Model-based deep learning with fully connected neural networks for accelerated magnetic resonance parameter mapping.

ABSTRACT:

Purpose

Quantitative magnetic resonance imaging (qMRI) enables imaging of physical parameters related to the nuclear spin of protons in tissue, and is poised to revolutionize clinical research. However, improving the accuracy and clinical relevance of qMRI is essential for its practical implementation. This requires significantly reducing the currently lengthy acquisition times to enable clinical examinations and provide an environment where clinical accuracy and reliability can be verified. Deep learning (DL) has shown promise in significantly reducing imaging time and improving image quality in recent years. This study introduces a novel approach, quantitative deep cascade of convolutional network (qDC-CNN), as a framework for accelerated quantitative parameter mapping, offering

SUBMITTER: Fujita N 

PROVIDER: S-EPMC12689753 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature