A Comprehensive Survey on Magnetic Resonance Image Reconstruction
Journal:
arXiv
Published Date:
Mar 10, 2025
Abstract
Magnetic resonance imaging (MRI) reconstruction is a fundamental task aimed
at recovering high-quality images from undersampled or low-quality MRI data.
This process enhances diagnostic accuracy and optimizes clinical applications.
In recent years, deep learning-based MRI reconstruction has made significant
progress. Advancements include single-modality feature extraction using
different network architectures, the integration of multimodal information, and
the adoption of unsupervised or semi-supervised learning strategies. However,
despite extensive research, MRI reconstruction remains a challenging problem
that has yet to be fully resolved. This survey provides a systematic review of
MRI reconstruction methods, covering key aspects such as data acquisition and
preprocessing, publicly available datasets, single and multi-modal
reconstruction models, training strategies, and evaluation metrics based on
image reconstruction and downstream tasks. Additionally, we analyze the major
challenges in this field and explore potential future directions.
