Rapid 4D-MRI reconstruction using a deep radial convolutional neural network: Dracula.

ABSTRACT:

Background and purpose

4D and midposition MRI could inform plan adaptation in lung and abdominal MR-guided radiotherapy. We present deep learning-based solutions to overcome long 4D-MRI reconstruction times while maintaining high image quality and short scan times.

Methods

Two 3D U-net deep convolutional neural networks were trained to accelerate the 4D joint MoCo-HDTV reconstruction. For the first network, gridded and joint MoCo-HDTV-reconstructed 4D-MRI were used as input and target data, respectively, whereas the second network was trained to directly calculate the midposition image. For both networks, input and target data had dimensions of 256 × 256 voxels (2D) and 16 respiratory phases. Deep learning-based MRI were verified against joint MoCo-HDTV-reconstructed MRI usi

SUBMITTER: Freedman JN 

PROVIDER: S-EPMC8216429 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature