bluuebunny/crossref_metadata_2025_split huggingface dataset:
TH‐AB‐201‐09
Authors: Mirzakhanian L , Benmakhlouf H , Seuntjens J 
Published: July 2016

Purpose:To determine the k_(Q_msr,Q)^(f_msr,f_ref) factor, introduced in the small field formalism for five common type chambers used in the calibration of Leksell Gamma‐Knife Perfexion model over a range of different phantom electron densities.Methods:Five chamber types including Exradin‐A16, A14SL, A14, A1SL and IBA‐CC04 are modeled in EGSnrc and PENELOPE Monte Carlo codes using the blueprints provided by the manufacturers. The chambers are placed in a previously proposed water‐filled phantom and four 16‐cm diameter spherical phantoms made of liquid water, Solid Water, ABS and polystyrene. Dose to the cavity of the chambers and a small water volume are calculated using EGSnrc/PENELOPE codes. The calculations are performed over a range of phantom electron densities for two chamber orientations. Using the calculated dose‐ratio in reference and machine specific reference field, the k_(Q_msr,Q)^(f_msr,f_ref) factor can be determined.Results:When chambers are placed along the symmetry axis of the collimator block (z‐axis), the CC04 requires the smallest correction followed by A1SL and A16. However, when detectors are placed perpendicular to z‐axis, A14SL needs the smallest and A16 the largest correction. Moreover, an increase in the phantom electron density results in a linear increase in the k_(Q_msr,Q)^(f_msr,f_ref). Depending on the chambers, the agreement between this study and a previous study performed varies between 0.05–0.70% for liquid water, 0.07–0.85% for Solid Water and 0.00–0.60% for ABS phantoms. After applying the EGSnrc‐calculated k_(Q_msr,Q)^(f_msr,f_ref) factors for A16 to the previously measured dose‐rates in liquid water, Solid Water and ABS normalized to the dose‐rate measured with TG‐21 protocol and ABS phantom, the dose‐rate ratios are found to be 1.004±0.002, 0.996±0.002 and 0.998±0.002 (3σ) respectively.Conclusion:Knowing the electron density of the phantoms, the calculated k_(Q_msr,Q)^(f_msr,f_ref) values in this work will enable users to apply the appropriate correction for their own specific phantom material.LM acknowledges partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290)
TH‐AB‐201‐11: Small‐Field Beam Quality Corrections and Their Clinical Impact for Flattened and Unflattened Fields of the TrueBeam
Authors: Hyun M , Labby Z , DeWerd L 
Published: July 2016

Purpose:To determine small‐field beam quality correction factors for several detectors used with flattened and unflattened beams of the Varian TrueBeam accelerator, and to quantify the clinical impact of these corrections through a treatment planning study.Methods:Small‐field beam quality correction factors were measured and simulated in EGSnrc for several microchambers and diodes. The Exradin W1 plastic scintillation detector was used as a reference for the measured results, and its response was validated using alanine measurements traceable to a primary standard. Symmetric, jaw‐defined field sizes ranging from 6 mm to 10 cm were used. Twelve hypofractionated clinical cases were then recalculated and/or reoptimized using beam models that were modified based on the resulting output factors. Three‐dimensional dose distributions and cumulative dose‐volume‐histogram plots were compared between the models. The impact of these measurements on the flattening‐filter‐scatter‐source (FFSS) model, and its subsequent effect on treatment plans, was also investigated.Results:The diodes exhibited over‐response in small fields, whereas the microchambers tended to under‐respond as field size decreased. Field‐size‐dependent effects were less significant for the flattening‐filter‐free beams (i.e., corrections were closer to unity) than for flattened fields of the same size. Recalculated and reoptimized plans displayed varying levels of deviation from the original dose distributions, with larger discrepancies at effective jaw settings of 3×3 cm2 or smaller. Dose distributions calculated using uncorrected microchamber output factors and/or modified FFSS showed large discrepancies in the low‐dose regions, which manifested as DVH changes for the organs at risk (OAR).Conclusion:Field‐size‐dependent beam quality corrections were measured and simulated for ionization chambers and diodes typically used in small fields. Plans calculated with models based on these measured data showed dosimetric deviations mainly out of field, which affected OAR dose metrics. Accordingly, these beam quality corrections should be accounted for during commissioning to ensure greater treatment planning accuracy.
TH‐AB‐BRB‐02: ET‐Reloaded
Authors: Wu Q. 
Published: July 2016

Current state‐of‐the art digital C‐arm medical linear accelerators are capable of delivering radiation treatments with high level of automation, which affords coordinated motions of gantry, couch, and multileaf collimator (MLC) with dose rate modulations. The new machine capacity has shown the potential to bring substantially improved radiation dosimetry and/or delivery efficiency to many challenging diseases. Combining an integrated beam orientation optimization algorithm with automated machine navigation, markedly improved dose conformity has been achieved using 4ρ therapy. Trajectory modulated radiation therapy (TMAT) can be used to deliver highly conformal dose to partial breast or to carve complex dose distribution for therapy involving extended volumes such as total marrow and total lymph node treatment. Dynamic electron arc radiotherapy (DEAR) not only overcomes the deficiencies of conventional electron therapy in dose conformity and homogeneity but also achieves so without patient‐specific shields. The combination of MLC and couch tracking provides improved motion management of thoracic and abdominal tumors. A substantial body of work has been done in these technological advances for clinical translation. The proposed symposium will provide a timely review of these exciting opportunities.

Learning Objectives:
Recognize the potential of using digitally controlled linacs for clinically significant improvements in delivered dose distributions for various treatment sites.
Identify existing approaches to treatment planning, optimization and delivery for treatment techniques utilizing the advanced functions of digital linacs and venues for further development and improvement.
Understand methods for testing and validating delivery system performance.
Identify tools available on current delivery systems for implementation and control for such treatments.
Obtain the update in clinical applications, trials and regulatory approval.K. Sheng, NIH U19AI067769, NIH R43CA183390, NIH R01CA188300, Varian Medical SystemsV. Yu, Varian Medical Systems, AAPM Summer Undergraduate Fellowship, NSF graduate fellowshipS. Nill, Elekta AB. Cancer Research UK under Programme C33589/A19727, NIHR Biomedical Research Centre at The Royal Marsden and The Institute of Cancer Research.
TH‐CD‐206‐09: Learning‐Based MRI‐CT Prostate Registration Using Spare Patch‐Deformation Dictionary
Authors: Yang X , Jani A , Rossi P , Mao H , Curran W , Liu T 
Published: July 2016

Purpose:To enable MRI‐guided prostate radiotherapy, MRI‐CT deformable registration is required to map the MRI‐defined tumor and key organ contours onto the CT images. Due to the intrinsic differences in grey‐level intensity characteristics between MRI and CT images, the integration of MRI into CT‐based radiotherapy is very challenging. We are developing a learning‐based registration approach to address this technical challenge.Methods:We propose to estimate the deformation between MRI and CT images in a patch‐wise fashion by using the sparse representation technique. Specifically, we assume that two image patches should follow the same deformation if their patch‐wise appearance patterns are similar. We first extract a set of key points in the new CT image. Then, for each key point, we adaptively construct a coupled dictionary from the training MRI‐CT images, where each coupled element includes both appearance and deformation of the same image patch. After calculating the sparse coefficients in representing the patch appearance of each key point based on the constructed dictionary, we can predict the deformation for this point by applying the same sparse coefficients to the respective deformations in the dictionary.Results:This registration technique was validated with 10 prostate‐cancer patients’ data and its performance was compared with the commonly used free‐form‐deformation‐based registration. Several landmarks in both images were identified to evaluate the accuracy of our approach. Overall, the averaged target registration error of the intensity‐based registration and the proposed method was 3.8±0.4 mm and 1.9±0.3 mm, respectively.Conclusion:We have developed a novel prostate MR‐CT registration approach based on patch‐deformation dictionary, demonstrated its clinical feasibility, and validated its accuracy. This technique will either reduce or compensate for the effect of patient‐specific treatment variation measured during the course of radiotherapy, is therefore well‐suited for a number of MRI‐guided adaptive radiotherapy, and potentially enhance prostate radiotherapy treatment outcome.
TH‐CD‐206‐10: Clinical Application of the MIND Demons Algorithm for Symmetric Diffeomorphic Deformable MR‐To‐CT Image Registration in Spinal Interventions
Authors: Reaungamornrat S , De Silva T , Uneri A , Wolinsky J , Khanna A , Kleinszig G , Vogt S , Prince J , Siewerdsen J 
Published: July 2016

Purpose.Accurate intraoperative localization of target anatomy (e.g., tumors and vertebral levels) and critical structures (e.g., nerves and vessels) is essential to safe, effective surgery. Preoperative CT/MR images can be used to identify such vital anatomy in intraoperative images through multimodality deformable registration. We proposed a deformable registration method to align preoperative MR to intraoperative CT using modality‐independent neighborhood descriptors (MIND) and a Huber metric for robust registration.Methods.The method, called MIND Demons, estimates time‐dependent diffeomorphisms between two images by optimizing a constrained symmetric energy functional incorporating priors on smoothness, conservation of momentum, geodesic length, invertibility, and closure under composition. Alternating optimization is performed using a regularized Gauss‐Newton (GN) method in a multiresolution scheme. Performance was measured in phantom experiments emulating image‐guided spine‐surgery and in a retrospective clinical study (N=15 patients) evaluated in terms of target registration error (TRE) and diffeomorphic properties of the resulting deformations.Results.The MIND Demons method outperformed free‐form deformation (FFD) methods and conventional Demons in phantom experiments (median TRE = 1.5 mm, compared to 10.2 mm for mutual information (MI) based FFD, 2.6 mm for local‐MI FFD, and 5.1 mm for normalized‐MI Demons). The resulting deformations resolved realistic deformation in clinical data with sub‐voxel TRE (<2 mm) in cases of cervical, thoracic, and lumbar spine and preserved topology with sub‐voxel invertibility (0.004 mm) and positive‐determinant nonsingular spatial‐Jacobians.Conclusion.A modality‐independent deformable registration method has been developed, incorporating the constrained symmetric energy functional and the Huber metric to yield stable and accurate registration, with computational efficiency improved using the GN optimization. The approach yielded registration accuracy suitable to application in image‐guided spine‐surgery across each region of the spine under realistic modes of deformation.S. Vogt and G. Kleinszig is with Siemens Healthcare XP.
TH‐CD‐209‐02: Beam Angle Optimization Incorporating Anatomical Heterogeneities for Pencil Beam Scanning Charged‐Particle Therapy
Authors: Toramatsu C , Inaniwa T 
Published: July 2016

Purpose:In charged particle therapy with pencil beam scanning (PBS), localization of the dose in the Bragg peak makes dose distributions sensitive to lateral tissue heterogeneities. Sensitivity of a PBS plan to lateral tissue heterogeneity can be reduced by selecting suitable beam angles. The purpose of this study is to establish a fast and accurate method of beam angle selection for PBS.Methods:The lateral tissue heterogeneity surrounding the path of respective pencil beams at a given angle was quantified with the heterogeneity number using effective density data of patients. To shorten the computation time, one dimensional dose calculation and optimization were conducted along the central axis of pencil beams originating from the scanning magnets. Calculation was performed for all fields with possible gantry and couch angles. The angles leading the minimum mean heterogeneity number was selected as the optimal beam angle. Three head and neck (H&N) cancer patients were used to evaluate our method. Dose distributions and their robustness to range and set up errors were evaluated for all tested angles, and their relation to their heterogeneity numbers were investigated.Results:Variety of heterogeneity number was clearly shown due to air cavities and bony structures in the patients. The mean value of heterogeneity number in all possible fields varied between 1.2 and 10.6 mm. By selecting a field with a lower mean value of heterogeneity number, target dose coverage and robustness of the fields were improved.Conclusion:We developed a computational method to determine a beam angle that leads good target coverage and robustness to setup errors. The developed stand‐alone computation program is simple, fast, accurate and applicable for beam angle selection in PBS therapy.
TH‐CD‐209‐03: Feasibility of CBCT‐Based Proton Dose Calculation Using a Histogram‐Matching Algorithm in Proton Beam Therapy
Authors: Arai K , Kadoya N , Kato T , Endo H , Komori S , Abe Y , Hirose K , Nakamura T , Wada H , Kikuchi Y , Jingu K 
Published: July 2016

Purpose:The aim of this study was to confirm On‐Board Imager cone‐beam computed tomography (CBCT) using a histogram‐matching algorithm as a useful method for proton dose calculation in head and neck radiotherapy.Methods:We studied one head and neck phantom and ten patients with head and neck cancer treated using intensity‐modulated radiation therapy (IMRT) and proton beam therapy. We modified Hounsfield unit (HU) values of CBCT (mCBCT) using a histogram‐matching algorithm. In order to evaluate the accuracy of the proton dose calculation, we compared dose differences in dosimetric parameters (Dmean) for clinical target volume (CTV), planning target volume (PTV) and left parotid and proton ranges (PR) between the planning CT (reference) and CBCT or mCBCT, and gamma passing rates of CBCT and mCBCT. To minimize the effect of organ deformation, we also performed image registration.Results:For patients, the average differences in Dmean for CTV, PTV, and left parotid between planning CT and CBCT were 1.63 ± 2.34%, 3.30 ± 1.02%, and 5.42 ± 3.06%, respectively. Similarly, the average differences between planning CT and mCBCT were 0.20 ± 0.19%, 0.58 ±0.43%, and 3.53 ±2.40%, respectively. The average differences in PR between planning CT and CBCT or mCBCT of a 50° beam for ten patients were 2.1 ± 2.1 mm and 0.3 ± 0.5 mm, respectively. Similarly, the average differences in PR of a 120° beam were 2.9 ± 2.6 mm and 1.1 ± 0.9 mm, respectively. The average dose and PR differences of mCBCT were smaller than those of CBCT. Additionally, the average gamma passing rates of mCBCT were larger than those of CBCT.Conclusion:We evaluated the accuracy of the proton dose calculation in CBCT and mCBCT with the image registration for ten patients. Our results showed that HU modification using a histogram‐matching algorithm could improve the accuracy of the proton dose calculation.
TH‐CD‐209‐04: Fuzzy Robust Optimization in Intensity‐Modulated Proton Therapy Planning to Account for Range and Patient Setup Uncertainties
Authors: An Y , Bues M , Schild S , Liu W 
Published: July 2016

Purpose:We propose to apply a robust optimization model based on fuzzy‐logic constraints in the intensity‐modulated proton therapy (IMPT) planning subject to range and patient setup uncertainties. The purpose is to ensure the plan robustness under uncertainty and obtain the best trade‐off between tumor dose coverage and organ‐at‐risk(OAR) sparing.Methods:Two IMPT plans were generated for 3 head‐and‐neck cancer patients: one used the planning target volume(PTV) method; the other used the fuzzy robust optimization method. In the latter method, nine dose distributions were computed ‐ the nominal one and one each for ±3mm setup uncertainties along three cardinal axes and for ±3.5% range uncertainty. For tumors, these nine dose distributions were explicitly controlled by adding hard constraints with adjustable parameters. For OARs, fuzzy constraints that allow the dose to vary within a certain range were used so that the tumor dose distribution was guaranteed by minimum compromise of that of OARs. We rendered this model tractable by converting the fuzzy constraints to linear constraints. The plan quality was evaluated using dose‐volume histogram(DVH) indices such as tumor dose coverage(D95%), homogeneity(D5%‐D95%), plan robustness(DVH band at D95%), and OAR sparing like D1% of brain and D1% of brainstem.Results:Our model could yield clinically acceptable plans. The fuzzy‐logic robust optimization method produced IMPT plans with comparable target dose coverage and homogeneity compared to the PTV method(unit: Gy[RBE]; average[min, max])(CTV D95%: 59 [52.7, 63.5] vs 53.5[46.4, 60.1], CTV D5% ‐ D95%: 11.1[5.3, 18.6] vs 14.4[9.2, 21.5]). It also generated more robust plans(CTV DVH band at D95%: 3.8[1.2, 5.6] vs 11.5[6.2, 16.7]). The parameters of tumor constraints could be adjusted to control the tradeoff between tumor coverage and OAR sparing.Conclusion:The fuzzy‐logic robust optimization generates superior IMPT with minimum compromise of OAR sparing.This research was supported by the National Cancer Institute Career Developmental Award K25CA168984, by the Fraternal Order of Eagles Cancer Research Fund Career Development Award, by The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, by Mayo Arizona State University Seed Grant, and by The Kemper Marley Foundation. eRA Person ID(s) for the Principal Investigator: 11017970 (Research Supported by National Institutes of Health)
TH‐CD‐209‐05: Impact of Spot Size and Spacing On the Quality of Robustly‐Optimized Intensity‐Modulated Proton Therapy Plans for Lung Cancer
Authors: Liu W , Schild S , Chang J , Liao Z , Ding X , Hu Y , Shen J , Korte S , Sahoo N , Wong W , Herman M , Bues M 
Published: July 2016

Purpose:To investigate how spot size and spacing affect plan quality, especially, plan robustness and the impact of interplay effect, of robustly‐optimized intensity‐modulated proton therapy (IMPT) plans for lung cancer.Methods:Two robustly‐optimized IMPT plans were created for 10 lung cancer patients: (1) one for a proton beam with in‐air energy dependent large spot size at isocenter (σ: 5–15 mm) and spacing (1.53σ); (2) the other for a proton beam with small spot size (σ: 2–6 mm) and spacing (5 mm). Both plans were generated on the average CTs with internal‐gross‐tumor‐volume density overridden to irradiate internal target volume (ITV). The root‐mean‐square‐dose volume histograms (RVH) measured the sensitivity of the dose to uncertainties, and the areas under RVH curves were used to evaluate plan robustness. Dose evaluation software was developed to model time‐dependent spot delivery to incorporate interplay effect with randomized starting phases of each field per fraction. Patient anatomy voxels were mapped from phase to phase via deformable image registration to score doses. Dose‐volume‐histogram indices including ITV coverage, homogeneity, and organs‐at‐risk (OAR) sparing were compared using Student‐t test.Results:Compared to large spots, small spots resulted in significantly better OAR sparing with comparable ITV coverage and homogeneity in the nominal plan. Plan robustness was comparable for ITV and most OARs. With interplay effect considered, significantly better OAR sparing with comparable ITV coverage and homogeneity is observed using smaller spots.Conclusion:Robust optimization with smaller spots significantly improves OAR sparing with comparable plan robustness and similar impact of interplay effect compare to larger spots. Small spot size requires the use of larger number of spots, which gives optimizer more freedom to render a plan more robust. The ratio between spot size and spacing was found to be more relevant to determine plan robustness and the impact of interplay effect than spot size alone.This research was supported by the National Cancer Institute Career Developmental Award K25CA168984, by the Fraternal Order of Eagles Cancer Research Fund Career Development Award, by The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, by Mayo Arizona State University Seed Grant, and by The Kemper Marley Foundation.
TH‐CD‐207B‐04: Is TTF a True Representation of the Sharpness Property of a Non‐Linear CT System?
Authors: Robins M , Solomon J , Samei E 
Published: July 2016

Purpose:To investigate if the task‐transfer‐function (TTF) accurately models the transfer properties of a CT system for lung nodule imaging.Methods:An anthropomorphic lung phantom was imaged using a standard chest protocol on a clinical CT scanner with and without 24 physically inserted synthetic lesions (nominal diameter: 8 – 10 mm). Images were reconstructed using FBP and iterative algorithm (SAFIRE, Siemens Healthcare). 3D TTF was measured using an established technique. Corresponding idealized virtual lesions were blurred with the TTF and superimposed onto lesion‐less phantom images. Images of the physically and virtually inserted lesions were compared in terms of rendition of spatial features (blurriness of the edges), lesion morphology, and lesion volume. Feature rendition was measured in terms root‐mean‐square (RMS) of the frequency power of the native and TTF‐transferred lesion edge transition. Morphology was assessed with the Regional Hausdorff Distance (RHD) using custom written code (MATLAB v2015b). Volumes were measured using a clinical segmentation tool (iNtution, TeraRecon).Results:The RMS was less than 0.02 and 0.05 for FBP and IR respectively. Using the nonlinear mixed effect (nlme) package (R, www.r‐project.org), the difference in RHD between virtual and physical lesions was 5% on average. There was less than 1 ± 5% (R2 > 0.97) and 3 ± 4% (R2 > 0.97) difference between the volumes of the physical lesions and the corresponding virtual lesions for FBP and IR respectively. Additionally, was closer concordance for images reconstructed with FBP than iterative reconstruction.Conclusion:The TTF was found to accurately model the transfer properties of the CT imaging system on lung lesions for both FBP and iterative reconstruction algorithms. TTF was found to offer slightly better lesion renditions when modeling images reconstructed with FBP versus iterative reconstruction. This methodology will be used in future investigation of more complex imaging tasks such as low contrast detectability of known lesions.