# Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model Source: [Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model]() Collection index: [llms.txt]() Data updated: 2026-01-02T03:11:53.500Z Authors: Dayang Wang, Srivathsa Pasumarthi, Greg Zaharchuk, Ryan Chamberlain Published: 2023-07-22T04:44:57.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Low-dose image synthesis on Synthesized 10% dose images (test)](): Low-dose image synthesis; Synthesized 10% dose images (test); PSNR: 42.29. ## Original sources and implementation [Original paper]() [PDF]()