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Purpose: To develop an angular correction methodology and characterize a high‐resolution complementary metal‐oxide‐semiconductor (CMOS) array for patient specific quality assurance on a robotic arm linear accelerator. Methods: Beam path files from the treatment planning software (TPS) were used to calculate the angle of radiation beam with respect to the detector plane. Beams from multiple discrete angles were delivered to the CMOS detector array and an angular dependency look up table (LUT) was created. The LUT was then used to correct for the angular dependency of the detector. An iso‐centric 5 mm fixed cone, non iso‐centric multi‐target fixed cone, 10 mm Iris and a multi‐leaf collimator (MLC) based collimated plan were delivered to the phantom and compared to the TPS with and without angular correction applied. Additionally, the CMOS array was compared to gafchromic film and a diode array. Results: Large errors of up to 30% were observed for oblique angles. When angular correction was applied, the gamma passing rate increased from 99.2% to 100% (average gamma value decreased from 0.29 to 0.14) for the 5‐mm iso‐centric cone plan. Similarly, the passing rate increased from 84.0% to 100% for the Iris plan and from 49.98% to 98.4% for the MLC plan when angular correction was applied. For the multi‐target plan, applying angular correction improved the gamma passing rate from 94% to 99.6%. The 5 mm iso‐centric fixed cone plan was also delivered to film, and the gamma passing rate was 91.3% when using gafchromic film as the reference dataset, whereas the diode array provided insufficient sampling for this plan. Conclusion: A methodology of calculating the beam angle based on the beam path files was developed and validated. The array was demonstrated to be superior to other quality assurance tools because of its sub‐millimeter spatial resolution and immediate read out of the results. [ABSTRACT FROM AUTHOR]

Purpose: Deep inspiration breath‐hold (DIBH) is crucial in reducing the lung and cardiac dose for treatment of left‐sided breast cancer. We compared the stability and reproducibility of two DIBH techniques: Active Breathing Coordinator (ABC) and VisionRT (VRT). Materials and Methods: We examined intra‐ and inter‐fraction positional variation of the left lung. Eight left‐sided breast cancer patients were monitored with electronic portal imaging during breath‐hold (BH) at every fraction. For each patient, half of the fractions were treated using ABC and the other half with VRT, with an equal amount starting with either ABC or VRT. The lung in each portal image was delineated, and the variation of its area was evaluated. Intrafraction stability was evaluated as the mean coefficient of variation (CV) of the lung area for the supraclavicular (SCV) and left lateral (LLat) field over the course of treatment. Reproducibility was the CV for the first image of each fraction. Daily session time and total imaging monitor units (MU) used in patient positioning were recorded. Results: The mean intrafraction stability across all patients for the LLat field was 1.3 ± 0.7% and 1.5 ± 0.9% for VRT and ABC, respectively. Similarly, this was 1.5 ± 0.7% and 1.6 ± 0.8% for VRT and ABC, respectively, for the SCV field. The mean interfraction reproducibility for the LLat field was 11.0 ± 3.4% and 14.9 ± 6.0% for VRT and ABC, respectively. Similarly, this was 13.0 ± 2.5% and 14.8 ± 9% for VRT and ABC, respectively, for the SCV. No difference was observed in the number of verification images required for either technique. Conclusions: The stability and reproducibility were found to be comparable between ABC and VRT. ABC can have larger interfractional variation with less feedback to the treating therapist compared to VRT as shown in the increase in geometric misses at the matchline. [ABSTRACT FROM AUTHOR]

Background: Active breathing coordinator (ABC)‐assisted deep inspiration breath hold (DIBH) is an important organ sparing radiation therapy (RT) technique for left‐sided breast cancer patients. Patients with advanced breast cancer undergoing chest wall and regional nodal irradiation often require a field matching technique. While field matching has been demonstrated to be safe and effective in free breathing patients, its safety and accuracy in DIBH/ABC use has not been previously reported. Purpose: To report the accuracy, feasibility, and safety of field matching with ABC/DIBH for patients receiving breast/chest wall irradiation with nodal irradiation using a three‐field technique. Methods: From December 2012 to May 2018, breast cancer patients undergoing ABC/DIBH‐based RT at a single institution were reviewed. For each fraction, the amount of overlap/gap between the supraclavicular and the tangential field were measured and recorded. Patient characteristics, including acute and delayed skin toxicities, were analyzed. Results: A total of 202 patients utilized ABC/DIBH and 4973 fractions had gap/overlap measurements available for analysis. The average gap/overlap measured at junction was 0.28 mm ± 0.99 mm. A total of 72% of fractions had no measurable gap/overlap (0 mm), while 5.6% had an overlap and 22.7% a gap. There was no significant trend for worsening or improvement of gap/overlap measurements with increasing fraction number per patient. OSLD measurements were compared to the planned dose. The median dose 1 cm above the junction was 106% ± 7% of planned dose (range 94%–116%). One centimeter below the junction, the median dose was 114% ± 11% of planned dose (range 95%–131%). At the junction, the median dose was 106% ± 16.3% of planned dose (range 86%–131%). Acute skin toxicity was similar to historically reported values (grade 3, 5.4%, grade 4, 0%). Conclusion: ABC‐assisted DIBH is a safe and technically feasible method of delivering RT in the setting of complex matching field technique for breast and regional nodal treatments. [ABSTRACT FROM AUTHOR]

Purpose: Many radiotherapy modalities can deliver concentrated radiation in the form of spots, such as Gamma Knife (GK), GammaPod (GP), intensity‐modulated proton therapy, and brachytherapy, and can be generalized as spot‐based treatments. These treatments have a great therapeutic advantage of creating potent target dose while sparing the surrounding normal tissues. However, global optimization to determine the spot positions, shapes, and intensities is an intractable combinatorial problem for any real 3D problem. The conventional approach adopts heuristic spot selection and intensity optimization in a sequential manner to mitigate the problem complexity. In this work, we propose a novel framework that enables global optimization of spot‐based treatment planning. Methods: The framework is based on kernel decomposition (KD) dose calculation, which models each spot dose as a scaled shift‐invariant kernel, with the reference kernels and scales pre‐calculated. During optimization, the framework incorporates Fast Fourier Transform (FFT) for objective and derivative evaluations and accommodates all spot candidates in optimization search with a temporal complexity of O(N3log N) as opposed to O(N6) complexity in the conventional beamlet framework for volume dimensions of N × N × N. We demonstrated the FFT framework using simulations with different objectives. The framework's planning performance was illustrated using clinical GK and GP cases. Results: Pre‐processing involves only a small number of reference kernels and a scale map for the KD model with marginal spatial and temporal overheads. For simulations with 512 × 512 image dimensions, plan optimization finished in ∼2 seconds with FFT, whereas it took 100× longer with the beamlet approach. For clinical cases, the FFT attained solutions within a minute with improved plan quality compared to clinical plans: better conformity and less integral dose because of using a global fine search space for optimal spots. Conclusions: The scaled shift‐invariance and FFT framework opens a new paradigm for spot‐based treatment planning, as it can substantially reduce both the spatial and temporal complexities. The framework makes global optimization for spot‐based treatment planning clinically feasible. [ABSTRACT FROM AUTHOR]

Purpose: Adaptive radiotherapy (ART), especially online ART, effectively accounts for positioning errors and anatomical changes. One key component of online ART process is accurately and efficiently delineating organs at risk (OARs) and targets on online images, such as cone beam computed tomography (CBCT). Direct application of deep learning (DL)‐based segmentation to CBCT images suffered from issues such as low image quality and limited available contour labels for training. To overcome these obstacles to online CBCT segmentation, we propose a registration‐guided DL (RgDL) segmentation framework that integrates image registration algorithms and DL segmentation models. Methods: The RgDL framework is composed of two components: image registration and RgDL segmentation. The image registration algorithm transforms/deforms planning contours that were subsequently used as guidance by the DL model to obtain accurate final segmentations. We had two implementations of the proposed framework—Rig‐RgDL (Rig for rigid body) and Def‐RgDL (Def for deformable)—with rigid body (RB) registration or deformable image registration (DIR) as the registration algorithm, respectively, and U‐Net as the DL model architecture. The two implementations of RgDL framework were trained and evaluated on seven OARs in an institutional clinical head‐and‐neck dataset. Results: Compared to the baseline approaches using the registration or the DL alone, RgDLs achieved more accurate segmentation, as measured by higher mean Dice similarity coefficients (DSCs) and other distance‐based metrics. Rig‐RgDL achieved a DSC of 84.5% on seven OARs on average, higher than RB or DL alone by 4.5% and 4.7%. The average DSC of Def‐RgDL was 86.5%, higher than DIR or DL alone by 2.4% and 6.7%. The inference time required by the DL model component to generate final segmentations of seven OARs was less than 1 s in RgDL. By examining the contours from RgDLs and DL case by case, we found that RgDL was less susceptible to image artifacts. We also studied how the performances of RgDL and DL vary with the size of the training dataset. The DSC of DL dropped by 12.1% as the number of training data decreased from 22 to 5, whereas RgDL only dropped by 3.4%. Conclusion: By incorporating the patient‐specific registration guidance to a population‐based DL segmentation model, RgDL framework overcame the obstacles associated with online CBCT segmentation, including low image quality and insufficient training data, and achieved better segmentation accuracy than baseline methods. The resulting segmentation accuracy and efficiency show promise for applying this RgDL framework for online ART. [ABSTRACT FROM AUTHOR]

Purpose: To develop a method that can extend dose from two isodose surfaces (isosurfaces) to the entire patient volume, and to demonstrate its application in radiotherapy plan isodose tuning. Methods: We hypothesized that volumetric dose distribution can be extended from two isosurfaces—the 100% isosurface and a reference isosurface—with the distances to these two surfaces (L100${L_{100}}$ and Lref${L_{{\rm{ref}}}}$) as extension variables. The extension function is modeled by a three‐dimensional lookup table (LUT), where voxel dose values from clinical plans are binned by three indexes: L100${L_{100}}$, Lref${L_{{\rm{ref}}}}$, and Dref${D_{{\rm{ref}}}}$ (reference dose level). The mean and standard deviation of voxel doses in each bin are calculated and stored in LUT. Volumetric dose extension is performed voxel‐wisely by indexing the LUT with the L100${L_{100}}$, Lref${L_{{\rm{ref}}}}$, and Dref${D_{{\rm{ref}}}}$ of each query voxel. The mean dose stored in the corresponding bin is filled into the query voxel as extended dose, and the standard deviation be filled voxel‐wisely as the uncertainty of extension result. We applied dose extension in isodose tuning, which aims to tune volumetric dose distribution by isosurface dragging. We adopted extended dose as an approximate dose estimation, and combined it with dose correction strategy to achieve accurate dose tuning. Results: We collected 32 post‐operative prostate volumetric‐modulated arc therapy (VMAT) cases and built the LUT and its associated uncertainties from the doses of 27 cases. The dose extension method was tested on five cases, whose dose distributions were defined as ground truth (GT). We extended the doses from 100% and 50% GT isosurfaces to the entire volume, and evaluated the accuracy of extended doses. The 5 mm/5% gamma passing rate (GPR) of extended doses are 92.0%. The mean error is 4.5%, which is consistent to the uncertainty estimated by LUT. The dose difference in 90.5% of voxels is within two sigma and 97.5% in three sigma. The calculation time is less than 2 seconds. To simulate plan isodose tuning, we optimized a dose with less sparing on rectum (than GT dose) and defined it as a "base dose"—the dose awaiting isosurface dragging. In front‐end, the simulated isodose tuning is conducted as such that the base dose was given to plan tuner, and its 50% isosurface would be dragged to the desired position (position of 50% isosurface in GT dose). In back‐end, the output of isodose tuning is obtained by (1) extending dose from the desired isosurfaces and viewed the extended dose as an approximate dose, (2) obtaining a correction map from the base dose, and (3) applying the correction map to the extended dose. The accuracy of output—extended dose with correction—was 97.2% in GPR (3 mm/3%) and less than 1% in mean dose difference. The total calculation time is less than 2 seconds, which allows for interactive isodose tuning. Conclusions: We developed a dose extension method that generates volumetric dose distribution from two surfaces. The application of dose extension is in interactive isodose tuning. The distance‐based LUT fashion and correction strategy guarantee the computation efficiency and accuracy in isodose tuning. [ABSTRACT FROM AUTHOR]

Purpose: Pre‐calculation of accurate dose deposition kernels for treatment planning of spot‐based radiotherapies, such as Gamma Knife (GK) and Gamma Pod (GP), can be very time‐consuming and may require large data storage with an enormous number of possible spots. We proposed a novel kernel decomposition (KD) model to address accurate and fast (real‐time) dose calculation with reduced data storage requirements for spot‐based treatment planning. The application of the KD model was demonstrated for clinical GK and GP radiotherapy platforms. Methods: The dose deposition kernel at each spot (shot position) is modeled as the product of a shift‐invariant kernel based on a reference kernel and spatially variant scale factor. The reference kernel, one for each collimator, is defined at the center of the commissioning phantom for GK and at the center of the treatment target for GP and calculated using the Monte Carlo (MC) method. The spatially variant scale factor is defined as the ratio of the mean tissue maximum ratio (TMR) at the candidate shot position to that at the reference kernel position, and the mean TMR map is calculated within the entire volume through parallel beam ray tracing on the density image followed by averaging over all source directions. The proposed KD dose calculations were compared with the MC method and with the GK and GP treatment planning system (TPS) computations for various shot positions and collimator sizes utilizing a phantom and 14 and 12 clinical plans for GK and GP, respectively. Results: For the phantom study, the KD Gamma index (3%/1 mm) passing rates were greater than 99% (median 100%) relative to the MC doses, except for the shots close to the boundary. The passing rates dropped below 90% for 8 mm (16 mm) shots positioned within ∼1 cm (∼2 cm) of the boundary. For the clinical GK plans, the KD Gamma passing rates were greater than 99% (median 100%) compared to the MC and greater than 92% (median 99%) compared to the TPS. For the clinical GP plans, the KD Gamma passing rates were greater than 95% (median 98%) compared to the MC and greater than 91% (median 97%) compared to the TPS. The scale factors were calculated in sub‐seconds with GPU implementation and only need to be calculated once before treatment plan optimization. The calculation of the dose kernel was also within sub‐seconds without requiring beam‐by‐beam calculation commonly done in the TPS. Conclusion: The proposed model can provide an accurate dose and enables real‐time dose and derivative calculations by kernel shifting and scaling without pre‐calculating or requiring large data storage for GK and GP dose deposition kernels during treatment planning. This model could be useful for spot‐based radiotherapy treatment planning by allowing an efficient global fine search for optimal spots. [ABSTRACT FROM AUTHOR]

Purpose: Stereotactic radiosurgery (SRS) has become a primary treatment for multiple brain metastases (BM) but may require distribution of BMs over several sessions to make delivery time and radiation toxicity manageable. Contrasting to equal fraction dose in conventional fractionation, distributed SRS delivers full dose to a subset of BMs in each session while avoiding adjacent BMs in the same session to reduce toxicity from overlapping radiation. However, current clinical treatment planning for distributed SRS relies on manual BM assignment, which can be tedious and error prone. This work describes a novel approach to automate the distribution of BM in the Gamma Knife (GK) clinical workflow. Methods: We represent each BM as an electrostatic field of the same polarity that exerts repulsive forces on other BMs in the same session. This representation naturally leads to separation of close BMs into different sessions to lower the potential energy. Indeed, the BM distribution problem can be formulated as minimization of the total potential energy from all treatment sessions subject to delivery time constraints in mixed‐integer quadratic programming (MIQP). We retrospectively studied eight clinical GK cases of multiple BM and compared the automated MIQP solution with clinically used BM distribution to demonstrate the efficacy of the proposed approach. Results: With the problem size equal to the number of BMs times the number of sessions, this MIQP can be solved in a minute on a personal workstation. The MIQP solution effectively separated BMs for a given number of treatment sessions and evened out the delivery time distribution among sessions. Compared to the clinically used manual BM distributions in paired t‐test for a similar range of delivery time variation, the automated BM distributions had lower energy objectives (range of decrease: [11% 89%]; median: 25%; P=.073), more uniformly distributed treatment volumes (range of decrease for the normalized standard deviation of volume distribution: [0.02 0.95]; median: 0.16; P=.013), more scattered BMs in each treatment session (range of increase for the mean minimum BM distance: [0 14] mm; median: 6 mm; P=.008), and lower overall V12 (range of decrease: [0.0 1.6] cc; median: 0.2 cc; P=.052). Moreover, without distribution, that is, with all BMs treated in the same session, V12 was substantially larger compared to both manual and automated BM distributions; the increase ranged from 0.1 to 16.6 cc with a median of 1.3 cc. Conclusions: The proposed approach models the clinical practice and provides an efficient solution for optimal selection of BM subsets for distributed SRS. Further evaluations are underway to establish this approach as a tool for improving clinical workflow and to facilitate systematic study on the benefits of distributed SRS treatments. [ABSTRACT FROM AUTHOR]

Purpose: GammaPod, a stereotactic partial breast irradiator allowing highly conformal radiation dose delivery, has its unique mechanical design and treatment planning system (TPS). However, the uniqueness of the system poses challenges regarding initial GammaPod system commissioning and routine quality assurance (QA). In this study, we report POD‐DOSI, a dedicated dosimetry system for accurate and efficient commissioning and QA of GammaPod. Materials and methods: The POD‐DOSI system consists of two subsystems, POD‐Scanner and POD‐Calculator. The POD‐Scanner is an automatic ion‐chamber positioning system driven by two translational stepper motors for anterior–posterior, longitudinal, and lateral beam scanning. The stepper motors are controlled by a microcomputer through an in‐house‐developed graphical user interface, which can be remotely accessed by a laptop via wireless connection. The POD‐Calculator is a commissioned GPU‐based Monte Carlo dose calculation engine, which calculates dose by transporting particles from phase space constructed for GammaPod. In our institution, the POD‐DOSI system was used for GammaPod TPS commissioning and dose verification. The POD‐Calculator was further developed as a secondary dose calculation tool performing patient‐specific plan QA before each treatment. Results: The POD‐DOSI system has been fully evaluated and tested, both mechanically and dosimetrically, and applied successfully to drive the commissioning of our GammaPod system. The POD‐Scanner achieved 0.1 mm accuracy in ion‐chamber positioning tests. The POD‐Calculator generated dose profiles matched well with water phantom measurements and TPS calculations to <0.5 mm accuracy. For end‐to‐end test on 56 different treatment plans, in‐water point dose measurements by POD‐Scanner were within ±2.20% of the doses calculated by POD‐Calculator (range: −2.01% to 2.20%, mean: 0.04%, std_dev: 1.10%). Correspondingly, when switching the calculation medium from water to breast tissue, the point doses calculated by the POD‐Calculator were within ±1.60% of the point doses calculated by the GammaPOD TPS (range: −1.59% to 1.51%, mean: −0.02%, std_dev: 0.73%). The average three‐dimensional gamma passing rate between the GammaPod TPS doses and the POD‐Calculator doses was 97.10% under the 2%/1 mm gamma criteria. The POD‐DOSI system substantially shortened the GammaPod dosimetry commissioning time from weeks to days. Conclusion: The developed POD‐DOSI system resolves the challenges and streamlines the process of GammaPod commissioning and QA. It improves the efficiency and accuracy for both GammaPod commissioning and routine patient‐specific QA. [ABSTRACT FROM AUTHOR]

Purpose: Stereotactic radiosurgery (SRS) has become a standard of care for patients' with brain metastases (BMs). However, the manual multiple BMs delineation can be time‐consuming and could create an efficiency bottleneck in SRS workflow. There is a clinical need for automatic delineation and quantitative evaluation tools. In this study, building on our previous developed deep learning‐based segmentation algorithms, we developed a web‐based automated BMs segmentation and labeling platform to assist the SRS clinical workflow. Method: This platform was developed based on the Django framework, including a web client and a back‐end server. The web client enables interactions as database access, data import, and image viewing. The server performs the segmentation and labeling tasks including: skull stripping; deep learning‐based BMs segmentation; and affine registration‐based BMs labeling. Additionally, the client can display BMs contours with corresponding atlas labels, and allows further postprocessing tasks including: (a) adjusting window levels; (b) displaying/hiding specific contours; (c) removing false‐positive contours; (d) exporting contours as DICOM RTStruct files; etc. Results: We evaluated this platform on 10 clinical cases with BMs number varied from 12‐81 per case. The overall operation took about 4–5 min per patient. The segmentation accuracy was evaluated between the manual contour and automatic segmentation with several metrics. The averaged center of mass shift was 1.55 ± 0.36 mm, the Hausdorff distance was 2.98 ± 0.63 mm, the mean of surface‐to‐surface distance (SSD) was 1.06 ± 0.31 mm, and the standard deviation of SSD was 0.80 ± 0.16 mm. In addition, the initial averaged false‐positive over union (FPoU) and false‐negative rate (FNR) were 0.43 ± 0.19 and 0.15 ± 0.10 respectively. After case‐specific postprocessing, the averaged FPoU and FNR were 0.19 ± 0.10 and 0.15 ± 0.10 respectively. Conclusion: The evaluated web‐based BMs segmentation and labeling platform can substantially improve the clinical efficiency compared to manual contouring. This platform can be a useful tool for assisting SRS treatment planning and treatment follow‐up. [ABSTRACT FROM AUTHOR]

The aim of this study was to report a single‐institution experience and commissioning data for Elekta VersaHD linear accelerators (LINACs) for photon beams in the Eclipse treatment planning system (TPS). Two VersaHD LINACs equipped with 160‐leaf collimators were commissioned. For each energy, the percent‐depth‐dose (PDD) curves, beam profiles, output factors, leaf transmission factors and dosimetric leaf gaps (DLGs) were acquired in accordance with the AAPM task group reports No. 45 and No. 106 and the vendor‐supplied documents. The measured data were imported into Eclipse TPS to build a VersaHD beam model. The model was validated by creating treatment plans spanning over the full‐spectrum of treatment sites and techniques used in our clinic. The quality assurance measurements were performed using MatriXX, ionization chamber, and radiochromic film. The DLG values were iteratively adjusted to optimize the agreement between planned and measured doses. Mobius, an independent LINAC logfile‐based quality assurance tool, was also commissioned both for routine intensity‐modulated radiation therapy (IMRT) QA and as a secondary check for the Eclipse VersaHD model. The Eclipse‐generated VersaHD model was in excellent agreement with the measured PDD curves and beam profiles. The measured leaf transmission factors were less than 0.5% for all energies. The model validation study yielded absolute point dose agreement between ionization chamber measurements and Eclipse within ±4% for all cases. The comparison between Mobius and Eclipse, and between Mobius and ionization chamber measurements lead to absolute point dose agreement within ±5%. The corresponding 3D dose distributions evaluated with 3%global/2mm gamma criteria resulted in larger than 90% passing rates for all plans. The Eclipse TPS can model VersaHD LINACs with clinically acceptable accuracy. The model validation study and comparisons with Mobius demonstrated that the modeling of VersaHD in Eclipse necessitates further improvement to provide dosimetric accuracy on par with Varian LINACs. [ABSTRACT FROM AUTHOR]

Purpose: The flattening filter (FF) has traditionally been used to flatten beams or create uniform fields in conformal and intensity‐modulated radiation therapy (IMRT) but reduces the dose rate. Many studies have confirmed improvement in delivery efficiency by removing the FF, also known as flattening filter free (FFF), especially for small field stereotactic body radiation therapy (SBRT); but it is unclear if large treatment fields still favor the FFF beam. We propose a novel, unified approach to quantify delivery efficiency of the FFF and flattened beams. Methods: We modeled the FF effect by inverse conical filters and systematically studied delivery efficiency (beam‐on time, BOT) by varying the filter thickness, including the FF and FFF mode. We formulated the BOT of different beams for any arbitrary fluence map in linear programming to solve the optimal inverse conical filter that minimizes the BOT. One‐dimensional optimal filters of minimum BOT were also derived in closed form for conical fluence to gain insight for arbitrary clinical fluence maps. We evaluated the BOT of the FFF beam and flattened beam for conformal treatment fields of various dimensions ranging from 5 cm × 5 cm to 25 cm × 25 cm. We also analyzed the BOT for 698 clinical IMRT prostate fluence maps of field size 10 cm × 10 cm, 17 head‐and‐neck fluence maps of field size 15 cm × 15 cm, and additional realistic test data from 90° rotation and up to 40 cm × 40 cm enlargement of these clinical fluence maps, which were all initially generated with flattened beams. Results: The FFF beam minimized the BOT for A field size less than 20 cm in single leaf pair cases and for conformal fields of dimension less than 20 cm × 20 cm. The FFF beam also minimized the BOT for all tested prostate and head‐and‐neck cases. The median BOT ratios of the FFF beam to the flattened beam were 0.56 and 0.61 for prostate and head‐and‐neck cases, respectively. The FFF beam minimized the BOT for field size up to 30 cm × 30 cm and had similar BOTs to those of the flattened beam for field size greater than 30 cm × 30 cm in those clinically realistic test data. Conclusion: The filter modeling and BOT calculation enable us to quantify delivery efficiency of the FFF beam and flattened beam in a unified approach. The FFF beam minimized the BOT both theoretically and in simulations for all clinically relevant field sizes and fluence maps in IMRT. The results for conformal fields imply that the FFF beam requires less BOT than the flattened beam for volumetric modulated arc therapy (VMAT) treatments. The delivery efficiency consideration favors the FFF beam in intensity‐modulated treatments and may eventually lead to removal of the FF in all future linear accelerator head designs. [ABSTRACT FROM AUTHOR]

Purpose To design and construct a three-dimensional (3D) anthropomorphic abdominal phantom for geometric accuracy and dose summation accuracy evaluations of deformable image registration ( DIR) algorithms for adaptive radiation therapy ( ART). Method Organ molds, including liver, kidney, spleen, stomach, vertebra, and two metastasis tumors, were 3D printed using contours from an ovarian cancer patient. The organ molds were molded with deformable gels made of different mixtures of polyvinyl chloride ( PVC) and the softener dioctyl terephthalate. Gels with different densities were obtained by a polynomial fitting curve that described the relation between the Hounsfield unit ( HU) and PVC-softener blending ratio. The rigid vertebras were constructed by molding of white cement and cellulose pulp. The final abdominal phantom was assembled by arranging all the fabricated organs inside a hollow dummy according to their anatomies, and sealed by deformable gel with averaged HU of muscle and fat. Fiducial landmarks were embedded inside the phantom for spatial accuracy and dose accumulation accuracy studies. Two channels were excavated to facilitate ionization chamber insertion for dosimetric measurements. Phantom properties such as deformable gel elasticity and HU stability were studied. The dosimetric measurement accuracy in the phantom was performed, and the DIR accuracies of three DIR algorithms available in the open source DIR toolkit- DIRART were also validated. Results The constructed deformable gel showed elastic behavior and was stable in HU values over times, proving to be a practical material for the deformable phantom. The constructed abdominal phantom consisted of realistic anatomies in terms of both anatomical shapes and densities when compared with its reference patient. The dosimetric measurements showed a good agreement with the calculated doses from the treatment planning system. Fiducial-based accuracy analysis conducted on the constructed phantom demonstrated the feasibility of applying the phantom for organ-wise DIR accuracy assessment. Conclusions We have designed and constructed an anthropomorphic abdominal deformable phantom with satisfactory elastic property, realistic organ density, and anatomy. This physical phantom can be used for routine validations of DIR geometric accuracy and dose accumulation accuracy in ART. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate an algorithm for real-time 3D tumor localization from a single x-ray projection image for lung cancer radiotherapy. Methods: Recently, we have developed an algorithm for reconstructing volumetric images and extracting 3D tumor motion information from a single x-ray projection [Li et al., Med. Phys. 37, 2822-2826 (2010)]. We have demonstrated its feasibility using a digital respiratory phantom with regular breathing patterns. In this work, we present a detailed description and a comprehensive evaluation of the improved algorithm. The algorithm was improved by incorporating respiratory motion prediction. The accuracy and efficiency of using this algorithm for 3D tumor localization were then evaluated on (1) a digital respiratory phantom, (2) a physical respiratory phantom, and (3) five lung cancer patients. These evaluation cases include both regular and irregular breathing patterns that are different from the training dataset. Results: For the digital respiratory phantom with regular and irregular breathing, the average 3D tumor localization error is less than 1 mm which does not seem to be affected by amplitude change, period change, or baseline shift. On an NVIDIA Tesla C1060 graphic processing unit (GPU) card, the average computation time for 3D tumor localization from each projection ranges between 0.19 and 0.26 s, for both regular and irregular breathing, which is about a 10% improvement over previously reported results. For the physical respiratory phantom, an average tumor localization error below 1 mm was achieved with an average computation time of 0.13 and 0.16 s on the same graphic processing unit (GPU) card, for regular and irregular breathing, respectively. For the five lung cancer patients, the average tumor localization error is below 2 mm in both the axial and tangential directions. The average computation time on the same GPU card ranges between 0.26 and 0.34 s. Conclusions: Through a comprehensive evaluation of our algorithm, we have established its accuracy in 3D tumor localization to be on the order of 1 mm on average and 2 mm at 95 percentile for both digital and physical phantoms, and within 2 mm on average and 4 mm at 95 percentile for lung cancer patients. The results also indicate that the accuracy is not affected by the breathing pattern, be it regular or irregular. High computational efficiency can be achieved on GPU, requiring 0.1-0.3 s for each x-ray projection. [ABSTRACT FROM AUTHOR]

Density of fine mode particles is an important physical parameter. However, its measurement and validation in the actual atmosphere are still difficult. In this study, the measurements at Shouxian, Anhui province, China (32.56°N, 116.78°E, elevation 22.7 m) in winter, are used for density retrieval and quality control. The effective density and number size distribution of fine mode aerosol can be obtained in real time, by using the scanning mobility particle sizer and aerodynamic particle sizer. During the experiment, the average density is about 1.66 ± 0.15 g/cm3 with the uncertainty of 0.19 g/cm3 calculated from the residuals in the overlap region of scanning mobility and aerodynamic particle sizers. A multi‐instrument measurement and quality control scheme is proposed to identify large measurement uncertainties. The extinction and scattering coefficients calculated from the merged fine particle number size distributions by Mie theory are comparable with the measurements from cavity‐enhanced albedometer. The differences between the calculated and observed scattering and extinction coefficients are less than 2%. Meanwhile, the deduced aerosol complex refractive index n and k are obtained by minimizing the sum of squared residuals of extinction and scattering coefficients and comparable with the values retrieved from the Sun‐sky radiometer CE318 measurements. The correlation coefficient on n is 0.72. However, the correlation on k is not optimized, explained by the influence of anthropogenic aerosol particles with strong absorption near ground and the poor accuracy of k. Compared with independent observations, a good agreement between the optical parameters is also obtained. Key Points: The merging algorithm to achieve the real‐time retrieval of fine mode aerosol density and number size distribution is improvedA method for estimating the uncertainties of retrieved densities and merged number size distributions from the fit residuals is proposedA scheme of multi‐instrument observation and quality control is proposed [ABSTRACT FROM AUTHOR]

Purpose: Image reconstruction and motion model estimation in four-dimensional cone-beam CT (4D-CBCT) are conventionally handled as two sequential steps. Due to the limited number of projections at each phase, the image quality of 4D-CBCT is degraded by view aliasing artifacts, and the accuracy of subsequent motion modeling is decreased by the inferior 4D-CBCT. The objective of this work is to enhance both the image quality of 4D-CBCT and the accuracy of motion model estimation with a novel strategy enabling simultaneous motion estimation and image reconstruction (SMEIR). Methods: The proposed SMEIR algorithm consists of two alternating steps: (1) model-based iterative image reconstruction to obtain a motion-compensated primary CBCT (m-pCBCT) and (2) motion model estimation to obtain an optimal set of deformation vector fields (DVFs) between the m-pCBCT and other 4D-CBCT phases. The motion-compensated image reconstruction is based on the simultaneous algebraic reconstruction technique (SART) coupled with total variation minimization. During the forward- and backprojection of SART, measured projections from an entire set of 4D-CBCT are used for reconstruction of the m-pCBCT by utilizing the updated DVF. The DVF is estimated by matching the forward projection of the deformed m-pCBCT and measured projections of other phases of 4D-CBCT. The performance of the SMEIR algorithm is quantitatively evaluated on a 4D NCAT phantom. The quality of reconstructed 4D images and the accuracy of tumor motion trajectory are assessed by comparing with those resulting from conventional sequential 4D-CBCT reconstructions (FDK and total variation minimization) and motion estimation (demons algorithm). The performance of the SMEIR algorithm is further evaluated by reconstructing a lung cancer patient 4D-CBCT. Results: Image quality of 4D-CBCT is greatly improved by the SMEIR algorithm in both phantom and patient studies. When all projections are used to reconstruct a 3D-CBCT by FDK, motion-blurring artifacts are present, leading to a 24.4% relative reconstruction error in the NACT phantom. View aliasing artifacts are present in 4D-CBCT reconstructed by FDK from 20 projections, with a relative error of 32.1%. When total variation minimization is used to reconstruct 4D-CBCT, the relative error is 18.9%. Image quality of 4D-CBCT is substantially improved by using the SMEIR algorithm and relative error is reduced to 7.6%. The maximum error (MaxE) of tumor motion determined from the DVF obtained by demons registration on a FDK-reconstructed 4D-CBCT is 3.0, 2.3, and 7.1 mm along left-right (L-R), anterior-posterior (A-P), and superior-inferior (S-I) directions, respectively. From the DVF obtained by demons registration on 4D-CBCT reconstructed by total variation minimization, the MaxE of tumor motion is reduced to 1.5, 0.5, and 5.5 mm along L-R, A-P, and S-I directions. From the DVF estimated by SMEIR algorithm, the MaxE of tumor motion is further reduced to 0.8, 0.4, and 1.5 mm along L-R, A-P, and S-I directions, respectively. Conclusions: The proposed SMEIR algorithm is able to estimate a motion model and reconstruct motion-compensated 4D-CBCT. The SMEIR algorithm improves image reconstruction accuracy of 4D-CBCT and tumor motion trajectory estimation accuracy as compared to conventional sequential 4D-CBCT reconstruction and motion estimation. [ABSTRACT FROM AUTHOR]

Carbon dioxide buildup in large-scale reactors can be detrimental to cell growth and productivity. In case of protein X, a therapeutic glycoprotein, when cultures were scaled up from bench scale to the pilot plant, there was a 40% loss of specific productivity. The dissolved CO(2) (dCO(2)) level was 179 +/- 9 mmHg at the pilot plant scale and 68 +/- 13 mmHg at bench scale. The authors proposed a comprehensive approach to maintain dCO(2) levels between 40 and 120 mmHg throughout the 14-day fed-batch process. A cell-free experiment was used to investigate the impact of the following parameters on dCO(2) removal: (1) sparge rate, (2) agitator speed, (3) bubble size, (4) bicarbonate concentration, (5) impeller position, and (6) aeration rate at the headspace of bioreactor. dCO(2) was measured using a fiber optic based probe. dCO(2) removal rate was a strong function of sparge rate and a weak function of agitator speed. Bubble size was modulated by the presence or absence of a sparge stone (10 microm pore size, 1 cm pipe i.d.). Open pipe provided 3- to 4-fold better dCO(2) removal for the same mass transfer coefficient (k(L)a) value. A mathematical model and a bench-scale experiment indicated that the benefit of a lower level of sodium bicarbonate in the culture medium was transient for batch and fed-batch cultures. Thus, this strategy was not used at pilot scale. Decreasing top impeller position improved k(L)a of dCO(2) by 2-fold. Changing headspace aeration rate from 0.02 to 0.04 vvm had no impact on dCO(2) removal. Two pilot runs were conducted using (A) open pipe and (B) antifoam in the presence of sparge stone, both in conjunction with lower impeller position. The presence of antifoam may interfere in product purification; however, demonstration of antifoam removal can be difficult. Open pipe allowed an alternative to using antifoam, as foam level with open pipe was significantly less. Both strategies successfully reduced dCO(2) level by 2.5-fold (179 +/- 9 vs 72 +/- 9 mmHg). Titer at day 10 of culture improved by 1.5-fold. Specific productivity improved by 41%. Historically, cultures were harvested around day 9-11 because of the high amount of foam; both strategies allowed the cultures to be extended up to day 14, resulting in 2-fold higher titer compared to that of the historical control without compromising protein quality.