mlsanityData to 14 Oct 2026

HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models

arXiv 2504.18405 ↗First posted 25 Apr 2025eess.IV cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a crucial role in the detection and characterization of focal liver lesions, with the hepatobiliary phase (HBP) providing essential diagnostic information. However, acquiring HBP images requires prolonged scan times, which may compromise patient comfort and scanner throughput. In this study, we propose a deep learning based approach for synthesizing HBP images from earlier contrast phases (precontrast and transitional) and compare three generative models: a perceptual U-Net, a perceptual GAN (pGAN), and a denoising diffusion probabilistic model (DDPM). We curated a multi-site DCE-MRI dataset from diverse clinical settings and introduced a contrast evolution score (CES) to assess training data quality, enhancing model performance. Quantitative evaluation using pixel-wise and perceptual metrics, combined with qualitative assessment through blinded radiologist reviews, showed that pGAN achieved the best quantitative performance but introduced heterogeneous contrast in out-of-distribution cases. In contrast, the U-Net produced consistent liver enhancement with fewer artifacts, while DDPM underperformed due to limited preservation of fine structural details. These findings demonstrate the feasibility of synthetic HBP image generation as a means to reduce scan time without compromising diagnostic utility, highlighting the clinical potential of deep learning for dynamic contrast enhancement in liver MRI. A project demo is available at: https://jhooge.github.io/hepatogen

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 41 of its 67 references that are in the map. First posted on arXiv 25 Apr 2025.

Builds on

41 of 67 references.

Closest ancestors · 9

Same direction, newest first

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  5. 2023CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis24 Mar
  6. 2023Diffusion Models for Contrast Harmonization of Magnetic Resonance Images14 Mar
  7. 2022Unsupervised Medical Image Translation with Adversarial Diffusion Models17 Jul
  8. 2022A Novel Unified Conditional Score-based Generative Framework for Multi-modal Medical Image Completion7 Jul
  9. 2021Diffusion Models for Implicit Image Segmentation Ensembles6 Dec

Other references · 32

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  5. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  6. 2022Generative Adversarial Networkscited by 5,627
  7. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  8. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  9. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
  10. 2016Density estimation using Real NVPcited by 2,042
  11. 2020Image Quality Assessment: Unifying Structure and Texture Similaritycited by 811
  12. 2023DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Datacited by 337
20 more
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  2. 2019Residual Flows for Invertible Generative Modelingcited by 257
  3. 2018MedGAN: Medical Image Translation using GANscited by 152
  4. 2018Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networkscited by 132
  5. 2016Unsupervised domain adaptation in brain lesion segmentation with adversarial networkscited by 125
  6. 2019Missing MRI Pulse Sequence Synthesis using Multi-Modal Generative Adversarial Networkcited by 98
  7. 2017Deep MR to CT Synthesis using Unpaired Datacited by 86
  8. 2020Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesiscited by 86
  9. 2021Breaking the Dilemma of Medical Image-to-image Translationcited by 82
  10. 2022Conversion Between CT and MRI Images Using Diffusion and Score-Matching Modelscited by 69
  11. 2016A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessmentcited by 57
  12. 2018Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data sizecited by 42
  13. 2019DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesiscited by 36
  14. 2018Deep CT to MR Synthesis using Paired and Unpaired Datacited by 31
  15. 2020Progressively Volumetrized Deep Generative Models for Data-Efficient Contextual Learning of MR Image Recoverycited by 14
  16. 2023Synthesis-based Imaging-Differentiation Representation Learning for Multi-Sequence 3D/4D MRIcited by 11
  17. 2024A study on the adequacy of common IQA measures for medical imagescited by 10
  18. 2023Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentationcited by 9
  19. 2023Synthesis of Contrast-Enhanced Breast MRI Using Multi-b-Value DWI-based Hierarchical Fusion Network with Attention Mechanismcited by 8
  20. 2020A Deep learning Approach to Generate Contrast-Enhanced Computerised Tomography Angiography without the Use of Intravenous Contrast Agentscited by 3

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