<p>The online adaptive radiation therapy for prostate cancer based on re-optimization has been shown to provide better daily target coverage through the treatment course, especially in treatment sessions with large anatomical deformation. However, the clinical implementation of such technique is still limited primarily due to two major challenges: the low efficiency of re-optimization and the lack of online quality assurance technique to verify delivery accuracy. This project aims at developing new techniques and understandings to address these two challenges. </p><p>The study was based on retrospective study on patient data following IRB-approved protocol, including both planning Computer Tomography (CT) and daily Cone-Beam Computer Tomography (CBCT) images. The project is divided in to three parts. The first two parts address primarily the efficiency challenge; and the third part of this project aims at validating the deliverability of the online re-optimized plans and developing an online delivery monitoring system. </p><p><bold>I. Overall implementation scheme.</bold> In this part, an evidence-based scheme, named Adaptive Image-Guided Radiation Therapy (AIGRT), was developed to integrate the re-optimization technique with the current IGRT technique. The AIGRT process first searches for a best plan for the daily target from a plan pool, which consists the original CT plan and all previous re-optimized plans. If successful, the selected plan is used for the daily treatment with translational shifts. Otherwise, the AIGRT invokes re-optimization process of the CT plan for the anatomy-of-the-day, which is added to the plan pool afterwards as a candidate plan for future fractions. The AIGRT scheme is evaluated by comparisons with daily re-optimization and online repositioning techniques based on daily target coverage, Organ-at-Risk (OAR) sparing and implementation efficiency. Simulated treatment courses for 18 patients with re-optimization alone, re-positioning alone and AIGRT shows that AIGRT offers reliable daily target coverage that is highly comparable to re-optimization everyday and significantly improves compared to re-positioning. AIGRT is also seen to provide improved organs-at-risk (OARs) sparing compared to re-positioning. Apart from dosimetric benefits, AIGRT in addition offers an efficient scheme to integrate re-optimization to current re-positioning-based IGRT workflow.</p><p><bold>II. Strategies for automatic re-optimization.</bold> This part aims at improving the efficiency of re-optimization through automation and strategic selections of optimization parameters. It investigates the strategies for performing fast (~2 min) automatic online re-optimization with a clinical treatment planning system; and explores the performance with different input parameters settings: the DVH objective settings, starting stage and iteration number (in the context of real time planning). Simulated treatments of 10 patients were re-optimized daily for the first week of treatment (5 fractions) using 12 different combinations of optimization strategies. Options for objective settings included guideline-based RTOG objectives, patient-specific objectives based on anatomy on the planning CT, and daily-CBCT anatomy-based objectives adapted from planning CT objectives. Options for starting stages involved starting re-optimization with and without the original plan's fluence map. Options for iteration numbers were 50 and 100. The adapted plans were then analysed by statistical modelling, and compared both in terms of dosimetry and delivery efficiency. The results show that all fast online re-optimized plans provide consistent coverage and conformity to the daily target. For OAR sparing however, different planning parameters led to different optimization results. The 3 input parameters, i.e. DVH objectives, starting stages and iteration numbers, contributed to the outcome of optimization nearly independently. Patient-specific objectives generally provided better OAR sparing compared to guideline-based objectives. The benefit in high-dose sparing from incorporating daily anatomy into objective settings was positively correlated with the relative change in OAR volumes from planning CT to daily CBCT. The use of the original plan fluence map as the starting stage reduced OAR dose at the mid-dose region, but increased 17% more monitor units. Only < 2cc differences in OAR V50% / V70Gy / V76Gy were observed between 100 and 50 iterations. Based on these results, it is feasible to perform automatic online re-optimization in ~2 min using a clinical treatment planning system. Selecting optimal sets of input parameters is the key to achieving high quality re-optimized plans, and should be based on the individual patient's daily anatomy, delivery efficiency and time allowed for plan adaptation. </p><p><bold>III. Delivery accuracy evaluation and monitoring.</bold> This part of the project aims at validating the deliverability of the online re-optimized plans and developing an online delivery monitoring system. This system is based on input from Dynamic Machine Information (DMI), which continuously reports actual multi-leaf collimator (MLC) positions and machine monitor units (MUs) at 50ms intervals. Based on these DMI inputs, the QA system performed three levels of monitoring/verification on the plan delivery process: (1) Following each input, actual and expected fluence maps delivered up to the current MLC position were dynamically updated using corresponding MLC positions in the DMI. The difference between actual and expected fluence maps creates a fluence error map (FEM), which is used to assess the delivery accuracy. (2) At each control point, actual MLC positions were verified against the treatment plan for potential errors in data transfer between the treatment planning system (TPS) and the MLC controller. (3) After treatment, delivered dose was reconstructed in the treatment planning system based on DMI data during delivery, and compared to planned dose. FEMs from 210 prostate IMRT beams were evaluated for error magnitude and patterns. In addition, systematic MLC errors of ±0.5 and ±1 mm for both banks were simulated to understand error patterns in resulted FEMs. Applying clinical IMRT QA standard to the online re-optimized plans suggests the deliverability of online re-optimized plans are similar to regular IMRT plans. Applying the proposed QA system to online re-optimized plans also reveals excellent delivery accuracy: over 99% leaf position differences are < 0.5 mm, and the majority of pixels in FEMs are < 0.5 MU with errors exceeding 0.5 MU primarily located on the edge of the fields. All clinical FEMs observed in this study have positive errors on the left edges, and negative errors on the right. Analysis on a typical FEM reveals positive correlation between the magnitude of fluence errors and the corresponding leaf speed. FEMs of simulated erroneous delivery exhibit distinct patterns for different MLC error magnitudes and directions, indicating the proposed QA system is highly specific in detecting the source of errors. Based on these results, it can be concluded that the proposed online delivery monitoring system is very sensitive to leaf position errors, highly specific of the error types, and therefore meets the purpose for online delivery accuracy verification. Post-treatment dosimetric verification shows minimal difference between planned and actual delivered DVH, further confirming that the online re-optimized plans can be accurately delivered.</p><p>In summary, this project addressed two most important challenges for clinical implementation of online ART, efficiency and quality assurance, through innovative system design, technique development and validation with clinical data. The efficiencies of the overall treatment scheme and the re-optimization process have been improved significantly; and the proposed online quality assurance system is found to be effective in catching and differentiating leaf motion errors.</p> / Dissertation

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第21395号 / 情博第681号 / 新制||情||117(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 石田 亨, 教授 吉川 正俊, 教授 河原 達也 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

This research delves into the complex interplay between wind turbine operations and bat conservation efforts, focusing on mitigating bat mortality caused by wind turbines in France. Despite comprehensive legal safeguards and conservation measures, bat fatalities remain a pressing concern, necessitating innovative solutions to reconcile environmental protection with energy production. The problem statement revolves around the challenge of optimising bat curtailment plans to minimise bat mortality while mitigating energy losses. With over 80% of bat species in France affected by wind turbine collisions, the urgency of this issue is underscored by the significant ecological implications and regulatory imperatives. Despite the existence of curtailment plans, there is a lack of comprehensive understanding regarding their effectiveness and potential trade-offs. This problem warrants a Master’s thesis project due to its multifaceted nature and practical implications. It requires a nuanced understanding of bat behaviours, wind turbine operations, and regulatory frameworks, making it both intellectually stimulating and socially relevant. Previous efforts have largely focused on static curtailment plans, leaving room for exploration of dynamic approaches and optimisation strategies. The methodology employed in this study involves the development of a Power BI tool and key performance indicators (KPIs) to evaluate different curtailment plans. Through comparative analysis, insights are gained into the efficacy of static versus dynamic curtailment plans, as well as the influence of weather conditions, such as rain, on curtailment decisions. Additionally, sensitivityanalysis is conducted to identify the most influential parameters and optimise curtailment plans accordingly. The key results of this study demonstrate the superiority of dynamic curtailment plans in reducing energy losses while maintaining sufficient protection for bat activity (higher than the 90% protection rate required by law) compared to static approaches (50% reduction in losses over an entire curtailment season). Insights gleaned from sensitivity analysis highlight the critical parameters influencing energy losses, informing targeted modifications to curtailment plans. Furthermore, the study underscores the importance of considering continuous variables, such as humidity, and site-specific factors, such as sunrise and sunset times, for more precise conservation strategies. The implications of this research extend beyond academia, informing policy-making and industry practices in wind energy and biodiversity conservation. By optimizing curtailment plans, stakeholders can achieve a balance between environmental protection and renewable energy generation, paving the way for sustainable development. Future research avenues include refining curtailment strategies based on continuous variables and conducting field studies to validate findings across diverse wind farm locations. / Denna forskning utforskar det komplexa samspel mellan vindkraftverkens drift och fladdermusbevarande insatser, med fokus på att mildra fladdermusdödlighet orsakad av vindkraftverk i Frankrike. Trots omfattande lagliga skydd och bevarandeåtgärder förblir fladdermusdödsfall ett påtagligt bekymmer, vilket kräver innovativa lösningar för att förena miljöskydd med energiproduktion. Problemformuleringen kretsar kring utmaningen att optimera fladdermusbegränsningsplaner för att minimera fladdermusdödlighet samtidigt som energiförluster mildras. Med över 80% av fladdermusarterna i Frankrike påverkade av kollisioner med vindkraftverk, understryks brådskan i detta ärende av dess betydande ekologiska konsekvenser och reglerande krav. Trots att begränsningsplaner existerar, finns det en brist på en heltäckande förståelse för deras effektivitet och potentiella avvägningar. Detta problem motiverar ett magisterprojekt på grund av dess mångfacetterade natur och praktiska konsekvenser. Det kräver en nyanserad förståelse för fladdermusars beteenden, vindkraftverks drift och reglerande ramar, vilket gör det både intellektuellt stimulerande och socialt relevant. Tidigare insatser har i stor utsträckning fokuserat på statiska begränsningsplaner och lämnat utrymme för utforskning av dynamiska tillvägagångssätt och optimeringsstrategier. Metoden som används i denna studie innefattar utvecklingen av ett Power BI-verktyg och nyckelprestationsindikatorer för att utvärdera olika begränsningsplaner. Genom jämförande analys får man insikter om effektiviteten hos statiska jämfört med dynamiska begränsningsplaner, samt påverkan av väderförhållanden, såsom regn, på begränsningsbeslut. Dessutom genomförs känslighetsanalys för att identifiera de mest inflytelserika parametrarna och optimera begränsningsplanerna därefter. De viktigaste resultaten av denna studie visar överlägsenheten hos dynamiska begränsningsplaner när det gäller att minska energiförluster samtidigt som tillräckligt skydd för fladdermusaktivitet bibehålls (högre än den 90% skyddsnivå som krävs enligt lag) jämfört med statiska metoder (50% minskning av förluster under en hel begränsningssäsong). Insikter från känslighetsanalysen belyser de kritiska parametrarna som påverkar energiförluster och ger vägledning för målinriktade modifieringar av begränsningsplaner. Dessutom betonar studien vikten av att beakta kontinuerliga variabler, såsom luftfuktighet, och platsspecifika faktorer, såsom soluppgångs- och solnedgångstider, för mer precisa bevarandestrategier. Denna forsknings betydelse sträcker sig bortom akademin och informerar beslutsfattande inom politik och branschpraxis inom vindenergi och biologisk mångfaldsbevarande. Genom att optimera begränsningsplaner kan intressenter uppnå en balans mellan miljöskydd och förnybar energiproduktion, vilket banar väg för hållbar utveckling. Framtida forskningsvägar inkluderar att förädla begränsningsstrategier baserade på kontinuerliga variabler och att genomföra fältstudier för att validera resultat på olika vindkraftsplatser.