DICOM-RT Structure Research Articles

Open RT Structures: A Solution for TG-263 Accessibility

BackgroundConsistency of nomenclature within radiation oncology is increasingly important as big data efforts and data sharing become more feasible. Automation of radiation oncology workflows depends on standardized contour nomenclature which enables toxicity and outcomes research, while also reducing medical errors and facilitating quality improvement activities. Recommendations for standardized nomenclature have been published in the American Association of Physicists in Medicine (AAPM) report from Task Group 263. Transitioning to TG-263 requires creation and management of structure template libraries and retraining of staff, which can be a considerable burden on clinical resources. Our aim is to develop a program that allows users to create TG-263 compliant structure templates in English, Spanish, or French to facilitate data sharing. Methods53 pre-made structure templates were arranged by treated organ based on an American Society for Radiation Oncology (ASTRO) consensus paper. Templates were further customized with common target structures, relevant OARs (e.g., Spleen for anatomically relevant sites such as gastroesophageal junction or stomach), sub-site specific templates (e.g. partial breast, whole breast, intact prostate, postoperative prostate, etc.) and brachytherapy templates. An informal consensus on OAR and target coloration was also achieved, though color selections are fully customizable within the program. ResultsThe resulting program is usable on any Windows system and generates template files in practice-specific DICOM or XML formats, extracting standardized structure nomenclature from an online database maintained by members of the TG-263U1 Task Group which ensures continuous access to up-to-date templates. ConclusionsWe have developed a tool to easily create and name DICOM-RT structures sets that are TG-263-compliant for all planning systems utilizing the DICOM standard. The program and source code are publicly available via GitHub, encouraging feedback from community users for improvement and guide further development.

Failure Mode and Effects Analysis Prior to the Introduction of AI Generated GTVs for Brain Metastases in the Clinical Workflow

AI autosegmentation of organs-at-risk (OARs) is common practice at many radiotherapy clinics. Despite the abundance of gross tumor volume (GTV) autosegmentation algorithms, adoption in clinical care has been slow due to the high risk associated with errors in GTV delineation. Here we present a failure mode and effects analysis (FMEA) to evaluate the risk associated with introducing AI derived GTVs in patients treated with stereotactic radiosurgery (SRS). An AI GTV autosegmentation algorithm for brain metastases was developed in-house based on a V-Net 3D CNN. Registered CT and MR images and a contour of the brain are input into the software and all identified lesions are returned in a DICOM-RT structure set. Following algorithm evaluation, a workflow was developed to enable AI GTV autosegmentation to be introduced clinically for every SRS patient. The following steps were added to existing procedures: 1) workflow to send CT/MR and brain structure to external server, 2) autosegmentation run on the server, 3) AI GTV structures with a standard nomenclature added to existing OAR structure set, and 4) MD review, editing, and approval of AI GTVs. After successfully completing the physics evaluation testing of the new process, we formed a team of 10 faculty and staff including physicists, residents, physicians, and planners to perform the FMEA prior to clinical implementation. The team met to map the process, identify potential failure modes, and score their frequency of occurrence, severity, and detectability. A 3-point scale (1, 3, or 5) was used to simplify the scoring process. Occurrence was defined as rare, sometimes, or often; severity as low, medium, or high; and detectability as obvious, possible, or challenging. The risk probability numbers (RPNs) were calculated and the steps in the process with the highest RPNs were flagged for further discussion. The FMEA team completed their process map and analysis primarily in 4 meetings. The process map began with acquisition of the patients CT simulation scan and ended with physician approval of final volumes for treatment planning. We identified 17 process steps and 72 possible failure modes, of which 26 were associated with the new workflow. Eighteen failure modes had an RPN greater than 30 (highest risk score in at least one category) and were flagged to assess mitigation strategies. Five were unique to the new AI GTV workflow and mitigation strategies will be designed prior to clinical use. Those involved risks related to inaccurate AI GTV contours, false positives, and an incomplete review stemming from over-reliance by team members on AI. AI is increasingly being employed at every step of radiotherapy to automate and streamline processes. The FMEA analysis resulted in the identification of the riskiest parts of using AI GTV autosegmentation. This can be an effective tool in the development of checks to ensure that GTV autosegmentation methods can be safely introduced in support of patient care.

PyRaDiSe: A Python package for DICOM-RT-based auto-segmentation pipeline construction and DICOM-RT data conversion

Background and objective: Despite fast evolution cycles in deep learning methodologies for medical imaging in radiotherapy, auto-segmentation solutions rarely run in clinics due to the lack of open-source frameworks feasible for processing DICOM RT Structure Sets. Besides this shortage, available open-source DICOM RT Structure Set converters rely exclusively on 2D reconstruction approaches leading to pixelated contours with potentially low acceptance by healthcare professionals. PyRaDiSe, an open-source, deep learning framework independent Python package, addresses these issues by providing a framework for building auto-segmentation solutions feasible to operate directly on DICOM data. In addition, PyRaDiSe provides profound DICOM RT Structure Set conversion and processing capabilities; thus, it applies also to auto-segmentation-related tasks, such as dataset construction for deep learning model training.Methods: The PyRaDiSe package follows a holistic approach and provides DICOM data handling, deep learning model inference, pre-processing, and post-processing functionalities. The DICOM data handling allows for highly automated and flexible handling of DICOM image series, DICOM RT Structure Sets, and DICOM registrations, including 2D-based and 3D-based conversion from and to DICOM RT Structure Sets. For deep learning model inference, extending given skeleton classes is straightforwardly achieved, allowing for employing any deep learning framework. Furthermore, a profound set of pre-processing and post-processing routines is included that incorporate partial invertibility for restoring spatial properties, such as image origin or orientation.Results: The PyRaDiSe package, characterized by its flexibility and automated routines, allows for fast deployment and prototyping, reducing efforts for auto-segmentation pipeline implementation. Furthermore, while deep learning model inference is independent of the deep learning framework, it can easily be integrated into famous deep learning frameworks such as PyTorch or Tensorflow. The developed package has successfully demonstrated its capabilities in a research project at our institution for organs-at-risk segmentation in brain tumor patients. Furthermore, PyRaDiSe has shown its conversion performance for dataset construction.Conclusions: The PyRaDiSe package closes the gap between data science and clinical radiotherapy by enabling deep learning segmentation models to be easily transferred into clinical research practice. PyRaDiSe is available on https://github.com/ubern-mia/pyradise and can be installed directly from the Python Package Index using pip install pyradise.

Evaluation of an Automated Algorithm for Design of 2-piece Molds for Patient-specific, Flexible Bolus

In external beam radiation therapy, use of sheet bolus can lead to compromises in set-up reproducibility and surface conformity, especially in areas of anatomical complexity. Patient-specific, flexible bolus has been shown to improve those aspects; however, manual fabrication involves using molds, which requires 3D modeling expertise and relies heavily on the skills of the technician. The aim of this work is to demonstrate that using 3D printed 2-piece molds, designed with an automated software algorithm, we were able to easily produce flexible silicone bolus for a variety of geometries, with accuracy in bolus thickness comparable to generic flexible bolus sheets, but with superior surface conformity. An anthropomorphic and a solid water phantom were CT scanned. TPS tools were used to create bolus structures for four anatomical sites (scalp, face, ear and breast) and one slab bolus. DICOM CT and contour data was then imported into the mold generating software. The automated algorithm for design of 2-piece mold STL files was then executed: mold design type was selected; a pouring hole was cut in the resulting mold structure and alignment guides were placed. Both block and shell mold designs were produced for each anatomical site. Mold STL files for 3D printing were exported, along with DICOM RT structure set files, for TPS-based verification of the resulting bolus structure accuracy. Once molds were 3D-printed, silicone rubber was poured. The thicknesses of the patient-specific bolus and a set of generic bolus sheets were measured with an ultrasonic thickness gauge. Patient-specific silicone boluses were positioned on phantoms and CT scanned; this process was repeated with standard bolus sheets taped to cover the same regions. Surface conformity was evaluated and compared. Silicone bolus HU homogeneity was evaluated from the CT data. Mold design took approximately five minutes, with an additional five minutes for TPS-based verification of the resulting bolus structure. 63% of all sheet bolus thickness measurements were found to fall within +/- 0.0 - 0.5 mm of the expected 5 mm and 10 mm sheet thickness and this was taken as the criterion for 3D printed mold-based bolus comparison. All silicone mold-based bolus thickness measurements fell within this criterion, for all anatomic and slab bolus sites and CT images demonstrate superior surface conformity for all anatomical site bolus. Mean silicone bolus density was 1.05 ± 0.04 g/cc and HU uniformity was 150 ± 19 HU. The automated algorithm for designing 2-piece molds is shown to be a simple and versatile method for producing spatially accurate, flexible, patient-specific bolus. 3D-printed mold-based silicone bolus provide superior patient fit and comparable thickness variation when compared to sheet bolus, and also demonstrate clinically acceptable radiological properties.

RADI-19. EVALUATION OF BRAIN METASTASIS LOCAL CONTROL POST RADIOSURGERY VIA MACHINE LEARNING AND RADIOMICS

Stereotactic radiosurgery can be used to treat multiple, surgically inaccessible, metastatic brain lesions in a single, minimally invasive outpatient procedure. For brain metastasis, stereotactic radiosurgery can provide excellent local control depending on the robustness of the treatment plan. Previous studies have been performed correlating key radiation planning factors to higher local control probability such as tumor size and maximum dose. However, a separate non-inferiority study demonstrated that higher prescription isodose lines (in excess of 70% or higher) did not correlate to local failure. The previous works were limited to shallow feature levels regarding only the dicom plan information and lacked a predictive model. In order to address these conflicting conclusions and to support clinical decision making, we propose a radiosurgery informatics pipeline to support testing these hypotheses with observational data. First, a multidisciplinary team generated a mind-map of relevant information to inform database design. Portions of this mind-map were implemented in a relational database system (PorstgreSQL), and populated with information from 1024 patients treated for brain metastasis via stereotactic radiosurgery. Clinical information were derived from curated databases and the array of intervention variables were mined from the DICOM RT plans, structure sets, images and dose via MATLAB scripts. These factors include, but are not limited to, radiation dosimetry, prior whole brain radiation, radiomic imaging features, prior radiosurgery status, and physician determined local control status. From this pipeline, we plan to use a multi-level feature-based supervised machine learning approach that will be created via boosting to predict local control in patients using local failure timing, or lack thereof, provided by physician. To control for local failure observer bias, an unsupervised machine learning model via random trees will be created to predict clusters of patient parameters with similar local control rates.

DVH Analytics: A DVH database for clinicians and researchers.

In this study, we build a vendor‐agnostic software application capable of importing and analyzing non‐image‐based DICOM files for various radiation treatment modalities (i.e., DICOM RT Dose, RT Structure, and RT Plan files). Dose‐volume histogram (DVH) and planning data are imported into a SQL database, and methods are provided to manage, edit, view, and download data. Furthermore, the software provides various analytical tools for plan evaluations, plan comparisons, benchmarking, and plan outcome predictions. DVH Analytics is developed using Python, including libraries such as pydicom, dicompyler, psycopg2, SciPy, Statsmodels, and Bokeh for parsing DICOM files, computing DVHs, communicating with a PostgreSQL database, performing statistical analyses, and creating a web‐based user interface. This software is open‐source and compatible with Windows, Mac OS, and Linux. For proof‐of‐concept, a database with over 3,000 DVHs from a single physician's head & neck practice was built. From these data, differences in means, correlations, and temporal trends in dose to multiple organs‐at‐risk (OARs) were observed. Furthermore, an example of the predictive regression tool is reported, where a model was constructed to predict maximum dose to brainstem based on minimum distance from planning target volume (PTV) and treatment beam source‐to‐skin distance (SSD). With DVH Analytics, we have developed a free, open‐source software program to parse, organize, and analyze non‐image‐based DICOM data for use in a radiation oncology setting. Furthermore, this software can be used to generate statistical models for the purposes of quality control or outcome predictions and correlations.

VoxelMages: a general-purpose graphical interface for designing geometries and processing DICOM images for PENELOPE

The design and construction of geometries for Monte Carlo calculations is an error-prone, time-consuming, and complex step in simulations describing particle interactions and transport in the field of medical physics. The software VoxelMages has been developed to help the user in this task. It allows to design complex geometries and to process DICOM image files for simulations with the general-purpose Monte Carlo code PENELOPE in an easy and straightforward way. VoxelMages also allows to import DICOM-RT structure contour information as delivered by a treatment planning system. Its main characteristics, usage and performance benchmarking are described in detail.

A utility to read automatically DICOM format data for GAMOS/Geant4 simulation

Introduction The simulation of a real clinical radiotherapy treatment requires using the detailed information about patient, acceleration and plan. Modern machines can provide this information in DICOM format, but Monte Carlo codes are not able to treat it. Purpose To provide a tool to automatically read the data on patient anatomy, including RT structures, and the RT plan parameters and prepare the corresponding Monte Carlo jobs. Materials and methods We have developed in GAMOS/Geant4 framework a set of tools based on DCMTK to read a patient anatomy in DICOM format and transform the Hounsfield values into Geant4 materials. The DICOM RT Structure files are also read and combined with the anatomy to identify the voxels that are included in each structure. Another tool converts the many parameters of a RT plan into Monte Carlo parameters. Several utilities help the user check for the correct interpretation of the DICOM parameters. Results The tool has been applied to read many different DICOM patient anatomies from different providers. A full radiotherapy treatment has been simulated and the dose calculated and written in DICOM format has been compared with the one obtained using the vendor TPS. Conclusion A simple way is provided to automatically convert a radiotherapy treatment in DICOM format to GAMOS/Geant4 parameters so that real clinical treatments can be easily simulated.

SU‐F‐J‐72: A Clinical Usable Integrated Contouring Quality Evaluation Software for Radiotherapy

Purpose:To introduce the Auto Contour Evaluation (ACE) software, which is the clinical usable, user friendly, efficient and all‐in‐one toolbox for automatically identify common contouring errors in radiotherapy treatment planning using supervised machine learning techniques.Methods:ACE is developed with C# using Microsoft .Net framework and Windows Presentation Foundation (WPF) for elegant GUI design and smooth GUI transition animations through the integration of graphics engines and high dots per inch (DPI) settings on modern high resolution monitors. The industrial standard software design pattern, Model‐View‐ViewModel (MVVM) pattern, is chosen to be the major architecture of ACE for neat coding structure, deep modularization, easy maintainability and seamless communication with other clinical software. ACE consists of 1) a patient data importing module integrated with clinical patient database server, 2) a 2D DICOM image and RT structure simultaneously displaying module, 3) a 3D RT structure visualization module using Visualization Toolkit or VTK library and 4) a contour evaluation module using supervised pattern recognition algorithms to detect contouring errors and display detection results. ACE relies on supervised learning algorithms to handle all image processing and data processing jobs. Implementations of related algorithms are powered by Accord.Net scientific computing library for better efficiency and effectiveness.Results:ACE can take patient's CT images and RT structures from commercial treatment planning software via direct user input or from patients’ database. All functionalities including 2D and 3D image visualization and RT contours error detection have been demonstrated with real clinical patient cases.Conclusion:ACE implements supervised learning algorithms and combines image processing and graphical visualization modules for RT contours verification. ACE has great potential for automated radiotherapy contouring quality verification. Structured with MVVM pattern, it is highly maintainable and extensible, and support smooth connections with other clinical software tools.

Methods, software and datasets to verify DVH calculations against analytical values: Twenty years late(r).

The authors designed data, methods, and metrics that can serve as a standard, independent of any software package, to evaluate dose-volume histogram (DVH) calculation accuracy and detect limitations. The authors use simple geometrical objects at different orientations combined with dose grids of varying spatial resolution with linear 1D dose gradients; when combined, ground truth DVH curves can be calculated analytically in closed form to serve as the absolute standards. dicom RT structure sets containing a small sphere, cylinder, and cone were created programmatically with axial plane spacing varying from 0.2 to 3 mm. Cylinders and cones were modeled in two different orientations with respect to the IEC 1217 Y axis. The contours were designed to stringently but methodically test voxelation methods required for DVH. Synthetic RT dose files were generated with 1D linear dose gradient and with grid resolution varying from 0.4 to 3 mm. Two commercial DVH algorithms-pinnacle (Philips Radiation Oncology Systems) and PlanIQ (Sun Nuclear Corp.)-were tested against analytical values using custom, noncommercial analysis software. In Test 1, axial contour spacing was constant at 0.2 mm while dose grid resolution varied. In Tests 2 and 3, the dose grid resolution was matched to varying subsampled axial contours with spacing of 1, 2, and 3 mm, and difference analysis and metrics were employed: (1) histograms of the accuracy of various DVH parameters (total volume, Dmax, Dmin, and doses to % volume: D99, D95, D5, D1, D0.03 cm(3)) and (2) volume errors extracted along the DVH curves were generated and summarized in tabular and graphical forms. In Test 1, pinnacle produced 52 deviations (15%) while PlanIQ produced 5 (1.5%). In Test 2, pinnacle and PlanIQ differed from analytical by >3% in 93 (36%) and 18 (7%) times, respectively. Excluding Dmin and Dmax as least clinically relevant would result in 32 (15%) vs 5 (2%) scored deviations for pinnacle vs PlanIQ in Test 1, while Test 2 would yield 53 (25%) vs 17 (8%). In Test 3, statistical analyses of volume errors extracted continuously along the curves show pinnacle to have more errors and higher variability (relative to PlanIQ), primarily due to pinnacle's lack of sufficient 3D grid supersampling. Another major driver for pinnacle errors is an inconsistency in implementation of the "end-capping"; the additional volume resulting from expanding superior and inferior contours halfway to the next slice is included in the total volume calculation, but dose voxels in this expanded volume are excluded from the DVH. PlanIQ had fewer deviations, and most were associated with a rotated cylinder modeled by rectangular axial contours; for coarser axial spacing, the limited number of cross-sectional rectangles hinders the ability to render the true structure volume. The method is applicable to any DVH-calculating software capable of importing dicom RT structure set and dose objects (the authors' examples are available for download). It includes a collection of tests that probe the design of the DVH algorithm, measure its accuracy, and identify failure modes. Merits and applicability of each test are discussed.

SU-E-J-93: Development of Pre-Contoured Human Model Library in DICOM-RT Format for the Epidemiological Study of the Radiotherapy Patients

Purpose: Prior to 3D conformal radiation therapy planning, patient anatomy information was mostly limited to 2D beams-eye-view from the conventional simulator. To analyze the outcomes of such treatments for radiation late effects, 3D computational human models are often used in commercial treatment planning systems (TPSs). However, several underlying difficulties such as time-consuming manual delineation procedures of a large number of structures in the model have always limited its applications. Primary objective of this work was to develop a human model library for the epidemiological study by converting 3D-surface model organs to DICOM-RT format (DICOM-RT structure) using an in-house built software. We converted the ICRP reference human models to DICOM-RT models, which can be readily adopted for various dose calculations. Methods: MATLAB based code were utilized to convert the contour drawings extracted in text-format from the 3D graphic-tool, Rhinoceros into DICOM-RT structure format for 50 different organs of each model using a 16GB dual-core processor. The conversion periods were measured for each DICOM-RT models, and the reconstructed structure volumes were validated against the original 3D-surface models in the TPS. Ten reference hybrid whole-body models (8-pediatric and 2-adults) were automatically processed to create DICOM-RT computational human model library. Results: Mean contour conversion period was found to be 580 (N=2) and 394.5 (N=8) seconds for 50 organs in the adult and pediatric models respectively. A good agreement for large organs (NRMSD <1.0%) and small organs (NRMSD <7.7%) was also observed between the original volumes and corresponding DICOM-RT structure volumes of the organs. Conclusion: The ICRP reference human models were converted into DICOM-RT format to support the epidemiological study using a large cohort of conventional radiotherapy patients. Due to its DICOM-compatibility, the library may be implemented to many other different applications. We also expect to develop the library by including additional models in future.

SU‐E‐T‐370: Evaluating Plan Quality and Dose Delivery Accuracy of Tomotherapy SBRT Treatments for Lung Cancer

Purpose:This study evaluated the plan quality and dose delivery accuracy of stereotactic body radiotherapy (SBRT) helical Tomotherapy (HT) treatments for lung cancer. Results were compared with those previously reported by our group for flattening filter (FF) and flattening filter free (FFF) VMAT treatments. This work forms part of an ongoing multicentre and multisystem planning and dosimetry audit on FFF beams for lung SBRT.Methods:CT datasets and DICOM RT structures delineating the target volume and organs at risk for 6 lung cancer patients were selected. Treatment plans were generated using the HT treatment planning system. Tumour locations were classified as near rib, near bronchial tree or in free lung with prescribed doses of 48Gy/4fr, 50Gy/5fr and 54Gy/3fr respectively. Dose constraints were specified by a modified RTOG0915 protocol used for an Australian SBRT phase II trial. Plan quality was evaluated using mean PTV dose, PTV volume receiving 100% of the prescribed dose (V100%), target conformity (CI=VD100%/VPTV) and low dose spillage (LDS=VD50%/VPTV). Planned dose distributions were compared to those measured using an ArcCheck phantom. Delivery accuracy was evaluated using a gamma‐index pass rate of 95% with 3% (of max dose) and 3mm criteria.Results:Treatment plans for all patients were clinically acceptable in terms of quality and accuracy of dose delivery. The following DVH metrics are reported as averages (SD) of all plans investigated: mean PTV dose was 115.3(2.4)% of prescription, V100% was 98.8(0.9)%, CI was 1.14(0.03) and LDS was 5.02(0.37). The plans had an average gamma‐index passing rate of 99.3(1.3)%.Conclusion:The results reported in this study for HT agree within 1 SD to those previously published by our group for VMAT FF and FFF lung SBRT treatments. This suggests that HT delivers lung SBRT treatments of comparable quality and delivery accuracy as VMAT using both FF and FFF beams.

Commissioning results of an automated treatment planning verification system.

A dose calculation verification system (VS) was acquired and commissioned as a second check on the treatment planning system (TPS). This system reads DICOM CT datasets, RT plans, RT structures, and RT dose from the TPS and automatically, using its own collapsed cone superposition/convolution algorithm, computes dose on the same CT dataset. The system was commissioned by extracting basic beam parameters for simple field geometries and dose verification for complex treatments. Percent depth doses (PDD) and profiles were extracted for field sizes using jaw settings 3 × 3 cm2 ‐ 40 × 40 cm2 and compared to measured data, as well as our TPS model. Smaller fields of 1 × 1 cm2 and 2 × 2 cm2 generated using the multileaf collimator (MLC) were analyzed in the same fashion as the open fields. In addition, 40 patient plans consisting of both IMRT and VMAT were computed and the following comparisons were made: 1) TPS to the VS, 2) VS to measured data, and 3) TPS to measured data where measured data is both ion chamber (IC) and film measurements. Our results indicated for all field sizes using jaw settings PDD errors for the VS on average were less than 0.87%, 1.38%, and 1.07% for 6x, 15x, and 18x, respectively, relative to measured data. PDD errors for MLC field sizes were less than 2.28%, 1.02%, and 2.23% for 6x, 15x, and 18x, respectively. The infield profile analysis yielded results less than 0.58% for 6x, 0.61% for 15x, and 0.77% for 18x for the VS relative to measured data. Analysis of the penumbra region yields results ranging from 66.5% points, meeting the DTA criteria to 100% of the points for smaller field sizes for all energies. Analysis of profile data for field sizes generated using the MLC saw agreement with infield DTA analysis ranging from 68.8%–100% points passing the 1.5%/1.5 mm criteria. Results from the dose verification for IMRT and VMAT beams indicated that, on average, the ratio of TPS to IC and VS to IC measurements was 100.5 ± 1.9% and 100.4 ± 1.3%, respectively, while our TPS to VS was 100.1 ± 1.0%. When comparing the TPS and VS to film measurements, the average percentage pixels passing a 3%/3 mm criteria based gamma analysis were 96.6 ± 4.2% and 97 ± 5.6%, respectively. When the VS was compared to the TPS, on average 98.1 ± 5.3% of pixels passed the gamma analysis. Based upon these preliminary results, the VS system should be able to calculate dose adequately as a verification tool of our TPS.PACS number: 87.55.km

SU-D-19A-07: Dosimetric Comparison of HDR Plesiotherapy and Electron Beam Therapy for Superficial Lesions

Purpose: Large superficial (skin, soft tissue sarcoma) lesions located on curved areas are hard to treat with electrons. The Freiburg Flap (Nucletron, Netherlands) is a flexible mesh style surface which can be easily shaped to fit curved surfaces for reproducible HDR fraction delivery. To understand the fundamental dosimetric differences, a dosimetric comparison was made between HDR plesiotherapy (Freiburg applicator for lesions over 4cm) and external electron beam radiotherapy over cases with varying target curvature (both stylized and clinical cases). Methods: Four stylized cases with variable complexity were created using artificial DICOM axial CT slices and RT structures (a square and three curved structures on a 4.5cm radius cylinder). They were planned using Oncentra v4.3 and exported to Pinnacle v9.6 for electrons planning. The HDR source dwell positions were optimized for the best coverage of the targets using graphical optimization. Electron treatment plans were created in Pinnacle using the same CT and RT structures of three HDR cases with surface lesions previously treated with the Freiburg flap. The En face electron plans used 6-12 MeV electrons and 0.5–1 cm bolus was added to increase surface dose. The electron plans were prescribed to an isodose line to conform to the target. Results:more » For all lesions, the average target dose coverage was similar (D90ave of 100% for HDR vs 101% for electrons). For lesions with high curvature, the HDR coverage was better (D90 102% vs D90 97% for electron). For all cases, adjacent structures high dose region was lower for HDR than electrons (D1cc 100% for HDR vs D1cc 111% for electrons). Conclusion: HDR plesiotherapy offers excellent target conformity for superficial targets similar to electrons. However, for lesions with complex curved surfaces, HDR has the advantage to achieve better dose distributions using graphical optimization to spare adjacent normal tissue while maximizing target coverage.« less

WE-F-16A-03: 3D Printer Application in Proton Therapy: A Novel Method to Deliver Passive-Scattering Proton Beams with a Fixed Range and Modulation for SRS and SRT

Purpose: To present a novel technique to deliver passive-scattering proton beam with fixed range and modulation using a 3D printed patient-specific bolus for proton stereotactic radiosurgery and radiotherapy. Methods: A CIRS head phantom was used to simulate a patient with a small brain lesion. A custom bolus was created in the Eclipse Treatment Planning System (TPS) to compensate for the different water equivalent depths from the patient surface to the target from multiple beam directions. To simulate arc therapy, a plan was created on the initial CT using three passive-scattering proton beams with a fixed range and modulations irradiating from different angles. The DICOM-RT structure file of the bolus was exported from the TPS and converted to STL format for 3D printing. The phantom was rescanned with the printed custom bolus and head cup to verify the dose distribution comparing to the initial plan. EBT3 films were placed in the sagital plane of the target to verify the delivered dose distribution. The relative stopping power of the printing material(ABSplus-P430) was measured using the Zebra multi-plate ion chamber. Results: The relative stopping power of the 3D printing material, ABSplus-P430 was 1.05 which is almost water equivalent. The dose difference between verificationmore » CT and Initial CT is almost negligible. Film measurement also confirmed the accuracy for this new proton delivery technique. Conclusion: Our method using 3D printed range modifiers simplify the treatment delivery of multiple passive-scattering beams in treatment of small lesion in brain. This technique makes delivery of multiple beam more efficient and can be extended to allow arc therapy with proton beams. The ability to create and construct complex patient specific bolus structures provides a new dimension in creating optimized quality treatment plans not only for proton therapy but also for electron and photon therapy.« less

A database paradigm for the management of DICOM-RT structure sets using a geographic information system

We devise a paradigm for representing the DICOM-RT structure sets in a database management system, in such way that secondary calculations of geometric information can be performed quickly from the existing contour definitions. The implementation of this paradigm is achieved using the PostgreSQL database system and the PostGIS extension, a geographic information system commonly used for encoding geographical map data. The proposed paradigm eliminates the overhead of retrieving large data records from the database, as well as the need to implement various numerical and data parsing routines, when additional information related to the geometry of the anatomy is desired.

Importing Contours from DICOM-RT Structure Sets with ITK4

This paper presents code for reading contours from DICOM RT structure set files into binary labels. This is a common requirement for transferring expert manual segmentations from radiotherapy treatment planning software into external image analysis environments (such as ITK). An implementation of RT structure set importing is provided with sample data from a prostate cancer radiotherapy planning volume. This is an update to the 2009 Insight Journal paper “Importing Contours from DICOM-RT Structure Sets” which worked with ITK 3.x and a local GDCM2 install.Updates: Mar 2013 Now correctly handles holes within structures. Thanks to Sebastian Steger for the code fix.

SU-E-T-34: Three-Dimensional Dose/MU Verification Tool for Cyberknife Radiotherapy Plans

Purpose: To improve independent dose/MU verification for Cyberknife treatment plans based on three‐dimensional dose calculation including DVH and isodose display and DVH statistics. Methods: Independent voxel‐based dose calculation application was developed on MATLAB platform based on DICOM CTimages, DICOM RT structures, DICOM RT dose and Cyberknife treatment plan XML file imported from Cyberknife dedicated TPS (Multiplan) for a given case. Dose calculation algorithm reproduces standard RayTracing algorithm used in MultiPlan including body surface consideration and water‐equivalent depth density scaling. To speed up performance only voxels of preselected volume structures are calculated. In addition, voxels forming three major planes through normalization/reference point are calculated for isodose comparison on background of corresponding CTimages and selected volume structures. Results: The application displays three integral DVHs per preselected structure: original from DICOM dose file, recalculated based on original DICOM dose and DICOM structures, and calculated based on independent dose and DICOM structures. DVH statistics table shows statistically robust Dmin, Dmax and Dmean and their percentage deviations for all three modalities above. In addition, percentage dose difference histograms are displayed for every preselected structure. Conclusions: Independent plan dose/MU check is recommended or even mandatory in some countries. Conventional Cyberknife dose/MU check procedure is based on a beam dose/MU list export from Multiplan with individual beam contributions to a single reference point. This approach can not handle well plans with multiple‐lesions and/or non‐isocentric beam geometries in general due to frequent dose‐outside a field situations. Newly developed application is voxel‐based and handles well multiple‐lesion cases. Unlike conventional approach, it is also more independent as it verifies parameters such as depth, equivalent depth and off‐axis distance i.e. key parameters for dose/MU calculation. As a 3D tool it also presents results in clinically relevant patient‐specific metrics.

SU-E-I-09: Patient Organ Doses from KV-CBCT Acquisitions as Function of Patient Size and Scan Protocols.

To investigate the organ dose dependence on patient size and position resulting from an image guidance procedures. Depending upon the degree of variability between patients, the feasibility tabulating the organ dose based upon patient size and imaging acquisition procedure. Such tables could be useful in estimating and accounting for organ dose resulting from imaging procedures. Monte-Carlo methods were used to calculate radiation dose to the patient resulting from a kV CBCT imaging scan based upon CT image data. A Python program was created to generate DVHs using the calculated 3D dose matrix and the DICOM-RT Structure Set for the patient. A number of head and pelvic CT scans were used in the study, and the resulting DVHs were analyzed to determine the variation of organ dose as a function of patient size. For the head scans in supine position, the average of the mean doses for some organs, along with the standard deviation in those mean doses, are as follows:Brainstem: 0.25cGy +/- 0.02cGyBrain: 0.26cGy +/- 0.01cGyOptic Chiasm: 0.22 +/- 0.03cGyBone: 0.55 +/- 0.04cGyAnterior Skin: 0.13 +/- 0.05cGyPosterior Skin: 0.23 +/- 0.06cGyEye: 0.11cGy +/- 0.05cGyFor Pelvic scans, dose to organs such as rectum, have likewise been shown to have some variation between patients of different sizes. The results show that it is possible to express organ dose as a function of imaging procedure, imaging device, scan location, body size, and orientation. For example, using a standard head scan on an OBI 1.4 system, the dose to the eye ranged from 0.05-0.20cGy, depending upon the patient size. Considering the amount of imaging dose compared to the therapeutic dose, this study indicates that organ dose could be estimated by using tabulated values based upon the scan procedure and patient size.

Evaluation of the accuracy of 3DVH software estimates of dose to virtual ion chamber and film in composite IMRT QA

A novel patient-specific intensity modulated radiation therapy (IMRT) QA system, 3DVH software and mapcheck 2, purports to be able to use diode array-measured beam doses and the patient's DICOM RT plan, structure set, and dose files to predict the delivered 3D dose distribution in the patient for comparison to the treatment planning system (TPS) calculated doses. In this study, the composite dose to an ion chamber and film in phantom predicted by the 3DVH and mapcheck 2 system is compared to the actual measured chamber and film doses. If validated in this context, then 3DVH can be used to perform an equivalent dose analysis as that obtained with film dosimetry and ion chamber-based composite IMRT QA. This is important for those losing their ability to perform film dosimetry for true composite IMRT QA and provides a measure of confidence in the accuracy of 3DVH 3D dose calculations which may replace phantom-based IMRT QA. The dosimetric results from 15 consecutive patient-specific IMRT QA tests performed by composite field irradiation of ion chamber and EDR2 film in a solid water phantom were compared to the predicted doses for those virtual detectors based on the calculated 3D dose by the 3DVH software using mapcheck 2 measured doses of each beam within each plan. For each of the 15 cases, immediately after performing the ion chamber plus film measurements, the mapcheck 2 was used to measure the dose for each beam of the plan. The dose to the volume of the virtual ion chamber and the dose distribution in the plane of the virtual film calculated by the 3DVH software was extracted. The ratio of the measured to 3DVH or eclipse-predicted ion chamber doses was calculated. The same plane in the phantom measured using film and calculated with eclipse was exported from 3DVH and the 2D gamma metric was used to compare the relationship between the film doses and the eclipse or 3DVH predicted planar doses. Also, the 3D gamma value was calculated in the 3DVH software which compares the eclipse dose to the 3DVH predicted dose distribution. For the 2D and 3D gamma metrics, 2% dose and 2 mm distance to agreement (DTA) were used. In addition, a simple dose difference was performed using either a 2% or 3% dose difference tolerance. The mean ratio ± standard deviation of the measured vs 3DVH or vs eclipse-predicted dose to the ion chamber was 1.013 ± 0.015 and 1.003 ± 0.012, respectively. For 3DVH vs eclipse, the mean percentage of pixels failing the 3D gamma metric was 1.2% ± 1.4% while the failure rate for the 2D gamma metric was 1.1% ± 0.9%. When either 3DVH or eclipse was compared to EDR2 film, the gamma failure rate was 2.3% ± 2.0% and 1.6% ± 1.7%, respectively. Mean dose difference failures were 9%-27% ± 5%-15% for 2 or 3% dose difference tolerances, depending on the combination of systems tested. No statistically significant differences were found for any of the planar dosimetric comparisons. 3DVH + mapcheck 2 predicts the same absolute dose, the percent of pixels failing the gamma metric, and the percent of pixels failing 2% or 3% dose difference tolerance tests as one would have obtained had one made measurements in solid water phantom using an ion chamber and coronal film instead of a diode array. This is also a necessary although not sufficient condition for validation of the accuracy of 3DVH predictions of the 3D dose using beam-by-beam measurements.