Replanning Process Research Articles

Path planning and obstacle avoidance control of UUV based on an enhanced A* algorithm and MPC in dynamic environment

Addressing the challenges of suboptimal path planning and insufficient dynamic obstacle avoidance for Unmanned Underwater Vehicles (UUVs), this paper presents a composite strategy that merges an enhanced A* path planning algorithm with Model Predictive Control (MPC). This dual-faceted approach synthesizes path planning and trajectory tracking control. Firstly, the six-degree-of-freedom kinematic and dynamic model of the UUV is established based on the modeling method of underwater vehicles. Secondly, an enhanced A* algorithm is implemented to generate an optimal reference path for the UUV within a three-dimensional environment. Subsequently, MPC is employed for trajectory tracking control. When encountering unforeseen dynamic obstacles on the reference path, the system initiates a real-time dynamic re-planning process, modifying the trajectory to circumvent obstacles while optimizing the objective function to guarantee the UUV's safe passage and accurate arrival at the intended destination. The simulation results prove the efficacy of this integrated method, demonstrating notable enhancements in the UUV's capacity for dynamic obstacle avoidance and the execution of real-time path planning.

Development of programs to predict the occurrence of mucositis from digital imaging and communications in medicine data by machine learning in head and neck volumetric modulated radiotherapy.

Volumetric modulated arc therapy (VMAT) with cisplatin for head and neck cancer is often accompanied by symptoms of pharyngeal and oral mucositis. However, no standard medical program exists for the prevention and treatment of mucositis, and the mechanisms of mucositis have not yet been fully proven. Therefore, adaptive radiotherapy (ART), which is a re-planning process, is administered when severe mucositis develops during the treatment period. We extracted the treatment plans of patients who developed severe mucositis from DICOM data and used machine learning to determine its quantitative features. This study aimed to develop a machine learning program that can predict the development of mucositis requiring ART. This study included 61 patients who received concurrent chemotherapy and radiotherapy (RT). For each patient, the equivalent square field size of each segmental irradiation field used for VMAT, dose per segment (Gy), clinical target volume high, and mean dose of the oral cavity (Gy) were calculated. Furthermore, 671 five-dimensional lists were generated from the acquired data. Support vector machine (SVM) and K-nearest neighbor (KNN) were used for machine learning. For the accuracy score, the test size was varied from 10% to 90%, and the random number of data extracted in each test size was further varied from 1 to 100 to calculate a mean accuracy score. The mean accuracy scores of SVM and KNN were 0.981±0.020 and 0.972±0.033, respectively. The presence or absence of ART for mucositis was classified with high accuracy. The classification of the five-dimensional list was implemented with high accuracy, and a program was constructed to predict the onset of mucositis requiring ART before treatment began. This study suggests that it may support preventive measures against mucositis and the completion of RT without having to re-plan.

A Study on Dynamic Motion Planning for Autonomous Vehicles Based on Nonlinear Vehicle Model

Autonomous driving technology, especially motion planning and the trajectory tracking method, is the foundation of an intelligent interconnected vehicle, which needs to be improved urgently. Currently, research on path planning methods has improved, but few of the current studies consider the vehicle's nonlinear characteristics in the reference model, due to the heavy computational effort. At present, most of the algorithms are designed by a linear vehicle model in order to achieve the real-time performance at the cost of lost accuracy. To achieve a better performance, the dynamics and kinematics characteristics of the vehicle must be simulated, and real-time computing ensured at the same time. In this article, a Takagi-Sugeno fuzzy-model-based closed-loop rapidly exploring random tree algorithm with on-line re-planning process is applied to build the motion planner, which effectively improves the vehicle performance of dynamic obstacle avoidance, and plans the local obstacle avoidance path in line with the dynamic characteristics of the vehicle. A nonlinear vehicle model is integrated into the motion planner design directly. For fast local path planning mission, the Takagi-Sugeno fuzzy modelling method is applied to the modeling process in the planner design, so that the vehicle state can be directly utilized into the path planner to create a feasible path in real-time. The performance of the planner was evaluated by numerical simulation. The results demonstrate that the proposed motion planner can effectively generate a reference trajectory that guarantees driving efficiency with a lower re-planning rate.

Autonomous Landing of a Quadrotor on a Moving Platform via Model Predictive Control

Landing on a moving platform is an essential requirement to achieve high-performance autonomous flight with various vehicles, including quadrotors. We propose an efficient and reliable autonomous landing system, based on model predictive control, which can accurately land in the presence of external disturbances. To detect and track the landing marker, a fast two-stage algorithm is introduced in the gimbaled camera, while a model predictive controller with variable sampling time is used to predict and calculate the entire landing trajectory based on the estimated platform information. As the quadrotor approaches the target platform, the sampling time is gradually shortened to feed a re-planning process that perfects the landing trajectory continuously and rapidly, improving the overall accuracy and computing efficiency. At the same time, a cascade incremental nonlinear dynamic inversion control method is adopted to track the planned trajectory and improve robustness against external disturbances. We carried out both simulations and outdoor flight experiments to demonstrate the effectiveness of the proposed landing system. The results show that the quadrotor can land rapidly and accurately even under external disturbance and that the terminal position, speed and attitude satisfy the requirements of a smooth landing mission.

Advanced Real-Time Hierarchical Task Network: Long-Term Behavior in Real-Time Games

HTN (Hierarchical Task Network) is a widespread technique in the game industry to achieve long-term behavior and smart intelligence in NPCs. However, in the more real-time games of recent years, a situation has emerged where the system is not functioning effectively. We understand the re-planning process is the fundamental problem of HTN in real-time games and propose a new method to solve this problem.

A column generation approach for the integrated crew re-planning problem

Planned maintenance and construction activities are crucial for heavily used railway networks to cope with the ever increasing demand. These activities lead to changes in the timetable and rolling stock schedule (often for multiple days) and can have a major impact on the crew schedule, as the changes can render many planned duties infeasible. In this paper, we propose a novel integrated approach to crew re-planning, i.e., the construction of new duties and rosters for the employees given changes in the timetable and rolling stock schedule. In current practice, the feasibility of the new rosters is ‘assured’ by allowing the new duties to deviate only slightly from the original ones. This allows the problem to be solved on a day-by-day basis. In the Integrated Crew Re-Planning Problem (ICRPP), we loosen this requirement and allow for more flexibility: The ICRPP considers the re-scheduling of crew for multiple days simultaneously, thereby explicitly taking the feasibility of the rosters into account. By integrating the scheduling and rostering decisions, we can allow for larger deviations from the original duties. We propose a mathematical formulation for the ICRPP, strengthen it using a family of valid cover inequalities, and develop a column generation approach to solve the problem. We apply our solution approach to practical instances from Netherlands Railways, and show the benefits of integrating the re-planning process.

Validation of in-house knowledge-based planning model for predicting change in target coverage during VMAT radiotherapy to in-operable advanced-stage NSCLC patients

Objectives. anatomical changes are inevitable during the course of radiotherapy treatments and, if significant, can severely alter expected dose distributions and affect treatment outcome. Adaptive radiotherapy (ART) is employed to maintain the planned distribution and minimise detriment to predicted treatment outcome. Typically, patients who may benefit from adaptive planning are identified via a re-planning process, i.e., re-simulation, re-contouring, re-planning and treatment plan quality assurance (QA). This time-intensive process significantly increases workload, can introduce delays and increases unnecessary stress to those patients who will not actually gain benefit. We consider it crucial to develop efficient models to predict changes to target coverage and trigger ART, without the need for re-planning. Methods. knowledge-based planning (KBP) models were developed using data for 20 patients’ (400 fractions) to predict changes in PTV V95 coverage Initially, this change in coverage was calculated on the synthetic computerised tomography (sCT) images produced using the Velocity adaptive radiotherapy software. Models were developed using patient (cell death bio-marker) and treatment fraction (PTV characteristic) specific parameters to predict and verified using five patients (100 fractions) data. Results. three models were developed using combinations of patient and fraction specific terms. The prediction accuracy of the model developed using biomarker (PD-L1 expression) and the difference in ‘planning’ and ‘fraction’ PTV centre of the mass (characterised by mean square difference, MSD) had the higher prediction accuracy, predicting the within ± 1.0% for 77% of the total fractions; with 59% for the model developed using, PTV size, PD-L1 and MSD and 48% PTV size and MSD respectively. Conclusion. the KBP models can predict very effectively and efficiently for advanced-stage NSCLC patients treated using volumetric modulated arc therapy and to identify patients who may benefit from adaption for a specific fraction.

Studio oriented learning environment method to improve student learning quality in interior design studio

Due to the complexity of activities and interior elements in the studio-oriented learning environment, it is essential to apply the right method in all stages to ensure they proceed sequentially and systematically. This is because presently the in-depth design method associated with this learning environment has not been fully explored to fulfill user needs in accordance with the design project. Therefore, this research aims to determine the right method used to improve students learning quality in the teaching process at the Interior Design studio, to obtain better responses. This research was carried out using the CAR model proposed by Kemmis and Mc Taggart consisting of four components, namely planning, action, observation, and reflection. The result showed that after the re-planning process, a reflection in the form of a separate cycle is conducted. Furthermore, independent learning methods in a conducive environment are carried out based on the Studio Oriented Learning Environment (SOLE) method, with assessments conducted by lecturers and students in each assistance group. This research has the ability to improve students thinking ability sequentially and systematically, as well as increase their participation and collaboration in producing quality designs.

Parotid sparing adaptive radiotherapy in head and neck cancer patients: A study evaluating resource intensiveness and impact on quality of life.

e24037 Background: Anatomical changes during the course of parotid sparing IMRT for head and neck cancer can lead to an increase in the actual dose absorbed by the parotid glands, which may be controlled with the use of interval adaptive replanning. In this prospective assessment of adaptive replanning for parotid dose changes, we explored the feasibility of carrying out adaptive replanning based on predefined objective dosimetric criteria at specified time points. We sought to assess the impact of this measure using a clinically meaningful endpoint of patient reported quality of life outcomes. Methods: 90 patients with head and neck radiotherapy indicated for definitive management or adjuvant therapy who had at least one parotid gland receiving a mean dose (MD) of between 25-30Gy were accrued in the study. The index parotid was delineated on the images acquired on 14th and 19th day and the MD was determined by overlaying the verification image on the planned CT. If the MD had increased by 2% of the initial intended dose, an adaptive plan was attempted with an aim to reduce MD by 2% without compromising PTV coverage; this plan was then used to deliver the remaining treatment. Patients were invited to complete QoL questionnaires: EORTC-QLQC30 with HN35 module, and XeQoL score at baseline, at 3 and 9 months after completion of treatment. Results: 46 out of 90 patients met the threshold for adaptive replanning and were switched to the new plan during treatment. Adaptive replanning was triggered at D14 for 31 patients and D19 for the remaining 15. Need for adaptive replanning was associated with receipt of concurrent chemotherapy and weight loss in the first two weeks of RT. QoL was evaluable for 50 patients at 3 months post treatment. In patients who required adaptive replanning per protocol, Mean XeQoL scores at 3 months showed significantly worse scores for overall scores (1.1 vs 2.3, p 0.001) and for the component individual physical, pain, psychological, and social domains. EORTC QLQ-C30 and HN35 questionnaires at 3 months also demonstrated significantly worse mean symptom scores of the relevant domains of mouth dryness (39 vs 60), stickiness (32 vs 54) and swallowing (39 vs 60) in patients who required adaptive replanning versus those who did not. No significant QoL trends were observable in the 31 patients who were evaluable at 9 months post treatment. The average time required for each step in the planning process was comparable for both the initial planning workflow and adaptive replanning process. Conclusions: The trigger criteria for replanning identified a population of patients who have significantly worsened quality of life due to radiation induced xerostomia. The benefits of adaptive replanning strategies based on weekly evaluation, binary thresholds and standard planning procedures is doubtful Clinical trial information: CTRI/2017/11/010683.

Dynamic Path Planning Of Unmanned Surface Vehicle Based On Genetic Algorithm With Sliding Curve Guidance System

Unmanned Surface Vehicle (USV) is an unmanned ship that is controlled through a remote control system (manual) or automatic control system (autopilot), move due to the thrust force from thruster machine and can turning due to the deflection angle of rudder. The USV path planning system becomes an important task so that the ship can make the global trajectory with the minimum travel distance according to the desired navigation while at the same able to avoid various obstacles from local dangerous situations that have the potential for collisions. To be able to do dynamic USV path planning, the Genetic Algorithm method with a sliding curve guidance system and PID MRAC controller is used. The use of this method gives smooth ship track performance with shortest distance in a 400x400 square meter map with static and dynamic obstacles. In a dynamic environment, the path replanning process that takes place in 0,98 seconds is able to find a new path that does not collide the obstacles. For the purposes of algorithm validation, the simulation is performed using MATLAB software with real ship parameters of 6 meters length USV.

Dosimetric Quality of Online Adapted Pancreatic Cancer Treatment Plans on an MRI-Guided Radiation Therapy System

PurposeStereotactic magnetic resonance image–guided adaptive radiation therapy (SMART) is an emerging technique that shows promise in the treatment of pancreatic cancer and other abdominopelvic malignancies. However, it is unknown whether the time-limited nature of on-table adaptive planning may result in dosimetrically suboptimal plans. The purpose of this study was to quantitatively address that question through systemic retrospective replanning of treated on-table adaptive pancreatic cancer cases. Methods and MaterialsOf 74 consecutive adapted fractions, 30 were retrospectively replanned based on deficiencies in planning target volume (PTV) and gross tumor volume (GTV) coverage or doses to organs-at-risk (OARs) that exceeded ideal constraints. Retrospective plans were created by adjusting dose-volume objectives in an iterative fashion until deemed optimized. The goal of replanning was to improve PTV/GTV coverage while keeping the dose to gastrointestinal OARs the same or lower or to reduce OAR doses while keeping PTV coverage the same or higher. The global maximum dose was required to be maintained within 2% of that of the treated adaptive plan to eliminate it as a confounding factor. A threshold of 5% improvement in PTV coverage or 5% decrease in OAR dose was used to define a clinically significant improvement. ResultsOf the 30 replans, 7 obtained at least 5% PTV coverage improvement. The average increase in PTV coverage for these plans was 11%. No plans were clinically significantly improved in terms of OAR sparing. Changes in beam-on time did not show any correlation. Statistical analysis via a linear mixed-effects model with a nested random effect suggested that both GTV and PTV coverage were improved over SMART process plans by 0.91 cc (P = .02) and 2.03 cc (P < .001), respectively. ConclusionsDosimetric plan quality of at least 10% of SMART fractions may be improved through more extensive replanning than is currently performed on-table. Further work is needed to develop an automated replanning workflow to streamline the in-depth replanning process to better fit into an on-table adaptive workflow.

Dynamic motion planner with trajectory optimisation for automated highway lane‐changing driving

This study proposes a dynamic motion planner with trajectory optimisation for automated highway lane-changing driving. Owing to the connected and automated vehicles (CAVs) technology that the real-time traffic information can be obtained, alternative trajectories can be generated to satisfy the vehicle kinematic constraints and avoid many types of potential collisions. An optimal control theory is adopted to select an optimal lane-changing path from the finite path set, and the appropriate acceleration and speed for the execution path are also determined. In order to avoid unnecessary motion re-planning process, this study puts forward a collision-avoidance monitoring algorithm to reduce the time consumption costs of the motion planner. Moreover, an online planning framework based on ‘decision-execution’ is explored. Applying this timeline framework can not only help to evaluate the dynamic planner's online performance, but also reduce the deviation between the online calculation and the actual execution caused by the time consumption. The simulations are performed in PreScan-Simulink platform and the experimental results show that the presented dynamic planner can complete the lane-changing manoeuvre safely and effectively in a high-speed environment.

An Evaluation of Urban Sprawl Process Post-earthquake: Example of Kocaeli/ Gölcük

İnsan yaşamının büyük bir kısmının geçtiği konutlar, barınma ihtiyacının ortaya çıkardığı mekânsal alanlardır. Kentsel doku üzerinde yoğun olarak bulunan konut alanları, kentlerin mücadele ettiği doğal afet, çarpık kentleşme, hızlı nüfus artışı, sosyo-ekonomik eşitsizlik gibi fiziksel, sosyal ve ekonomik sorunlarla karşılaşmaktadır. 1999 Marmara Depremi, merkez üssü Gölcük başta olmak üzere birçok kentte büyük hasarlara neden olmuştur. Gölcük’ün kent gelişimi sürecine bakıldığında; deprem öncesinde tersane ve diğer sanayi tesisleri nedeni ile kıyıda yerleşim daha yoğun iken, deprem sonrasında ise yeniden planlama sürecinde kıyı alanı zemin yapısının sağlam olmadığı tespit edilmiştir. Yeniden planlama sürecinde yeni konut alanları zemini sağlam doğal kaynakların bulunduğu güney kent çeperine kaydırılmıştır. Bu durum kentin güney çeperlerindeki tarım alanları ve ormanlık arazilerin tahribatına, tarihsel süreç içerisinde bu dağlık alanlarda kurulan tarihi ve kırsal dokunun zedelenmesine neden olmuştur. Kent çeperinde yaşanan bu yapılaşma sorunları çalışmanın önemli problemlerini oluşturmaktadır. Bu kapsamda Gölcük’ün mevcut kentleşme süreçleri ile konut planlama sisteminin uyumu, sürdürülebilirlik ve dirençlilik perspektifinden sorgulanacaktır. Alan çalışması için yerleşim ölçeğinde seçilen dokuların fiziksel, sosyal ve ekonomik sorunları; kentsel dirençlilik üzerinden bütüncül bir yaklaşımla ele alınmıştır. Kentsel dokuda ortaya çıkan sorunlara karşı dirençli ve sürdürülebilir kent planlaması, nitelikli konut ve yaşanabilir çevre üretimindeki farkındalığın sektördeki her alana yansıtılması için yol göstermesi hedeflenmektedir.

The Role of Dorsal Premotor Cortex in Resolving Abstract Motor Rules: Converging Evidence From Transcranial Magnetic Stimulation and Cognitive Modeling.

In this study, repetitive transcranial magnetic stimulation (rTMS) was applied over left dorsal premotor cortex (PMd) while participants performed a novel task paradigm that required planning of responses in accordance with both instructed rules and present stimuli. rTMS is a noninvasive form of neurostimulation that can interfere with ongoing processing of a targeted cortical region, resulting in a transient "virtual lesion" that can reveal the contribution of the region to ongoing behavior. Increased response times (RTs) were observed specifically when rTMS was applied over PMd while participants were preparing to execute a complex response to an uninstructed stimulus. To further delineate the effect of stimulation, condition-specific RT distributions were modeled as three-parameter Weibull distributions through hierarchical Bayesian modeling (HBM). Comparison of the estimated parameters to those of a paired control demonstrated that while PMd-rTMS slightly decreased nondecision time, it also greatly increased the variability in the RT distribution. This increased variability resulted in an overall increase in predicted mean RT and is consistent with a delay in cognitive processes. In conjunction, an ACT-R cognitive model of the task was developed in order to systematically test alternative hypotheses on the potential cognitive functions that may be affected by stimulation of PMd. ACT-R simulations suggested that participant's behavior was due to an effect of TMS on a "re-planning" process, indicating that PMd may be specifically involved in planning of complex motor responses to specific visual stimuli. In conjunction with the HBM modeling effort, these results suggest that PMd-rTMS is capable of pausing or slowing the execution of a motor response-planning process.

Selection of head and neck cancer patients for adaptive replanning of radiation treatment using kV-CBCT

Purpose; To develop a CBCT-based method for selection of head and neck cancer (HNC) patients that may require adaptive replanning. Materials and Methods; We retrospectively analyzed 11 HNC patients with different degree of weight loss during the 7 weeks of IMRT treatment. Weight loss related dosimetric parameters were determined using CBCT and verified against new CT scans acquired during adaptive replanning process. The planning CT body surface in the head and neck region was modified using the CBCT body surface. The target volumes (PTV) and organs at risks (OAR) were modified where necessary following the body shrinkage. Anatomical and dosimetric changes were studied using the resulting modified CT. Dose calculation was performed using the parameters of the original treatment plan with the modified CT structure set using Eclipse treatment planning system. Action levels for adaptive replanning were developed evaluating dosimetric changes in all structures. Results; The largest volume loss occurred in the lateral neck, the cheeks and chin regions. The correlation % dose versus % volume changes is quasi-linear and patient/structure-specific. A 10 mm loss in the neck average radius corresponded to an increment in the dose to the PTVs/OARs of up to 4%. Deep PTV’s maintain good coverage (V100 > 95%) during the treatment but PTV coverage is reduced in shallow regions. Conclusion; The estimated dosimetric changes obtained for CBCT and replanning CT are consistent. The anatomical and dosimetric changes in HNC patients derived from CBCT can be used to guide decision-making as to when during RT treatment replanning is necessary or optional.

Post-traumatic urbanism: Repressing Manshiya and Wadi Salib

Trauma is defined as a wound or an injury caused by an act of violence on one's body, or as a severe anxiety caused by an unpleasant experience. The victims of traumatic events may develop psychological stress disorder, which is manifested in several symptoms: post-traumatic stress disorder. The 1948 Arab-Israeli had caused both physical and psychological trauma. The symptoms of this trauma are still visible today in various Israeli cities. As a result of the war, Israeli cities had annexed formerly owned Palestinian villages and neighborhoods. Along the years, these vacated Palestinian houses were settled by Jewish immigrants, turned to slums and became the target of several urban renewal projects. These renewal projects mainly asked to erase all traces of the neighborhood's Arab past, and to introduce a new urban order. This research focuses on Al Manshiya in Tel Aviv-Jaffa and Wadi Salib in Haifa, two former Palestinian neighborhoods, which were vacated from their original inhabitants. This research surveys the re-planning process of both neighborhoods, its implementation and its current status. Asking whether one can depict symptoms of post-trauma in the urban scheme and in the buildings' architecture. Al Manshiya was torn down completely in the 1970's, in order to make place for Tel Aviv's new central business district. This project was never fully completed, as the symptoms of the post-trauma are manifested in the disconnected grand office buildings, the urban void and the parking lots surrounding them. The majority of Wadi Salib was torn down as well, as several decaying buildings are still standing in the cleared and empty neighborhood. The emptiness, neglect and oblivion emphasize the post-traumatic experience. In the recent years however, several projects asked to deal with the neighborhoods' past and heritage. Even then, the references remained superficial leaving the trauma unattended and not curing the neighborhoods' from their post-trauma symptoms.

Planning Dynamically Feasible Trajectories for Quadrotors Using Safe Flight Corridors in 3-D Complex Environments

There is extensive literature on using convex optimization to derive piece-wise polynomial trajectories for controlling differential flat systems with applications to three-dimensional flight for Micro Aerial Vehicles. In this work, we propose a method to formulate trajectory generation as a quadratic program (QP) using the concept of a Safe Flight Corridor (SFC). The SFC is a collection of convex overlapping polyhedra that models free space and provides a connected path from the robot to the goal position. We derive an efficient convex decomposition method that builds the SFC from a piece-wise linear skeleton obtained using a fast graph search technique. The SFC provides a set of linear inequality constraints in the QP allowing real-time motion planning. Because the range and field of view of the robot's sensors are limited, we develop a framework of Receding Horizon Planning , which plans trajectories within a finite footprint in the local map, continuously updating the trajectory through a re-planning process. The re-planning process takes between 50 to 300 ms for a large and cluttered map. We show the feasibility of our approach, its completeness and performance, with applications to high-speed flight in both simulated and physical experiments using quadrotors.

Robust Planning of Nonlinear Rendezvous with Uncertainty

Practical uncertainties are not considered by optimal rendezvous trajectory designs proposed in many current studies. In this study, a robust optimization method for on-ground rendezvous trajectory design is proposed by considering the uncertainties and orbital replanning process. Two robust performance indices related to the final rendezvous errors and the total velocity increment are first defined, then a multi-objective optimization model (including the minimum and minimum rendezvous errors) is formulated. The unscented transformation method is used to efficiently compute the robust indices in optimization process, and a multi-objective, nondominated sorting genetic algorithm is employed to obtain a Pareto-optimal solution set. It is shown that the proposed approach can be used to design a rendezvous trajectory with the and final rendezvous errors that are both robust against uncertainties. Furthermore, the proposed approach can identify the most preferable design space in which specific solutions for practical application of rendezvous control are selected.

TU‐H‐CAMPUS‐JeP3‐03: Clinical Implementation of An Online Replanning Process

Purpose:The purpose of this study is to report the clinical implementation of an online replanning process with CT‐on‐Rails to address daily interfractional variations.Methods:A treatment planning package (MONACO/ADMIRE/MOSAIQ, Elekta) is used to perform online adaptive replanning, using segment aperture morphing (SAM) algorithm implemented in MONACO, based on daily CTs acquired from a CT‐on‐Rails (ARTISTE, Siemens). The online replanning is implemented in prostate RT in the following workflow:(1) Generating a target contour based on the image of the day, using ADMIRE deformable image registration software with manual editing if necessary; (2) Automatically modifying segment apertures from the original plan using the SAM algorithm to account for the changes in the target from planning to daily CTs including deformation; (3) Calculating the dose distribution for the new apertures with the same MUs as in the original plan;(4) Transferring the new plan into MOSAIQ Record &amp; Verify system;(5) Performing a pre‐delivery QA using both our home‐grown and RADCALC software (LifeLine);(6) Delivering the adaptive plan for the fraction.The entire workflow takes &lt;10 min. using a 16‐CPU hardware.Results:The online adaptive replanning process was successfully applied during RT for prostate cancers. The adaptive plan significantly improved the plan quality in both target coverage and organs‐at‐risk (OAR) sparing when compared to the current standard of practice; IGRT repositioning plan. For the average of 10 prostate cases, PTV‐V100, Rectum‐V70, and Bladder‐V45 (%) coverages were (92.84 ± 1.61/97.61 ± 1.45/98.85 ± 1.13), (6.76 ± 2.37/5.40 ± 2.61/3.52 ± 0.94) and (8.20 ± 3.55/5.72 ± 2.61/6.68 ± 3.21) for the IGRT, online SAM, and reoptimization plans, respectively.Conclusion:The online adaptive replanning workflow is clinically practical for prostate RT and can quickly generate adaptive plans with superior plan qualities when compared to the repositioning plans, improving the management of interfractional variations.This study was partially supported by Elekta Inc.

Performance and Effect of Automatic Contouring in Automatic Adaptive Planning for Prostate Cancer Volumetric Modulated Radiotherapy

Volumetric modulated radiotherapy (VMAT) has been widely used in treating prostate cancer. However, when large daily inter-fractional anatomic changes occur, a simple iso-center shift may not be adequate for target coverage and normal tissue sparing. The fully adaptive radiotherapy involves re-planning process based on daily repeat computed tomography images (CTs), which is impractical with the manual re-contouring and trial-and-error re-planning process. In this paper, we quantitatively evaluated the performance of automatic contouring in an automatic adaptive planning method on cone beam CTs, and demonstrated the feasibility and effectiveness of using the auto contours for online automatic adaptive planning to compensate for the target dose deficit and the critical organ overdose caused by inter-fractional anatomical changes for prostate cancer. Keywords: Adaptive radiotherapy, automatic contouring, automatic re-planning, deformable image registration, inter-fractional motion, prostate cancer, volumetric modulated radiotherapy.