mlsanityData to 14 Oct 2026

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

arXiv 2307.11980 ↗First posted 22 Jul 2023eess.IV cs.CV cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 16 of its 29 references that are in the map. First posted on arXiv 22 Jul 2023.

Builds on

16 of 29 references.

References · 16

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  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
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4 more
  1. 2017Face Aging With Conditional Generative Adversarial Networkscited by 51
  2. 2022One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer for Missing Data Imputationcited by 40
  3. 2021TED-net: Convolution-free T2T Vision Transformer-based Encoder-decoder Dilation network for Low-dose CT Denoisingcited by 21
  4. 2019IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRIcited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 2.

3 papers.

  1. 2024Gadolinium dose reduction for brain MRI using conditional deep learningcited by 1
  2. 2026Exploiting Intermediate Reconstructions in Optical Coherence Tomography for Test-Time Adaption of Medical Image Segmentationcited by 1
  3. 2026Medical Foundation Model Features as Perceptual Loss for Brain MRI Contrast Dose Simulationcited by 0