and Hausdorff distances (mean±std) were 0.77±0.07 and 13.13±5.01mm, respectively; for small bowel, the corresponding metrics were 0.73±0.08and 14.15±4.72mm, respectively. Conclusion: The preliminary results demonstrated the potential of the proposed algorithm in auto-segmenting large and small bowel on low field MRI images in MRI-guided adaptive radiation therapy. Further work will be focused on improving its segmentation accuracy and lessening human interaction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-SPS-06: Implementation of a Back-Projection Algorithm for 2D in Vivo Dosimetry with An EPID System

Purpose: To implement a back-projection algorithm for 2D dose reconstructions for in vivo dosimetry in radiation therapy using an Electronic Portal Imaging Device (EPID) based on amorphous silicon. Methods: An EPID system was used to calculate dose-response function, pixel sensitivity map, exponential scatter kernels and beam hardenig correction for the back-projection algorithm. All measurements were done with a 6 MV beam. A 2D dose reconstruction for an irradiated water phantom (30×30×30 cm{sup 3}) was done to verify the algorithm implementation. Gamma index evaluation between the 2D reconstructed dose and the calculated with a treatment planning system (TPS) was done. Results: A linear fit was found for the dose-response function. The pixel sensitivity map has a radial symmetry and was calculated with a profile of the pixel sensitivity variation. The parameters for the scatter kernels were determined only for a 6 MV beam. The primary dose was estimated applying the scatter kernel within EPID and scatter kernel within the patient. The beam hardening coefficient is σBH= 3.788×10{sup −4} cm{sup 2} and the effective linear attenuation coefficient is µAC= 0.06084 cm{sup −1}. The 95% of points evaluated had γ values not longer than the unity, with gamma criteria of ΔD = 3% and Δd = 3 mm, and within the 50% isodose surface. Conclusion: The use of EPID systems proved to be a fast tool for in vivo dosimetry, but the implementation is more complex that the elaborated for pre-treatment dose verification, therefore, a simplest method must be investigated. The accuracy of this method should be improved modifying the algorithm in order to compare lower isodose curves.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-88: Comparison of Two Deformable Image Registration Algorithms for CT-To-CT Contour Propagation

Purpose: To compare the contour propagation accuracy of two deformable image registration (DIR) algorithms in the Raystation treatment planning system – the “Hybrid” algorithm based on image intensities and anatomical information; and the “Biomechanical” algorithm based on linear anatomical elasticity and finite element modeling. Methods: Both DIR algorithms were used for CT-to-CT deformation for 20 lung radiation therapy patients that underwent treatment plan revisions. Deformation accuracy was evaluated using landmark tracking to measure the target registration error (TRE) and inverse consistency error (ICE). The deformed contours were also evaluated against physician drawn contours using Dice similarity coefficients (DSC). Contour propagation was qualitatively assessed using a visual quality score assigned by physicians, and a refinement quality score (0 0.9 for lungs, > 0.85 for heart, > 0.8 for liver) and similar qualitative assessments (VQS < 0.35, RQS > 0.75 for lungs). When anatomical structures were used to control the deformation, the DSC improved more significantly for the biomechanical DIR compared to the hybrid DIR, while the VQS and RQS improved only for the controlling structures. However, while the inclusion of controlling structures improved the TRE for the hybrid DIR, it increased the TRE for the biomechanical DIR. Conclusion: The hybrid DIR was found to perform slightly better than the biomechanical DIR based on lower TRE while the DSC, VQS, and RQS studies yielded comparable results for both. The use of controlling structures showed considerable improvement in the hybrid DIR results and is recommended for clinical use in contour propagation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-148: A Collapsed Cone Algorithm Can Be Used for Quality Assurance for Monaco Treatment Plans for the MR-Linac

Purpose: Treatment plans for the MR-linac, calculated in Monaco v5.19, include direct simulation of the effects of the 1.5T B{sub 0}-field. We tested the feasibility of using a collapsed-cone (CC) algorithm in Oncentra, which does not account for effects of the B{sub 0}-field, as a fast online, independent 3D check of dose calculations. Methods: Treatment plans for six patients were generated in Monaco with a 6 MV FFF beam and the B{sub 0}-field. All plans were recalculated with a CC model of the same beam. Plans for the same patients were also generated in Monaco without the B{sub 0}-field. The mean dose (Dmean) and doses to 10% (D10%) and 90% (D90%) of the volume were determined, as percentages of the prescribed dose, for target volumes and OARs in each calculated dose distribution. Student’s t-tests between paired parameters from Monaco plans and corresponding CC calculations were performed. Results: Figure 1 shows an example of the difference between dose distributions calculated in Monaco, with the B{sub 0}-field, and the CC algorithm. Figure 2 shows distributions of (absolute) difference between parameters for Monaco plans, with the B{sub 0}-field, and CC calculations. The Dmean and D90% values for the CTVs and PTVs were significantly different, but differences in dose distributions arose predominantly at the edges of the target volumes. Inclusion of the B{sub 0}-field had little effect on agreement of the Dmean values, as illustrated by Figure 3, nor on agreement of the D10% and D90% values. Conclusion: Dose distributions recalculated with a CC algorithm show good agreement with those calculated with Monaco, for plans both with and without the B{sub 0}-field, indicating that the CC algorithm could be used to check online treatment planning for the MRlinac. Agreement for a wider range of treatment sites, and the feasibility of using the γ-test as a simple pass/fail criterion, will be investigated.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-198: A Cross-Platform Adaptation of An a Priori Scatter Correction Algorithm for Cone-Beam Projections to Enable Image- and Dose-Guided Proton Therapy

Purpose: Cone-beam CT (CBCT) imaging may enable image- and dose-guided proton therapy, but is challenged by image artefacts. The aim of this study was to demonstrate the general applicability of a previously developed a priori scatter correction algorithm to allow CBCT-based proton dose calculations. Methods: The a priori scatter correction algorithm used a plan CT (pCT) and raw cone-beam projections acquired with the Varian On-Board Imager. The projections were initially corrected for bow-tie filtering and beam hardening and subsequently reconstructed using the Feldkamp-Davis-Kress algorithm (rawCBCT). The rawCBCTs were intensity normalised before a rigid and deformable registration were applied on the pCTs to the rawCBCTs. The resulting images were forward projected onto the same angles as the raw CB projections. The two projections were subtracted from each other, Gaussian and median filtered, and then subtracted from the raw projections and finally reconstructed to the scatter-corrected CBCTs. For evaluation, water equivalent path length (WEPL) maps (from anterior to posterior) were calculated on different reconstructions of three data sets (CB projections and pCT) of three parts of an Alderson phantom. Finally, single beam spot scanning proton plans (0–360 deg gantry angle in steps of 5 deg; using PyTRiP) treating a 5 cm central spherical target in the pCT were re-calculated on scatter-corrected CBCTs with identical targets. Results: The scatter-corrected CBCTs resulted in sub-mm mean WEPL differences relative to the rigid registration of the pCT for all three data sets. These differences were considerably smaller than what was achieved with the regular Varian CBCT reconstruction algorithm (1–9 mm mean WEPL differences). Target coverage in the re-calculated plans was generally improved using the scatter-corrected CBCTs compared to the Varian CBCT reconstruction. Conclusion: We have demonstrated the general applicability of a priori CBCT scatter correction, potentially opening for CBCT-based image/dose-guided proton therapy, including adaptive strategies. Research agreement with Varian Medical Systems, not connected to the present project.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-G-BRA-02: Development of a Learning Based Block Matching Algorithm for Ultrasound Tracking in Radiotherapy

Purpose: To develop an ultrasound learning-based tracking algorithm with the potential to provide real-time motion traces of anatomy-based fiducials that may aid in the effective delivery of external beam radiation. Methods: The algorithm was developed in Matlab R2015a and consists of two main stages: reference frame selection, and localized block matching. Immediately following frame acquisition, a normalized cross-correlation (NCC) similarity metric is used to determine a reference frame most similar to the current frame from a series of training set images that were acquired during a pretreatment scan. Segmented features in the reference frame provide the basis for the localized block matching to determine the feature locations in the current frame. The boundary points of the reference frame segmentation are used as the initial locations for the block matching and NCC is used to find the most similar block in the current frame. The best matched block locations in the current frame comprise the updated feature boundary. The algorithm was tested using five features from two sets of ultrasound patient data obtained from MICCAI 2014 CLUST. Due to the lack of a training set associated with the image sequences, the first 200 frames of the image sets were considered a valid training set for preliminary testing, and tracking was performed over the remaining frames. Results: Tracking of the five vessel features resulted in an average tracking error of 1.21 mm relative to predefined annotations. The average analysis rate was 15.7 FPS with analysis for one of the two patients reaching real-time speeds. Computations were performed on an i5-3230M at 2.60 GHz. Conclusion: Preliminary tests show tracking errors comparable with similar algorithms at close to real-time speeds. Extension of the work onto a GPU platform has the potential to achieve real-time performance, making tracking for therapy applications a feasible option. This work is partially funded by NIH grant R01CA190298.

60 APPLIED LIFE SCIENCES↗