TagGen: Diffusion-based generative model for cardiac MR tagging super resolution.

ABSTRACT:

Purpose

The aim of the work is to develop a cascaded diffusion-based super-resolution model for low-resolution (LR) MR tagging acquisitions, which is integrated with parallel imaging to achieve highly accelerated MR tagging while enhancing the tag grid quality of low-resolution images.

Methods

We introduced TagGen, a diffusion-based conditional generative model that uses low-resolution MR tagging images as guidance to generate corresponding high-resolution tagging images. The model was developed on 50 patients with long-axis-view, high-resolution tagging acquisitions. During training, we retrospectively synthesized LR tagging images using an undersampling rate (R) of 3.3 with truncated outer phase-encoding lines. During inference, we evaluated the performance of TagGen and c

SUBMITTER: Sun C 

PROVIDER: S-EPMC12021330 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature