ESTRO meets Asia 2024 - Abstract Book

S20

Invited Speaker

ESTRO meets Asia 2024

Optimizing imaging protocols in radiotherapy is paramount for enhancing treatment efficacy and patient safety. Radiotherapy relies heavily on precise imaging to ensure accurate tumor targeting while minimizing exposure to healthy tissues. Imaging protocols are integral throughout radiotherapy, encompassing treatment planning, delivery, and post-treatment follow-up. Effective optimization of these protocols is essential for improving both patient outcomes and operational efficiency in clinical settings. High-quality imaging is fundamental in radiotherapy for delineating tumor boundaries and critical structures. Techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) provide the necessary anatomical and functional information for precise treatment planning. Advanced imaging methods like cone-beam CT (CBCT) (MVCT) and four-dimensional CT (4D-CT) allow for real-time visualization of tumor movement, further enhancing treatment accuracy. A significant challenge in imaging protocol optimization is reducing the radiation dose associated with imaging procedures. High doses can contribute to cumulative radiation exposure, increasing the risk of secondary malignancies. Developing low-dose imaging protocols that maintain diagnostic quality is critical. Ensuring high image quality is vital for accurate tumor delineation. Patient movement, imaging artifacts, and machine calibration can affect image quality. Optimization efforts must balance image clarity with radiation dose and acquisition time. Integrating diverse imaging modalities with radiotherapy delivery systems poses a technical challenge. Ensuring seamless communication and synchronization between imaging and treatment devices is essential for effective protocol optimization. Variability in patient anatomy and tumor motion necessitates individualized imaging protocols. Adaptive imaging strategies that tailor protocols to each patient's specific needs are being explored to address this variability. Integrating artificial intelligence (AI) and machine learning (ML) into imaging protocol optimization holds significant promise. AI-driven algorithms can analyze vast datasets to identify optimal imaging protocols, predict patient-specific responses, and automate imaging and treatment planning aspects. Additionally, the development of hybrid imaging modalities that combine the strengths of different techniques offers the potential for enhanced precision and efficiency in radiotherapy. Optimization of imaging protocols in radiotherapy is crucial for improving treatment safety and efficiency. Advances in imaging technology, dose reduction methods, real-time adaptation, and interdisciplinary collaboration are key strategies driving this optimization. The future integration of AI and hybrid imaging modalities promises further enhancements in treatment precision and patient outcomes. Ongoing research and innovation in this field are essential for improving radiotherapy practices.

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MLC modelling and configuration in TPSs: Current challenges and new methodologies

Victor Hernandez 1 , Jordi Saez 2

1 Medical Physics, Hospital Sant Joan de Reus, Reus, Spain. 2 Radiation Oncology, Hospital Clínic, Barcelona, Spain

Abstract

The accurate modeling of Multi-Leaf Collimators (MLC) within Treatment Planning Systems (TPSs) is crucial for the precision of IMRT/VMAT dose calculations. Despite its importance, there is a lack of standardized procedures for commissioning MLC models in TPSs as per international protocols and guidelines. This absence leads to varied