What type of study is this?

This is a Quantitative Study study.

September 17, 2025

Highly accelerated and motion-robust 2D TSE brain MRI: Combining SAMER retrospective MoCo with a data-driven regularizer

Key Points

The method achieves a higher signal-to-noise ratio, demonstrating a significant improvement in image quality.
Four-fold accelerated scans show reduced motion artifacts, ensuring clearer imaging results in brain studies.
A data-driven deep learning network enhances traditional imaging techniques, integrating motion correction effectively.
In vivo tests indicate that combining these technologies is crucial for advancing 2D TSE imaging practices.

Abstract

Motivation: To meet the clinical demand for fast and motion-robust brain MRI. Goal(s): To integrate retrospective motion correction with a data-driven deep learning reconstruction method to achieve high-quality, motion-robust 2D TSE imaging. Approach: Motion trajectory information was derived from scout and guidance line-based motion correction. A data-driven deep learning network was developed, interleaving multiple conjugate gradient SENSE (+motion) optimizations with network regularization, and was trained and evaluated on TSE data. Results: The method demonstrated improved signal-to-noise ratio (SNR) and reduced motion artifacts in vivo, utilizing 4-fold accelerated scans with induced step motion. Impact: We integrate retrospective motion correction into a data-driven deep learning network to facilitate fast and motion-robust 2D TSE imaging in the brain.

Bookmark

Cite This Study

Lugo et al. (Tue,) studied this question.

synapsesocial.com/papers/68d4597031b076d99fa5c68c https://doi.org/https://doi.org/10.58530/2025/4432

Bookmark