Y Wu

Technical Note: DIRART- A software suite for deformable image registration and adaptive radiotherapy research

Purpose: Recent years have witnessed tremendous progress in image guide radiotherapy technology and a growing interest in the possibilities for adapting treatment planning and delivery over the course of treatment. One obstacle faced by the research community has been the lack of a comprehensive open-source software toolkit dedicated for adaptive radiotherapy (ART). To address this need, the authors have developed a software suite called the Deformable Image Registration and Adaptive Radiotherapy Toolkit (DIRART). Methods: DIRART is an open-source toolkit developed in MATLAB. It is designed in an object-oriented style with focus on user-friendliness, features, and flexibility. It contains four classes of DIR algorithms, including the newer inverse consistency algorithms to provide consistent displacement vector field in both directions. It also contains common ART functions, an integrated graphical user interface, a variety of visualization and image-processing features, dose metric analysis functions, and interface routines. These interface routines make DIRART a powerful complement to the Computational Environment for Radiotherapy Research (CERR) and popular image-processing toolkits such as ITK. Results :DIRART provides a set of image processing/registration algorithms and postprocessing functions to facilitate the development and testing of DIR algorithms. It also offers a good amount of options for DIR results visualization, evaluation, and validation. Conclusions : By exchanging data with treatment planning systems via DICOM-RT files andCERR, and by bringing image registration algorithms closer to radiotherapy applications, DIRART is potentially a convenient and flexible platform that may facilitate ART and DIR research.

Technical Note: Electronic chart checks in a paperless radiation therapy clinic

PURPOSE EcCk, which stands for Electronic Chart ChecK, is a computer software and database system. It was developed to improve quality and efficiency of patient chart checking in radiation oncology departments. The core concept is to automatically collect and analyze patient treatment data, and to report discrepancies and potential concerns. METHODS EcCk consists of several different computer technologies, including relational database, DICOM, dynamic HTML, and image processing. Implemented in MATLAB and C#, EcCk processes patient data in DICOM, PDF, Microsoft Word, database, and Pinnacle native formats. Generated reports are stored on the storage server and indexed in the database. A standalone report-browser program is implemented to allow users to view reports on any computer in the department. Checks are performed according to predefined logical rules, and results are presented through color-coded reports in which discrepancies are summarized and highlighted. Users examine the reports and take appropriate actions. The core design is intended to automate human task and to improve the reliability of the performed tasks. The software is not intended to replace human audits but rather to aid as a decision support tool. RESULTS The software was successfully implemented in the clinical environment and has demonstrated the feasibility of automation of this common task with modern clinical tools. The software integrates multiple disconnected systems and successfully supports analysis of data in diverse formats. CONCLUSIONS While the human is the ultimate expert, EcCk has a significant potential to improve quality and efficiency of patient treatment record audits, and to allow verification of tasks that are not easily performed by humans. EcCk can potentially relieve human experts from simple and repetitive tasks, and allow them to work on other important tasks, and in the end to improve the quality and safety of radiation therapy treatments.

Technical Note: Deformable image registration on partially matched images for radiotherapy applications

In radiation therapy applications, deformable image registrations (DIRs) are often carried out between two images that only partially match. Image mismatching could present as superior-inferior coverage differences, field-of-view (FOV) cutoffs, or motion crossing the image boundaries. In this study, the authors propose a method to improve the existing DIR algorithms so that DIR can be carried out in such situations. The basic idea is to extend the image volumes and define the extension voxels (outside the FOV or outside the original image volume) as NaN (not-a-number) values that are transparent to all floating-point computations in the DIR algorithms. Registrations are then carried out with one additional rule that NaN voxels can match any voxels. In this way, the matched sections of the images are registered properly, and the mismatched sections of the images are registered to NaN voxels. This method makes it possible to perform DIR on partially matched images that otherwise are difficult to register. It may also improve DIR accuracy, especially near or in the mismatched image regions.

Software tool for physics chart checks.

PURPOSE Physics chart check has long been a central quality assurance (QC) measure in radiation oncology. The purpose of this work is to describe a software tool that aims to accomplish simplification, standardization, automation, and forced functions in the process. METHODS AND MATERIALS Nationally recognized guidelines, including American College of Radiology and American Society for Radiation Oncology guidelines and technical standards, and the American Association of Physicists in Medicine Task Group reports were identified, studied, and summarized. Meanwhile, the reported events related to physics chart check service were analyzed using an event reporting and learning system. A number of shortfalls in the chart check process were identified. To address these problems, a software tool was designed and developed under Microsoft. Net in C# to hardwire as many components as possible at each stage of the process. RESULTS The software consists of the following 4 independent modules: (1) chart check management; (2) pretreatment and during treatment chart check assistant; (3) posttreatment chart check assistant; and (4) quarterly peer-review management. The users were a large group of physicists in the authors radiation oncology clinic. During over 1 year of use the tool has proven very helpful in chart checking management, communication, documentation, and maintaining consistency. CONCLUSIONS The software tool presented in this work aims to assist physicists at each stage of the physics chart check process. The software tool is potentially useful for any radiation oncology clinics that are either in the process of pursuing or maintaining the American College of Radiology accreditation.

DIRART - A software suite for deformable image registration and adaptive radiotherapy research

PURPOSE Recent years have witnessed tremendous progress in image guide radiotherapy technology and a growing interest in the possibilities for adapting treatment planning and delivery over the course of treatment. One obstacle faced by the research community has been the lack of a comprehensive open-source software toolkit dedicated for adaptive radiotherapy (ART). To address this need, the authors have developed a software suite called the Deformable Image Registration and Adaptive Radiotherapy Toolkit (DIRART). METHODS DIRART is an open-source toolkit developed in MATLAB. It is designed in an object-oriented style with focus on user-friendliness, features, and flexibility. It contains four classes of DIR algorithms, including the newer inverse consistency algorithms to provide consistent displacement vector field in both directions. It also contains common ART functions, an integrated graphical user interface, a variety of visualization and image-processing features, dose metric analysis functions, and interface routines. These interface routines make DIRART a powerful complement to the Computational Environment for Radiotherapy Research (CERR) and popular image-processing toolkits such as ITK. RESULTS DIRART provides a set of image processing/registration algorithms and postprocessing functions to facilitate the development and testing of DIR algorithms. It also offers a good amount of options for DIR results visualization, evaluation, and validation. CONCLUSIONS By exchanging data with treatment planning systems via DICOM-RT files and CERR, and by bringing image registration algorithms closer to radiotherapy applications, DIRART is potentially a convenient and flexible platform that may facilitate ART and DIR research. 0 2011 Ameri-

WE‐C‐214‐04: ADQ — A Software Tool That Automatically, Autonomously, Intelligently and Instantly Verify Patient Radiation Therapy Beam Deliveries

Purpose: To effectively mitigate errors in IMRTradiation therapydelivery, all beams, all fractions, for all patients should be checked in vivo, immediately, automatically, autonomously and intelligently for integrity, quality and safety. For this purpose, ADQ or Automatic Dynalog QA, is implemented for instant and automatic patient delivery beam verification. Methods: ADQ contains multiple functional modules. DICOM receiver program is developed in C++ to receive DICOM‐RT Plans from treatment planning systems, process data and save to database. The verification tool is implemented in MATLAB that automatically validates beam parameters (gantry angle, collimator angle and positions, MLC positions, fluence maps, etc.) between the treatment plans and recorded dynamic MLC log files, generate reports for each treatment session, and send out alert emails for detected urgent problems. Report reviewer is implemented in C++ that enables physicists to review, comment and confirm reports. ADQ programs use own database and Mosaiq R&V database. Simple, automatic and no human intervention is needed unless an error is detected. Results: ADQ is running to generate near real‐time QA reports for every treatment date. DICOM receiver is running 24 hours to collect plans. Report reviewer deployed through network facilitates easy access to reports. All IMRT beams delivered to the patients are checked for a period of four months to study the reliability, MLC performance, false positive rate and importantly identify true positive. More than 80000 thousand beams from 4 different Linacs were analyzed up‐to‐date. Conclusions: We developed new software tools to improve the RT treatment QA by automatic checking patient treatment beam delivery records for each patient and each treatment session. Report data achieved in database can be easily used for further studies, for example, analysis of MLC leaf failures.

Methods to model and predict the ViewRay treatment deliveries to aid patient scheduling and treatment planning

A software tool is developed, given a new treatment plan, to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance image-guided radiation therapy (MR-IGRT) delivery system. This tool is necessary for managing patient treatment scheduling in our clinic. The predicted treatment delivery time and the assessment of plan complexities could also be useful to aid treatment planning. A patients total treatment delivery time, not including time required for localization, is modeled as the sum of four components: 1) the treatment initialization time; 2) the total beam-on time; 3) the gantry rotation time; and 4) the multileaf collimator (MLC) motion time. Each of the four components is predicted separately. The total beam-on time can be calculated using both the planned beam-on time and the decay-corrected dose rate. To predict the remain-ing components, we retrospectively analyzed the patient treatment delivery record files. The initialization time is demonstrated to be random since it depends on the final gantry angle of the previous treatment. Based on modeling the relationships between the gantry rotation angles and the corresponding rotation time, linear regression is applied to predict the gantry rotation time. The MLC motion time is calculated using the leaves delay modeling method and the leaf motion speed. A quantitative analysis was performed to understand the correlation between the total treatment time and the plan complexity. The proposed algorithm is able to predict the ViewRay treatment delivery time with the average prediction error 0.22 min or 1.82%, and the maximal prediction error 0.89 min or 7.88%. The analysis has shown the correlation between the plan modulation (PM) factor and the total treatment delivery time, as well as the treatment delivery duty cycle. A possibility has been identified to significantly reduce MLC motion time by optimizing the positions of closed MLC pairs. The accuracy of the proposed prediction algorithm is sufficient to support patient treatment appointment scheduling. This developed software tool is currently applied in use on a daily basis in our clinic, and could also be used as an important indicator for treatment plan complexity. PACS number(s): 87.55.N.A software tool is developed, given a new treatment plan, to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance image‐guided radiation therapy (MR‐IGRT) delivery system. This tool is necessary for managing patient treatment scheduling in our clinic. The predicted treatment delivery time and the assessment of plan complexities could also be useful to aid treatment planning. A patients total treatment delivery time, not including time required for localization, is modeled as the sum of four components: 1) the treatment initialization time; 2) the total beam‐on time; 3) the gantry rotation time; and 4) the multileaf collimator (MLC) motion time. Each of the four components is predicted separately. The total beam‐on time can be calculated using both the planned beam‐on time and the decay‐corrected dose rate. To predict the remain‐ing components, we retrospectively analyzed the patient treatment delivery record files. The initialization time is demonstrated to be random since it depends on the final gantry angle of the previous treatment. Based on modeling the relationships between the gantry rotation angles and the corresponding rotation time, linear regression is applied to predict the gantry rotation time. The MLC motion time is calculated using the leaves delay modeling method and the leaf motion speed. A quantitative analysis was performed to understand the correlation between the total treatment time and the plan complexity. The proposed algorithm is able to predict the ViewRay treatment delivery time with the average prediction error 0.22 min or 1.82%, and the maximal prediction error 0.89 min or 7.88%. The analysis has shown the correlation between the plan modulation (PM) factor and the total treatment delivery time, as well as the treatment delivery duty cycle. A possibility has been identified to significantly reduce MLC motion time by optimizing the positions of closed MLC pairs. The accuracy of the proposed prediction algorithm is sufficient to support patient treatment appointment scheduling. This developed software tool is currently applied in use on a daily basis in our clinic, and could also be used as an important indicator for treatment plan complexity. PACS number(s): 87.55.N

SU-FF-J-126: An Open-Source Radiotherapy Image Registration Toolkit Integrated with CERR

Purpose: We developed an image registration module for the open‐source system CERR (the computational environment for radiotherapy research) for the purpose of radiation therapyimage guidance quality assurance and image analysis. This new module provides functionality useful for registration algorithm optimization, visualization, analysis, and validation. Method and Materials: We implemented a series of automatic (rigid and deformable) and semi‐automatic image registration methods. We implemented 20+ original and variant deformable methods, including optical flow based methods with different constraints, level set motion based methods and demon based methods. We imported ITK rigid image registration methods into CERR, including affine transform, similarity transform, Euler3D transform, versor3D transform, etc. We also implemented a multi‐grid framework to improve the speed and accuracy of the automatic registration process. In addition, we implemented 3D control point matching methods. We use several similarity metrics, including MSE, cross correlation, MI etc., to quantitatively analyze and validate the registration results. For results visualization, we implemented functions such as difference, dynamic checkerboard, image mirror, deformation field vector and grid plotting, etc. We programmed the GUI in MATLAB and Java, deformable methods in MATLAB and ITK rigid registration methods in C++. Results: The new CERR module supports 3D rigid and deformable registration. By monitoring the registration process and measuring the results, we can optimize the registration methods by tuning the parameters. We tested the deformable methods with chest CT images and the results were satisfied. Conclusion: The goal of this work is to extend the functions of CERR, to provide an open source implementation of image registration algorithms for radiotherapy research. As an ongoing project, we plan to provide more registration methods and further improve the integration of the registration results with treatment planning data.

SU‐E‐T‐218: Comprehensive Plan Integrity and Quality Check by Accessing Eclipse Planning Data Remotely Via a Novel Eclipse‐API Client‐Server Interface

Purpose: It is desirable to seamlessly and remotely access treatment plan data in Eclipse in order to allow sequential treatment planning and quality assurance automation, e.g. automatic plan quality check and comprehensive plan integrity check. The manual DICOM export function and the native Eclipse SQL interface cannot fully support the needs. Therefore we have developed a procedure and computer programs to enable access of Eclipse planning data remotely, and directly from Eclipse server, via Eclipse API, so to support such clinical software applications requiring advanced mathematics and extensive computation. Methods: A novel client-server interface was developed to allow external software programs to remotely and seamlessly access the Eclipse planning data using Eclipse-API from outside the Eclipse workstations. Remote (client) programs send requests as network socket messages to the server program running on an Eclipse workstation, and the server program uses Eclipse-API functions to access planning data, and sends data back to the client programs. Results: The Eclipse-API client-server interface were implemented and successfully tested in clinical settings. Comprehensive plan integrity and quality check programs, which were previously supporting Pinnacle TPS only, have been extended to support Eclipse and are executable on any network computer in the department independent of Eclipse installation. Advanced plan check features, e.g. IMRT optimization structure overlapping check, on-the-fly derivation of new structures in planning quality check, that were previously not possible in C# programs via Eclipse-API, have now been implemented, allowed by the inclusion of advanced mathematics packages e.g. MATLAB in the external programs. Conclusion: The new Eclipse-API client-server interface has enabled Eclipse data to be accessed remotely and seamlessly. It has the potential to significantly improve the comprehensiveness of third party software programs based on Eclipse-API for both clinical and research purposes, and would be useful to extend the Eclipse / ARIA ecosystem. This work was partially funded by Varian Medical System

SU-E-T-629: Prediction of the ViewRay Radiotherapy Treatment Time for Clinical Logistics

Purpose: An algorithm is developed in our clinic, given a new treatment plan, to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance-image guided radiation therapy (MR-IGRT) delivery system. This algorithm is necessary for managing patient treatment appointments, and is useful as an indicator to assess the treatment plan complexity. Methods: A patient’s total treatment delivery time, not including time required for localization, may be described as the sum of four components: (1) the treatment initialization time; (2) the total beam-on time; (3) the gantry rotation time; and (4) the multileaf collimator (MLC) motion time. Each of the four components is predicted separately. The total beam-on time can be calculated using both the planned beam-on time and the decay-corrected delivery dose rate. To predict the remaining components, we quantitatively analyze the patient treatment delivery record files. The initialization time is demonstrated to be random since it depends on the final gantry angle and MLC leaf positions of the previous treatment. Based on modeling the relationships between the gantry rotation angles and the corresponding rotation time, and between the furthest MLC leaf moving distance and the corresponding MLC motion time, the total delivery time is predicted using linear regression. Results: The proposed algorithm has demonstrated the feasibility of predicting the ViewRay treatment delivery time for any treatment plan of any patient. The average prediction error is 0.89 minutes or 5.34%, and the maximal prediction error is 2.09 minutes or 13.87%. Conclusion: We have developed a treatment delivery time prediction algorithm based on the analysis of previous patients’ treatment delivery records. The accuracy of our prediction is sufficient for guiding and arranging patient treatment appointments on a daily basis. The predicted delivery time could also be used as an indicator to assess the treatment plan complexity. This work was supported by a research grant from Viewray Inc.