HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models

• URL: http://arxiv.org/abs/2504.18405v1
• Date: Fri, 25 Apr 2025 15:01:09 GMT
• Title: HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models
• Authors: Jens Hooge, Gerard Sanroma-Guell, Faidra Stavropoulou, Alexander Ullmann, Gesine Knobloch, Mark Klemens, Carola Schmidt, Sabine Weckbach, Andreas Bolz,
• Abstract summary: We propose a deep learning based approach for synthesizing hepatobiliary phase (HBP) images from earlier contrast phases.<n> Quantitative evaluation using pixel-wise and perceptual metrics, combined with blinded radiologist reviews, showed that pGAN achieved the best quantitative performance.<n>In contrast, the U-Net produced consistent liver enhancement with fewer artifacts, while DDPM underperformed due to limited preservation of fine structural details.
• Score: 33.7054351451505
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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

Related papers

• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  We propose a unified MRI reconstruction model robust to various measurement undersampling patterns and image resolutions.<n>Our model improves SSIM by 11% and PSNR by 4 dB over a state-of-the-art CNN (End-to-End VarNet) with 600$times$ faster inference than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Multiscale Latent Diffusion Model for Enhanced Feature Extraction from Medical Images [5.395912799904941]
  variations in CT scanner models and acquisition protocols introduce significant variability in the extracted radiomic features. LTDiff++ is a multiscale latent diffusion model designed to enhance feature extraction in medical imaging.
  arXiv  Detail & Related papers  (2024-10-05T02:13:57Z)
• Synthesizing Late-Stage Contrast Enhancement in Breast MRI: A Comprehensive Pipeline Leveraging Temporal Contrast Enhancement Dynamics [0.3499870393443268]
  This study presents a pipeline for synthesizing late-phase DCE-MRI images from early-phase data.<n>The proposed approach introduces a novel loss function, Time Intensity Loss (TI-loss), leveraging the temporal behavior of contrast agents to guide the training of a generative model.<n>Two metrics are proposed to evaluate image quality: the Contrast Agent Pattern Score ($mathcalCP_s$), which validates enhancement patterns in annotated regions, and the Average Difference in Enhancement ($mathcalED$), measuring differences between real and generated enhancements.
  arXiv  Detail & Related papers  (2024-09-03T04:31:49Z)
• Leveraging Multimodal CycleGAN for the Generation of Anatomically Accurate Synthetic CT Scans from MRIs [1.779948689352186]
  We analyse the capabilities of different configurations of Deep Learning models to generate synthetic CT scans from MRI. Several CycleGAN models were trained unsupervised to generate CT scans from different MRI modalities with and without contrast agents. The results show how, depending on the input modalities, the models can have very different performances.
  arXiv  Detail & Related papers  (2024-07-15T16:38:59Z)
• Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models [2.8981737432963506]
  We propose a latent diffusion model capable of acquisition time-conditioned image synthesis of DCE-MRI temporal sequences. Our results demonstrate our method's ability to generate realistic multi-sequence fat-saturated breast DCE-MRI.
  arXiv  Detail & Related papers  (2024-03-20T18:01:57Z)
• Gadolinium dose reduction for brain MRI using conditional deep learning [66.99830668082234]
  Two main challenges for these approaches are the accurate prediction of contrast enhancement and the synthesis of realistic images. We address both challenges by utilizing the contrast signal encoded in the subtraction images of pre-contrast and post-contrast image pairs. We demonstrate the effectiveness of our approach on synthetic and real datasets using various scanners, field strengths, and contrast agents.
  arXiv  Detail & Related papers  (2024-03-06T08:35:29Z)
• Synthesis of Contrast-Enhanced Breast MRI Using Multi-b-Value DWI-based Hierarchical Fusion Network with Attention Mechanism [15.453470023481932]
  Contrast-enhanced MRI (CE-MRI) provides superior differentiation between tumors and invaded healthy tissue. The use of gadolinium-based contrast agents (GBCA) to obtain CE-MRI may be associated with nephrogenic systemic fibrosis and may lead to bioaccumulation in the brain. To reduce the use of contrast agents, diffusion-weighted imaging (DWI) is emerging as a key imaging technique.
  arXiv  Detail & Related papers  (2023-07-03T09:46:12Z)
• On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
  Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input. DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases. We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
  arXiv  Detail & Related papers  (2023-06-23T03:09:03Z)
• OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing [62.993663757843464]
  Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion. OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues. No standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
  arXiv  Detail & Related papers  (2022-06-17T08:11:26Z)
• Negligible effect of brain MRI data preprocessing for tumor segmentation [36.89606202543839]
  We conduct experiments on three publicly available datasets and evaluate the effect of different preprocessing steps in deep neural networks. Our results demonstrate that most popular standardization steps add no value to the network performance. We suggest that image intensity normalization approaches do not contribute to model accuracy because of the reduction of signal variance with image standardization.
  arXiv  Detail & Related papers  (2022-04-11T17:29:36Z)
• Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
  We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T08:41:17Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE) [50.65891535040752]
  We propose a diffusion encoding scheme, called Slice-Interleaved Diffusion. SIDE, that interleaves each diffusion-weighted (DW) image volume with slices encoded with different diffusion gradients. We also present a method based on deep learning for effective reconstruction of DW images from the highly slice-undersampled data.
  arXiv  Detail & Related papers  (2020-02-25T14:48:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.