Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model
This work addresses the need for flexible contrast dose simulation in MRI to reduce reliance on Gadolinium-based contrast agents, which is an incremental improvement for medical imaging applications.
The paper tackles the problem of limited availability of high-quality low-dose MRI datasets for deep learning-based contrast dose reduction by proposing a novel transformer model (Gformer) that synthesizes images with arbitrary contrast enhancement levels, achieving better performance than state-of-the-art methods in quantitative evaluations.
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.