mlsanityData to 7 Oct 2026

Synthesizing MR Image Contrast Enhancement Using 3D High-resolution ConvNets

arXiv 2104.01592 ↗First posted 4 Apr 2021eess.IV cs.CVcited by 5

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Abstract (arXiv)

textitObjective: Gadolinium-based contrast agents (GBCAs) have been widely used to better visualize disease in brain magnetic resonance imaging (MRI). However, gadolinium deposition within the brain and body has raised safety concerns about the use of GBCAs. Therefore, the development of novel approaches that can decrease or even eliminate GBCA exposure while providing similar contrast information would be of significant use clinically. textitMethods: In this work, we present a deep learning based approach for contrast-enhanced T1 synthesis on brain tumor patients. A 3D high-resolution fully convolutional network (FCN), which maintains high resolution information through processing and aggregates multi-scale information in parallel, is designed to map pre-contrast MRI sequences to contrast-enhanced MRI sequences. Specifically, three pre-contrast MRI sequences, T1, T2 and apparent diffusion coefficient map (ADC), are utilized as inputs and the post-contrast T1 sequences are utilized as target output. To alleviate the data imbalance problem between normal tissues and the tumor regions, we introduce a local loss to improve the contribution of the tumor regions, which leads to better enhancement results on tumors. textitResults: Extensive quantitative and visual assessments are performed, with our proposed model achieving a PSNR of 28.24dB in the brain and 21.2dB in tumor regions. textitConclusion and Significance: Our results suggest the potential of substituting GBCAs with synthetic contrast images generated via deep learning. Code is available at urlhttps://github.com/chenchao666/Contrast-enhanced-MRI-Synthesis

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 26 of its 51 references that are in the map. First posted on arXiv 4 Apr 2021.

Builds on

26 of 51 references.

References · 26

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14 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 0; 2024: 1; 2025: 0; 2026: 2.

5 papers.

  1. 2022One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer for Missing Data Imputationcited by 40
  2. 2024Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networkscited by 6
  3. 2026MRI Contrast Enhancement Kinetics World Modelcited by 4
  4. 2022Brain MRI-to-PET Synthesis using 3D Convolutional Attention Networkscited by 2
  5. 2026Uncertainty-guided Generation of Dark-field Radiographscited by 0