Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models

• URL: http://arxiv.org/abs/2403.13890v3
• Date: Wed, 17 Jul 2024 16:04:45 GMT
• Title: Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models
• Authors: Richard Osuala, Daniel M. Lang, Preeti Verma, Smriti Joshi, Apostolia Tsirikoglou, Grzegorz Skorupko, Kaisar Kushibar, Lidia Garrucho, Walter H. L. Pinaya, Oliver Diaz, Julia A. Schnabel, Karim Lekadir,
• Abstract summary: 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.
• Score: 2.8981737432963506
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrast agents in dynamic contrast enhanced magnetic resonance imaging allow to localize tumors and observe their contrast kinetics, which is essential for cancer characterization and respective treatment decision-making. However, contrast agent administration is not only associated with adverse health risks, but also restricted for patients during pregnancy, and for those with kidney malfunction, or other adverse reactions. With contrast uptake as key biomarker for lesion malignancy, cancer recurrence risk, and treatment response, it becomes pivotal to reduce the dependency on intravenous contrast agent administration. To this end, we propose a multi-conditional latent diffusion model capable of acquisition time-conditioned image synthesis of DCE-MRI temporal sequences. To evaluate medical image synthesis, we additionally propose and validate the Fr\'echet radiomics distance as an image quality measure based on biomarker variability between synthetic and real imaging data. Our results demonstrate our method's ability to generate realistic multi-sequence fat-saturated breast DCE-MRI and uncover the emerging potential of deep learning based contrast kinetics simulation. We publicly share our accessible codebase at https://github.com/RichardObi/ccnet and provide a user-friendly library for Fr\'echet radiomics distance calculation at https://pypi.org/project/frd-score.

Related papers

• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  Deep neural networks have shown great potential for reconstructing high-fidelity images from undersampled measurements. Our model is based on neural operators, a discretization-agnostic architecture. Our inference speed is also 1,400x faster than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI Synthesis [3.3966430276631208]
  Conditional Autoregressive Vision Model improves synthesis of contrast-enhanced brain tumor MRI. Deep learning methods have been applied to synthesize virtual contrast-enhanced MRI scans from non-contrast images. Inspired by the resemblance between the gradual dose increase and the Chain-of-Thought approach in natural language processing, CAVM uses an autoregressive strategy.
  arXiv  Detail & Related papers  (2024-06-23T10:50:22Z)
• 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)
• Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation [0.9722528000969453]
  This study explores the feasibility of producing synthetic contrast enhancements by translating pre-contrast T1-weighted fat-saturated breast MRI to their corresponding first DCE-MRI sequence using a generative adversarial network (GAN) We assess the generated DCE-MRI data using quantitative image quality metrics and apply them to the downstream task of 3D breast tumour segmentation. Our results highlight the potential of post-contrast DCE-MRI synthesis in enhancing the robustness of breast tumour segmentation models via data augmentation.
  arXiv  Detail & Related papers  (2023-11-17T21:48:41Z)
• 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)
• Faithful Synthesis of Low-dose Contrast-enhanced Brain MRI Scans using Noise-preserving Conditional GANs [102.47542231659521]
  Gadolinium-based contrast agents (GBCA) are indispensable in Magnetic Resonance Imaging (MRI) for diagnosing various diseases. GBCAs are expensive and may accumulate in patients with potential side effects. It is unclear to which extent the GBCA dose can be reduced while preserving the diagnostic value.
  arXiv  Detail & Related papers  (2023-06-26T13:19:37Z)
• Multi-view Contrastive Learning with Additive Margin for Adaptive Nasopharyngeal Carcinoma Radiotherapy Prediction [7.303184467211488]
  We propose a supervised multi-view contrastive learning method with an additive margin. For each patient, four medical images are considered to form multi-view positive pairs. In addition, the embedding space is learned by means of contrastive learning.
  arXiv  Detail & Related papers  (2022-10-27T06:21:24Z)
• 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)
• CyTran: A Cycle-Consistent Transformer with Multi-Level Consistency for Non-Contrast to Contrast CT Translation [56.622832383316215]
  We propose a novel approach to translate unpaired contrast computed tomography (CT) scans to non-contrast CT scans. Our approach is based on cycle-consistent generative adversarial convolutional transformers, for short, CyTran. Our empirical results show that CyTran outperforms all competing methods.
  arXiv  Detail & Related papers  (2021-10-12T23:25:03Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)

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.