Gaussian Processes for real-time 3D motion and uncertainty estimation during MR-guided radiotherapy.

Respiratory motion during radiotherapy causes uncertainty in the tumor's location, which is typically addressed by an increased radiation area and a decreased dose. As a result, the treatments' efficacy is reduced. The recently proposed hybrid MR-linac scanner holds the promise to efficiently deal with such respiratory motion through real-time adaptive MR-guided radiotherapy (MRgRT).

The future of MRI in radiation therapy: Challenges and opportunities for the MR community.

Radiation therapy is a major component of cancer treatment pathways worldwide. The main aim of this treatment is to achieve tumor control through the delivery of ionizing radiation while preserving healthy tissues for minimal radiation toxicity. Because radiation therapy relies on accurate localization of the target and surrounding tissues, imaging plays a crucial role throughout the treatment chain.

A mask-compatible, radiolucent, 8-channel head and neck receive array for MRI-guided radiotherapy treatments and pre-treatment simulation.

Immobilization masks are used to prevent patient movement during head and neck (H&N) radiotherapy. Motion restriction is beneficial both during treatment, as well as in the pre-treatment simulation phase, where magnetic resonance imaging (MRI) is often used for target definition. However, the shape and size of the immobilization masks hinder the use of regular, close-fitting MRI receive arrays.

Real-time 3D motion estimation from undersampled MRI using multi-resolution neural networks.

Purpose: To enable real-time adaptive magnetic resonance imaging-guided radiotherapy (MRIgRT) by obtaining time-resolved three-dimensional (3D) deformation vector fields (DVFs) with high spatiotemporal resolution and low latency (  ms). Theory and Methods: Respiratory-resolved -weighted 4D-MRI of 27 patients with lung cancer were acquired using a golden-angle radial stack-of-stars readout. A multiresolution convolutional neural network (CNN) called TEMPEST was trained on up to 32 retrospectively undersampled MRI of 17 patients, reconstructed with a nonuniform fast Fourier transform, to learn optical flow DVFs.