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

ABSTRACT:

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 ( <500  ms). Theory and Methods: Respiratory-resolved T1 -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. TEMPEST was validated using 4D respiratory-resolved MRI, a digital phantom, and a physical motion phantom. The time-resolved motion estimation was evaluated in-vivo

SUBMITTER: Terpstra ML 

PROVIDER: S-EPMC9298075 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature