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Register a new userRapidBrachyMCTPS 2.0: A Comprehensive and Flexible Monte Carlo-Based Treatment Planning System for Brachytherapy Applications
تخطيط العلاج المبني على الحسابات الشبه الحقيقية السريعة 2.0: نظام كامل ومرن لتخطيط العلاج لتطبيقات العلاج المحيط
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Harry Glickman
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توصلنا سابقا إلى RapidBrachyMCTPS، وهي مجموعة أدوات تخطيط العلاج بالبراخيثرافيا تتضمن واجهة مستخدم رسومية (GUI) ومحرك حساب الجرعة المونت كارلو (MC) مبني على Geant4. يصف هذا العمل الأدوات التي تمت إضافتها حديثًا إلى RapidBrachyMCTPS، بحيث يصبح التطبيق الوحيد لتخطيط العلاج بالبراخيثرافيا المستند إلى MC. تضمن التغييرات الملحوظة استيرادًا ووضعًا محدثًا للمطبوعات، وأدوات التحيز في ثلاثة أبعاد، وخوارزميات تحسين الجرعة المحدثة أيضًا، بإضافة إلى تحسين موقع الإقامة ووقت الإقامة، كما يتم تحسين زوايا الدرع الدوراني في البراخيثرافيا المعدلة بالشدة. تم التحقق من الوحدات الرئيسية لـ RapidBrachyMCTPS بما في ذلك استيراد DICOM، استيراد ووضع المطبوعات، التحيز، تعيين المواد، تحديد المصدر، إعادة بناء الكاتيتر، توليد EGSphant، والتفاعل مع الرمز MC، وأدوات تحسين وتحليل الجرعة. تم تصميم حالتين مرضية للتوضيح من هذه المبادئ، مما يوضح التحكم والمرونة التي يقدمها RapidBrachyMCTPS لجميع خطوات طريق التخطيط العلاجي. يصبح RapidBrachyMCTPS الآن تطبيقًا مستقلًا لتخطيط العلاج بالبراخيثرافيا، ويوفر واجهة مستخدم سهلة الاستخدام للوصول إلى حسابات MC القوية. يمكن استخدامه للتحقق من توزيعات الجرعة من أنظمة تخطيط العلاج السريري أو الخوارزميات الحسابية المستندة إلى النموذج، ويناسب أيضًا اختبار تركيبات جديدة من المصادر الإشعاعية والمطبوعات، وخاصة التي محمية بمواد ذات ذروة عالية.
We have previously described RapidBrachyMCTPS, a brachytherapy treatment planning toolkit consisting of a graphical user interface (GUI) and a Geant4-based Monte Carlo (MC) dose calculation engine. This work describes the tools that have recently been added to RapidBrachyMCTPS, such that it now serves as the first stand-alone application for MC-based brachytherapy treatment planning. Notable changes include updated applicator import and positioning, three-plane contouring tools, and updated dose optimization algorithms that, in addition to optimizing dwell position and dwell time, also optimize the rotating shield angles in intensity modulated brachytherapy. The main modules of RapidBrachyMCTPS were validated including DICOM import, applicator import and positioning, contouring, material assignment, source specification, catheter reconstruction, EGSphant generation, interface with the MC code, and dose optimization and analysis tools. Two patient cases were simulated to demonstrate these principles, illustrating the control and flexibility offered by RapidBrachyMCTPS for all steps of the treatment planning pathway. RapidBrachyMCTPS is now a stand-alone application for brachytherapy treatment planning, and offers a user-friendly interface to access powerful MC calculations. It can be used to validate dose distributions from clinical treatment planning systems or model-based dose calculation algorithms, and is also well suited to testing novel combinations of radiation sources and applicators, especially those shielded with high-Z materials.
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High dose-rate brachytherapy (HDRBT) is widely used for gynecological cancer treatment. Although commercial treatment planning systems (TPSs) have inverse optimization modules, it takes several iterations to adjust planning objectives to achieve a satisfactory plan. Interactive plan-modification modules enable modifying the plan and visualizing results in real time, but they update plans based on simple geometrical or heuristic algorithms, which cannot ensure resulting plan optimality. This project develops an interactive plan optimization module for HDRBT of gynecological cancer. By efficiently solving an optimization problem in real time, it allows a user to visualize a plan and interactively modify it to improve quality. We formulated an optimization problem with an objective function containing a weighted sum of doses to normal organs subject to user-specified target coverage. A user interface was developed that allows a user to adjust organ weights using scroll bars. With a simple mouse click, the optimization problem is solved in seconds with a highly efficient alternating-direction method of multipliers and a warm start optimization strategy. Resulting clinically relevant D2cc of organs are displayed immediately. This allows a user to intuitively adjust plans with satisfactory quality. We tested the effectiveness of our development in cervix cancer cases treated with a tandem-and-ovoid applicator. It took a maximum of 3 seconds to solve the optimization problem in each instance. With interactive optimization capability, a satisfactory plan can be obtained in <1 min. In our clinic, although the time for plan adjustment was typically <10min with simple interactive plan modification tools in TPS, the resulting plans do not ensure optimality. Our plans achieved on average 5% lower D2cc than clinical plans, while maintaining target coverage.
Purpose: For Monte Carlo simulation of radiotherapy, x-ray CT number of every system needs to be calibrated and converted to mass density and elemental composition. This study aims to formulate material properties of body tissues for practical two-step conversion from CT number. Methods: We used the latest compilation on body tissues that constitute reference adult male and female. We formulated the relations among mass, electron, and elemental densities into polylines to connect representative tissues, for which we took mass-weighted mean for the tissues in limited density regions. We compared the polyline functions of mass density with a bi-line for electron density and broken lines for elemental densities, which were derived from preceding studies. Results: There was generally high correlation between mass density and the other densities except of C, N, and O for light spongiosa tissues occupying 1% of body mass. The polylines fitted to the dominant tissues and were generally consistent with the bi-line and the broken lines. Conclusions: We have formulated the invariant relations between mass and electron densities and from mass to elemental densities for body tissues. The formulation enables Monte Carlo simulation in treatment planning practice without additional burden with CT-number calibration.
Inverse treatment planning in radiation therapy is formulated as optimization problems. The objective function and constraints consist of multiple terms designed for different clinical and practical considerations. Weighting factors of these terms are needed to define the optimization problem. While a treatment planning system can solve the optimization problem with given weights, adjusting the weights for high plan quality is performed by human. The weight tuning task is labor intensive, time consuming, and it critically affects the final plan quality. An automatic weight-tuning approach is strongly desired. The weight tuning procedure is essentially a decision making problem. Motivated by the tremendous success in deep learning for decision making with human-level intelligence, we propose a novel framework to tune the weights in a human-like manner. Using treatment planning in high-dose-rate brachytherapy as an example, we develop a weight tuning policy network (WTPN) that observes dose volume histograms of a plan and outputs an action to adjust organ weights, similar to the behaviors of a human planner. We train the WTPN via end-to-end deep reinforcement learning. Experience replay is performed with the epsilon greedy algorithm. Then we apply the trained WTPN to guide treatment planning of testing patient cases. The trained WTPN successfully learns the treatment planning goals to guide the weight tuning process. On average, the quality score of plans generated under the WTPNs guidance is improved by ~8.5% compared to the initial plan with arbitrary weights, and by 10.7% compared to the plans generated by human planners. To our knowledge, this is the first tool to adjust weights for the treatment planning in a human-like fashion based on learnt intelligence. The study demonstrates potential feasibility to develop intelligent treatment planning system via deep reinforcement learning.
Purpose: A Monte Carlo (MC) beam model and its implementation in a clinical treatment planning system (TPS, Varian Eclipse) are presented for a modified ultra-high dose-rate electron FLASH radiotherapy (eFLASH-RT) LINAC. Methods: The gantry head without scattering foils or targets, representative of the LINAC modifications, was modelled in Geant4. The energy spectrum ({sigma}E) and beam source emittance cone angle ({theta}cone) were varied to match the calculated and Gafchromic film measured central-axis percent depth dose (PDD) and lateral profiles. Its Eclipse configuration was validated with measured profiles of the open field and nominal fields for clinical applicators. eFLASH-RT plans were MC forward calculated in Geant4 for a mouse brain treatment and compared to a conventional (Conv-RT) plan in Eclipse for a human patient with metastatic renal cell carcinoma. Results: The beam model and its Eclipse configuration agreed best with measurements at {sigma}E=0.5 MeV and {theta}cone=3.9+/-0.2 degrees to clinically acceptable accuracy (the absolute average error was within 1.5% for in-water lateral, 3% for in-air lateral, and 2% for PDD). The forward dose calculation showed dose was delivered to the entire mouse brain with adequate conformality. The human patient case demonstrated the planning capability with routine accessories in relatively complex geometry to achieve an acceptable plan (90% of the tumor volume receiving 95% and 90% of the prescribed dose for eFLASH and Conv-RT, respectively). Conclusion: To the best of our knowledge, this is the first functional beam model commissioned in a clinical TPS for eFLASH-RT, enabling planning and evaluation with minimal deviation from Conv-RT workflow. It facilitates the clinical translation as eFLASH-RT and Conv-RT plan quality were comparable for a human patient. The methods can be expanded to model other eFLASH irradiators.
The International Electrotechnical Commission (IEC) has previously defined standard rotation operators for positive gantry, collimator and couch rotations for the radiotherapy DICOM coordinate system that is commonly used by treatment planning systems. Coordinate transformations to the coordinate systems of commonly used Monte Carlo (MC) codes (BEAMnrc/DOSXYZnrc and VMC++) have been derived and published in the literature. However, these coordinate transformations disregard patient orientation during the computed tomography (CT) scan, and assume the most commonly used head first, supine orientation. While less common, other patient orientations are used in clinics - Monte Carlo verification of such treatments can be problematic due to the lack of appropriate coordinate transformations. In this work, a solution has been obtained by correcting the CT-derived phantom orientation and deriving generalized coordinate transformations for field angles in the DOSXYZnrc and VMC++ codes. The rotation operator that includes any possible patient treatment orientation was determined using the DICOM Image Orientation tag (0020,0037). The derived transformations of the patient image and beam direction angles were verified by comparison of MC dose distributions with the Eclipse treatment planning system, calculated for each of the eight possible patient orientations.
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