Patterns of practice of image guided particle therapy for cranio-spinal irradiation: A site specific multi-institutional survey of European Particle Therapy Network.

Purpose: To investigate the current practice patterns in image-guided particle therapy (IGPT) for cranio-spinal irradiation (CSI).

Methods: A multi-institutional survey was distributed to European particle therapy centres to analyse all aspects of IGPT. Based on the survey results, a Delphi consensus analysis was developed to define minimum requirements and optimal workflow for clinical practice.

Challenges and opportunities in the development and clinical implementation of artificial intelligence based synthetic computed tomography for magnetic resonance only radiotherapy.

Synthetic computed tomography (sCT) generated from magnetic resonance imaging (MRI) can serve as a substitute for planning CT in radiation therapy (RT), thereby removing registration uncertainties associated with multi-modality imaging pairing, reducing costs and patient radiation exposure. CE/FDA-approved sCT solutions are nowadays available for pelvis, brain, and head and neck, while more complex deep learning (DL) algorithms are under investigation for other anatomic sites. The main challenge in achieving a widespread clinical implementation of sCT lies in the absence of consensus on sCT commissioning and quality assurance (QA), resulting in variation of sCT approaches across different hospitals.

Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data.

Background And Purpose: Active breathing motion management in radiotherapy consists of motion monitoring, quantification and mitigation. It is impacted by associated latencies of a few 100 ms. Artificial neural networks can successfully predict breathing motion and eliminate latencies.