Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model

TL;DR

This paper introduces Gformer, a transformer-based iterative model that synthesizes MRI images with arbitrary contrast levels, improving contrast dose reduction and elimination while supporting clinical tasks like tumor segmentation.

Contribution

The paper presents a novel Gformer model with sub-sampling attention and rotational shift modules for flexible contrast synthesis in MRI, addressing dataset limitations and variability in contrast agents.

Findings

Gformer outperforms state-of-the-art contrast synthesis methods.

The model effectively supports dose reduction and tumor segmentation tasks.

Quantitative results demonstrate improved image quality and clinical utility.

Abstract

Deep learning (DL) based contrast dose reduction and elimination in MRI imaging is gaining traction, given the detrimental effects of Gadolinium-based Contrast Agents (GBCAs). These DL algorithms are however limited by the availability of high quality low dose datasets. Additionally, different types of GBCAs and pathologies require different dose levels for the DL algorithms to work reliably. In this work, we formulate a novel transformer (Gformer) based iterative modelling approach for the synthesis of images with arbitrary contrast enhancement that corresponds to different dose levels. The proposed Gformer incorporates a sub-sampling based attention mechanism and a rotational shift module that captures the various contrast related features. Quantitative evaluation indicates that the proposed model performs better than other state-of-the-art methods. We further perform quantitative evaluation on downstream tasks such as dose reduction and tumor segmentation to demonstrate the clinical utility.