Self-supervised learning for improved calibrationless radial MRI with NLINV-Net.

ABSTRACT:

Purpose

To develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training.

Methods

NLINV-Net is a model-based neural network architecture that directly estimates images and coil sensitivities from (radial) k-space data via nonlinear inversion (NLINV). Combined with a training strategy using self-supervision via data undersampling (SSDU), it can be used for imaging problems where no ground truth reconstructions are available. We validated the method for (1) real-time cardiac imaging and (2) single-shot subspace-based quantitative T1 mapping. Furthermore, region-optimized virtual (ROVir) coils were used to suppress artifacts stemming from outside the field of view and to focus the k-space-based SS

SUBMITTER: Blumenthal M 

PROVIDER: S-EPMC12328127 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature