Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative
  Global Transformer Model
Dayang Wang Srivathsa Pasumarthi Greg Zaharchuk Ryan Chamberlain
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.