No description available.

The primary objective of this research was to develop and implement a new approach to on-line adaptive radiation therapy (ART) in which daily imaging, plan adaptation and radiation delivery are temporally integrated and performed concurrently. The advantages of this approach are: reduction of treatment time compared to conventional on-line ART; ability to perform a complete plan re-optimization with minimal extension of treatment time; ability to detect and correct for intra-fractional patient motion. This work was motivated by an initial study which compared four radiosurgery techniques. This study was the first quantitative analysis of the correlation between patient anatomy and the quality of treatment plans produced by different radiosurgery techniques. A number of different relationships, both qualitative and quantitative, were discovered between patient anatomy and dosimetric parameters for different techniques. The results were used to successfully predetermine the optimal radiosurgery technique based on patient anatomy. The first step in developing a new approach to on-line ART involved accelerating plan adaptation (re-optimization) using direct aperture optimization (DAO). A series of techniques for adapting the original treatment plan to correct for the deterioration of dose distribution quality caused by the anatomical deformations were investigated. Through modification of the DAO algorithm the optimization search space was reduced and the plan adaptation was significantly accelerated. Next, a new approach to on-line ART was proposed and investigated, in which accelerated plan adaptation and radiation delivery were integrated together and performed concurrently. A fundamental advantage of this approach is that most of the plan re-optimization was performed during radiation delivery, so the time spent adapting the original plan did not significantly increase the overall treatment time. Finally, daily imaging, accelerated plan adaptation and radiation delivery were all temporally integrated using an integrated Linac/Cone Beam CT system. Intra-fractional patient images were used to successfully re-optimize the original treatment plan in near real-time to account for anatomy deformations. This thesis concludes that integration of daily imaging, plan adaptation and radiation delivery for near real-time ART is both feasible and advantageous. With further advances in related technologies, it promises to become a part of clinical practice in the near future.

The primary objective of this research was to develop and implement a new approach to on-line adaptive radiation therapy (ART) in which daily imaging, plan adaptation and radiation delivery are temporally integrated and performed concurrently. The advantages of this approach are: reduction of treatment time compared to conventional on-line ART; ability to perform a complete plan re-optimization with minimal extension of treatment time; ability to detect and correct for intra-fractional patient motion. This work was motivated by an initial study which compared four radiosurgery techniques. This study was the first quantitative analysis of the correlation between patient anatomy and the quality of treatment plans produced by different radiosurgery techniques. A number of different relationships, both qualitative and quantitative, were discovered between patient anatomy and dosimetric parameters for different techniques. The results were used to successfully predetermine the optimal radiosurgery technique based on patient anatomy. The first step in developing a new approach to on-line ART involved accelerating plan adaptation (re-optimization) using direct aperture optimization (DAO). A series of techniques for adapting the original treatment plan to correct for the deterioration of dose distribution quality caused by the anatomical deformations were investigated. Through modification of the DAO algorithm the optimization search space was reduced and the plan adaptation was significantly accelerated. Next, a new approach to on-line ART was proposed and investigated, in which accelerated plan adaptation and radiation delivery were integrated together and performed concurrently. A fundamental advantage of this approach is that most of the plan re-optimization was performed during radiation delivery, so the time spent adapting the original plan did not significantly increase the overall treatment time. Finally, daily imaging, accelerated plan adaptation and radiation delivery were all temporally integrated using an integrated Linac/Cone Beam CT system. Intra-fractional patient images were used to successfully re-optimize the original treatment plan in near real-time to account for anatomy deformations. This thesis concludes that integration of daily imaging, plan adaptation and radiation delivery for near real-time ART is both feasible and advantageous. With further advances in related technologies, it promises to become a part of clinical practice in the near future.

The primary objective of this research was to develop and implement a new approach to on-line adaptive radiation therapy (ART) in which daily imaging, plan adaptation and radiation delivery are temporally integrated and performed concurrently. The advantages of this approach are: reduction of treatment time compared to conventional on-line ART; ability to perform a complete plan re-optimization with minimal extension of treatment time; ability to detect and correct for intra-fractional patient motion. This work was motivated by an initial study which compared four radiosurgery techniques. This study was the first quantitative analysis of the correlation between patient anatomy and the quality of treatment plans produced by different radiosurgery techniques. A number of different relationships, both qualitative and quantitative, were discovered between patient anatomy and dosimetric parameters for different techniques. The results were used to successfully predetermine the optimal radiosurgery technique based on patient anatomy. The first step in developing a new approach to on-line ART involved accelerating plan adaptation (re-optimization) using direct aperture optimization (DAO). A series of techniques for adapting the original treatment plan to correct for the deterioration of dose distribution quality caused by the anatomical deformations were investigated. Through modification of the DAO algorithm the optimization search space was reduced and the plan adaptation was significantly accelerated. Next, a new approach to on-line ART was proposed and investigated, in which accelerated plan adaptation and radiation delivery were integrated together and performed concurrently. A fundamental advantage of this approach is that most of the plan re-optimization was performed during radiation delivery, so the time spent adapting the original plan did not significantly increase the overall treatment time. Finally, daily imaging, accelerated plan adaptation and radiation delivery were all temporally integrated using an integrated Linac/Cone Beam CT system. Intra-fractional patient images were used to successfully re-optimize the original treatment plan in near real-time to account for anatomy deformations. This thesis concludes that integration of daily imaging, plan adaptation and radiation delivery for near real-time ART is both feasible and advantageous. With further advances in related technologies, it promises to become a part of clinical practice in the near future. / Science, Faculty of / Physics and Astronomy, Department of / Graduate

No description available.

A previous approach to robust intensity-modulated radiation therapy (IMRT) treatment planning for moving tumours in the lung involves solving a single planning problem before treatment and using the resulting solution in all of the subsequent treatment sessions. In this thesis, we develop two adaptive robust IMRT optimization approaches for lung cancer, which involve using information gathered in prior treatment sessions to guide the reoptimization of the treatment for the next session. The first method is based on updating an estimate of the uncertain effect, while the second is based on additionally updating the dose requirements to account for prior errors in dose. We present computational results using real patient data for both methods and an asymptotic analysis for the first method. Through these results, we show that both methods lead to improvements in the final dose distribution over the traditional robust approach, but differ greatly in their daily dose performance.

A previous approach to robust intensity-modulated radiation therapy (IMRT) treatment planning for moving tumours in the lung involves solving a single planning problem before treatment and using the resulting solution in all of the subsequent treatment sessions. In this thesis, we develop two adaptive robust IMRT optimization approaches for lung cancer, which involve using information gathered in prior treatment sessions to guide the reoptimization of the treatment for the next session. The first method is based on updating an estimate of the uncertain effect, while the second is based on additionally updating the dose requirements to account for prior errors in dose. We present computational results using real patient data for both methods and an asymptotic analysis for the first method. Through these results, we show that both methods lead to improvements in the final dose distribution over the traditional robust approach, but differ greatly in their daily dose performance.

L'imagerie est maintenant partie intégrante des traitements de radiothérapie. Avec la technologie CBCT embarquée sur les appareils de traitement, l'imagerie tomographique permet non seulement de repositionner fidèlement le patient tout au long de son traitement mais aussi d'adapter la planification initiale aux modifications quotidiennes de volume. C'est la radiothérapie adaptative, objet des travaux de cette thèse. Nous avons établi les limites techniques de précision de repositionnement des équipements utilisé. Ensuite, à partir des acquisitions CBCT quotidiennes de patients traités pour la prostate, nous avons élaboré une stratégie de traitement basée sur une banque de plans personnalisés. Nous avons mis au point une méthode semi-automatique de sélection du plan de traitement du jour qui a montré une efficacité supérieure à la sélection par des opérateurs expérimentés. Enfin, nous avons quantifié les doses additionnelles à la dose thérapeutique associées à l'utilisation quotidienne de l'imagerie CBCT. En conclusion, on peut dire qu'avec l'imagerie CBCT embarquée, on peut voir ce que l'on veut traiter, irradier ce que l'on a vu et contrôler ce qu'on a traité. / Imaging is now fully integrated in the radiation therapy process. With on-board CBCT systems, tomography imaging allows not only patient positioning but also treatment planning adaptation with patient anatomy modifications, throughout the entire treatment. This is called adaptive radiation therapy, and is the main subject of this PhD thesis. During this work, we measured the repositioning accuracy of the system used. We also developed a treatment strategy using daily CBCT images and a personalized plan database to adapt treatment plan to patient anatomy. We found a way to select the daily treatment plan that shows superiority over operator selection. Then we also quantified the additional dose delivered while using this technique and the impact with regards to the risks added to patients. As a conclusion, with CBCT imaging, radiation therapy has entered an era where one can see what need to be treated, can treat what has been seen and can control what has been treated.

<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