Treatment Planning Optimization for Non-coplanar Volumetric Modulated Arc Therapy - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Treatment Planning Optimization for Non-coplanar Volumetric Modulated Arc Therapy write by Franklin Okoli. This book was released on 2020. Treatment Planning Optimization for Non-coplanar Volumetric Modulated Arc Therapy available in PDF, EPUB and Kindle. Non-coplanar VMAT has the potential to treat tumors that are located in close proximity of critical organs or that are partially surrounded by normal tissues. The use of dynamic couch-gantry trajectories offers an opportunity for more precise and accurate delivery of radiation during cancer treatments.Despite the improvements that have been seen in non-coplanar VMAT, challenges still persist.In this thesis, we present a methodology for the determination of collision free couch-gantry orientations. This methodology is proposed to avoid the possibility of a collision during the motion of the couch and gantry during treatment.We propose an algorithm based on simulated annealing for non-coplanar VMAT treatment planning. This algorithm is used to select the best possible beam orientations for the accurate delivery of the prescribed dose.We also propose a RRT inspired algorithm for generating the trajectory for the non-coplanarVMAT plan delivery. The algorithm is proposed to solve the problem of the addition of intermediate beam orientations observed in beam selection methods.From the results of studies on the proposed methods, improvements to collision detection, global optimization and trajectory generation for non-coplanar VMAT treatment planning were observed. These improvements include a better dosimetry with respect to reductions in mean dose to the organs-at-risk and a more efficient delivery trajectory.Our studies indicate that there exists a reasonable trade-off between treatment plan quality and plan deliverability if the dosimetry of the VMAT treatment plan is continuously monitored during trajectory generation.
Non-Coplanar Arc Optimization for Stereotactic Ablative Radiotherapy Treatment Planning
Non-Coplanar Arc Optimization for Stereotactic Ablative Radiotherapy Treatment Planning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Non-Coplanar Arc Optimization for Stereotactic Ablative Radiotherapy Treatment Planning write by John Lincoln. This book was released on 2023. Non-Coplanar Arc Optimization for Stereotactic Ablative Radiotherapy Treatment Planning available in PDF, EPUB and Kindle. Stereotactic ablative radiotherapy (SABR) is a technique that delivers a high dose of radiation in a single or small number of fractions and requires rapid fall off outside the target. In current clinical practice it is imperative when treating with ablative doses that radiation to organs-at-risk (OARs) is minimized as much as possible to avoid treatment related toxicity in healthy tissue. Often SABR is delivered with volumetric modulated arc therapy (VMAT), an efficient delivery technique that relies on complex modulation throughout an arc to optimize dose objectives. Non-coplanar optimization methods have been proposed to automatically select geometries that minimize overlap between targets and organs-at-risk (OARs) in the radiation beams-eye-view (BEV). When applied in intensity modulation radiotherapy IMRT or VMAT, these have been shown to significantly reduce dose to OARs as compared to conventional coplanar trajectories. These methods still face barriers to widespread clinical implementation, such as efficiency issues. The purpose of this thesis is to demonstrate that automatically optimized non-coplanar arc geometries for SABR with VMAT leads to dose reductions to OARs. Additionally, this thesis considers the differences between cranial and extracranial SABR and evaluates arc geometry optimization for sites with varying biological complexity. The thesis is comprised of three manuscripts that evaluate the arc geometry optimization for sites treated with SABR. The first manuscript for cranial SABR, "Comparison of anatomically informed template trajectories with patient specific trajectories for stereotactic radiosurgery and radiotherapy," compares a commercial general arc template with an optimized arc template and patient specific arc geometry, concluding that patient specific geometries were dosimetrically superior. The second manuscript for extracranial SABR, "Static couch non-coplanar arc selection optimization for lung SBRT treatment planning," demonstrates a patient-specific method to choose arcs that combine dose reduction to OARs with clinically acceptable target conformity. The third manuscript for extracranial SABR, "Biologically optimized non-coplanar arc selection for small and large target volumes in liver SBRT," shows that choosing optimized arcs has the potential for dose reduction to target-encompassing OARs, such as the liver. These manuscripts address differences and similarities of performing SABR in various sites throughout the body. They offer solutions that are ready to use and require minimal additions to current clinical workflows. Finally, they also demonstrate the dosimetric advantages of non-coplanar arc delivery for multiple disease sites.
Beam Orientation Optimization in Intensity-modulated Radiation Therapy Treatment Planning
Beam Orientation Optimization in Intensity-modulated Radiation Therapy Treatment Planning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Beam Orientation Optimization in Intensity-modulated Radiation Therapy Treatment Planning write by Andrei Pugachev. This book was released on 2002. Beam Orientation Optimization in Intensity-modulated Radiation Therapy Treatment Planning available in PDF, EPUB and Kindle.
Khan's Treatment Planning in Radiation Oncology
Khan's Treatment Planning in Radiation Oncology - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Khan's Treatment Planning in Radiation Oncology write by Faiz M. Khan. This book was released on 2021-09-17. Khan's Treatment Planning in Radiation Oncology available in PDF, EPUB and Kindle. Offering comprehensive coverage of the clinical, physical, and technical aspects of radiation treatment planning, Khan’s Treatment Planning in Radiation Oncology, Fifth Edition, provides a team approach to this complex field. Drs. Paul W. Sperduto and John P. Gibbons are joined by expert contributing authors who focus on the application of physical and clinical concepts to solve treatment planning problems—helping you provide effective, state-of-the-art care for cancer patients. This unique, well-regarded text has been updated throughout to reflect the most current practices in today’s radiation oncology treatment.
Fully Automated Radiation Therapy Treatment Planning Through Knowledge-Based Dose Predictions
Fully Automated Radiation Therapy Treatment Planning Through Knowledge-Based Dose Predictions - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Fully Automated Radiation Therapy Treatment Planning Through Knowledge-Based Dose Predictions write by Angelia Landers. This book was released on 2018. Fully Automated Radiation Therapy Treatment Planning Through Knowledge-Based Dose Predictions available in PDF, EPUB and Kindle. Intensity-modulated radiotherapy treatment planning is an inverse problem that typically includes numerous parameters that have to be manually tuned by expert planners. This process can take hours or even days and can often lead to suboptimal plans. In this study, we developed a technique for fully automated radiotherapy treatment planning with the guidance of dose predictions using high quality or evolving knowledge bases. Knowledge-based planning (KBP) dose prediction provides patient-specific estimations for the capabilities and limitations of a plan. Statistical voxel dose learning (SVDL) was developed to predict the voxel dose of new patients. The method was compared to supervised machine learning methods, spectral regression (SR) and support vector regression (SVR), to evaluate the prediction accuracy and robustness of using small training sets. SVDL was found to have higher prediction accuracy than the more sophisticated machine learning methods and effective even with small training sets. To remove any dependence on hyperparameters that require manual tuning, voxel-based non-coplanar 4 radiotherapy and coplanar volumetric modulated arc therapy (VMAT) optimization problems were modified to include the KBP predicted doses. The new cost functions encourage the plans to meet or improve on the predicted doses. Because of this, the resulting plan quality is heavily reliant on the plan quality of the KBP training set. To ensure high quality plans, non-coplanar and coplanar IMRT plans were manually created using all available beams. The resulting automated plans were of superior quality compared to manually-created plans. In the case of no existing high quality training set, evolving-knowledge-base (EKB) planning was developed. An initial, low quality training set was used for the first epoch of automated planning. In subsequent epochs, the superior plans from the previous epoch were taken as the training set. Overall plan quality was observed to improve through epochs, plateauing after 3 and 6 epochs for lung and head & neck planning, respectively. The final EKB plans were significantly higher quality than manually-created VMAT plans and equivalent to manually-created 4 plans. Through the course of this work, we established a robust and accurate KBP dose prediction technique, which we then utilized in our automated planning protocol. Both the use of high quality training sets and EKB planning created high quality plans in a more efficient and consistent manner than hyperparameter tuning.
