Towards mid-position based Stereotactic Body Radiation Therapy using online magnetic resonance imaging guidance for central lung tumours.

Central lung tumours can be treated by magnetic resonance (MR)-guided radiotherapy. Complications might be reduced by decreasing the Planning Target Volume (PTV) using mid-position (midP)-based planning instead of Internal Target Volume (ITV)-based planning. In this study, we aimed to verify a method to automatically derive patient-specific PTV margins for midP-based planning, and show dosimetric robustness of midP-based planning for a 1.

First experimental demonstration of VMAT combined with MLC tracking for single and multi fraction lung SBRT on an MR-linac.

Background And Purpose: VMAT is not currently available on MR-linacs but could maximize plan conformality. To mitigate respiration without compromising delivery efficiency, MRI-guided MLC tumour tracking was recently developed for the 1.5 T Unity MR-linac (Elekta AB, Stockholm, Sweden) in combination with IMRT.

Intensive Cryotherapy in the Emergency Department (ICED): A Randomized Controlled Trial.

Introduction: Pain control is an essential component of musculoskeletal injury treatment in the emergency department (ED). We evaluated the most effective type of cryotherapy for analgesia of acute musculoskeletal injury and the impact on opioid utilization.

Methods: This was a prospective, randomized, single-blind controlled trial of adult ED patients who presented with acute musculoskeletal pain.

Synthetic CT for single-fraction neoadjuvant partial breast irradiation on an MRI-linac.

A synthetic computed tomography (sCT) is required for daily plan optimization on an MRI-linac. Yet, only limited information is available on the accuracy of dose calculations on sCT for breast radiotherapy. This work aimed to (1) evaluate dosimetric accuracy of treatment plans for single-fraction neoadjuvant partial breast irradiation (PBI) on a 1.

DeepDose: a robust deep learning-based dose engine for abdominal tumours in a 1.5 T MRI radiotherapy system.

We present a robust deep learning-based framework for dose calculations of abdominal tumours in a 1.5 T MRI radiotherapy system. For a set of patient plans, a convolutional neural network is trained on the dose of individual multi-leaf-collimator segments following the DeepDose framework.