The application of Magnetic Resonance Imaging (MRI) to guide Radiation Therapy (RT) procedures is the new quantum leap in radiation oncology. This approach, known as MRI-guided RT, has been motivated by the superior soft-tissue contrast, improved organ motion visualization, and enhanced capacity for imaging of tissue and tumor physiology provided by MRI compared to Computed Tomography (CT). The first generation of MRI-guided RT was based on the use of offline MRI (outside the treatment session) for treatment planning and evaluation of treatment response. The current second generation was marked by the introduction of MRI-guided linear accelerator (MR-linac) systems. These systems combine MRI with a linear accelerator to provide an online MRI platform for treatment planning, adaptation, and monitoring during a treatment session.
The goal of this Research Topic is to discuss new developments in MRI for radiation therapy and translation of MRI-guided radiation therapy to clinical practice. Particular attention will be given to online MRI-guided RT, which offers unique opportunities for adaptive treatment within each fraction in tumors affected by continuous motion (e.g. tumors in the lungs, kidneys, liver, pancreas, and neck) and by sporadic motion (e.g. tumors in the prostate and rectum). Perspectives on intra-fraction and inter-fraction adaptation and anatomical and biological adaptation will be collected to analyze short-term and long-term clinical impact.
This Research Topic welcomes manuscripts focused on the development and application of MRI techniques to radiation therapy, including:
• Fast and geometrically accurate MRI techniques for treatment planning
• Real-time MRI techniques for adaptive treatment of mobile organs
• Quantitative MRI techniques (perfusion, diffusion, MR fingerprinting, etc.) for biological adaptation and evaluation of treatment response
• Application of artificial intelligence to the acceleration of MRI acquisition and autosegmenation
• Application of MRI to perform inter-fraction and intra-fraction adaptation on an MR-linac system
The application of Magnetic Resonance Imaging (MRI) to guide Radiation Therapy (RT) procedures is the new quantum leap in radiation oncology. This approach, known as MRI-guided RT, has been motivated by the superior soft-tissue contrast, improved organ motion visualization, and enhanced capacity for imaging of tissue and tumor physiology provided by MRI compared to Computed Tomography (CT). The first generation of MRI-guided RT was based on the use of offline MRI (outside the treatment session) for treatment planning and evaluation of treatment response. The current second generation was marked by the introduction of MRI-guided linear accelerator (MR-linac) systems. These systems combine MRI with a linear accelerator to provide an online MRI platform for treatment planning, adaptation, and monitoring during a treatment session.
The goal of this Research Topic is to discuss new developments in MRI for radiation therapy and translation of MRI-guided radiation therapy to clinical practice. Particular attention will be given to online MRI-guided RT, which offers unique opportunities for adaptive treatment within each fraction in tumors affected by continuous motion (e.g. tumors in the lungs, kidneys, liver, pancreas, and neck) and by sporadic motion (e.g. tumors in the prostate and rectum). Perspectives on intra-fraction and inter-fraction adaptation and anatomical and biological adaptation will be collected to analyze short-term and long-term clinical impact.
This Research Topic welcomes manuscripts focused on the development and application of MRI techniques to radiation therapy, including:
• Fast and geometrically accurate MRI techniques for treatment planning
• Real-time MRI techniques for adaptive treatment of mobile organs
• Quantitative MRI techniques (perfusion, diffusion, MR fingerprinting, etc.) for biological adaptation and evaluation of treatment response
• Application of artificial intelligence to the acceleration of MRI acquisition and autosegmenation
• Application of MRI to perform inter-fraction and intra-fraction adaptation on an MR-linac system