Few-shot learning for highly accelerated 3D time-of-flight MRA reconstruction.

ABSTRACT:

Purpose

To develop a deep learning-based reconstruction method for highly accelerated 3D time-of-flight MRA (TOF-MRA) that achieves high-quality reconstruction with robust generalization using extremely limited acquired raw data, addressing the challenge of time-consuming acquisition of high-resolution, whole-head angiograms.

Methods

A novel few-shot learning-based reconstruction framework is proposed, featuring a 3D variational network specifically designed for 3D TOF-MRA that is pre-trained on simulated complex-valued, multi-coil raw k-space datasets synthesized from diverse open-source magnitude images and fine-tuned using only two single-slab experimentally acquired datasets. The proposed approach was evaluated against existing methods on acquired retrospectively undersa

SUBMITTER: Li H 

PROVIDER: S-EPMC12681298 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature