T-CACE: A Time-Conditioned Autoregressive Contrast Enhancement Multi-Task Framework for Contrast-Free Liver MRI Synthesis, Segmentation, and Diagnosis

• URL: http://arxiv.org/abs/2508.09919v1
• Date: Wed, 13 Aug 2025 16:14:14 GMT
• Title: T-CACE: A Time-Conditioned Autoregressive Contrast Enhancement Multi-Task Framework for Contrast-Free Liver MRI Synthesis, Segmentation, and Diagnosis
• Authors: Xiaojiao Xiao, Jianfeng Zhao, Qinmin Vivian Hu, Guanghui Wang,
• Abstract summary: We propose a Time-Conditioned Autoregressive Contrast Enhancement (T-CACE) framework for synthesizing contrast-enhanced MRI (CEMRI) directly from non-contrast MRI (NCMRI)<n>T-CACE introduces three core innovations: a conditional token encoding (CTE) mechanism that unifies anatomical priors and temporal phase information into latent representations; and a dynamic time-aware attention mask (DTAM) that adaptively modulates inter-phase information flow.<n>Experiments on two independent liver MRI datasets demonstrate that T-CACE outperforms state-of-the-art methods in image synthesis, segmentation, and lesion
• Score: 5.9273237904788045
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Magnetic resonance imaging (MRI) is a leading modality for the diagnosis of liver cancer, significantly improving the classification of the lesion and patient outcomes. However, traditional MRI faces challenges including risks from contrast agent (CA) administration, time-consuming manual assessment, and limited annotated datasets. To address these limitations, we propose a Time-Conditioned Autoregressive Contrast Enhancement (T-CACE) framework for synthesizing multi-phase contrast-enhanced MRI (CEMRI) directly from non-contrast MRI (NCMRI). T-CACE introduces three core innovations: a conditional token encoding (CTE) mechanism that unifies anatomical priors and temporal phase information into latent representations; and a dynamic time-aware attention mask (DTAM) that adaptively modulates inter-phase information flow using a Gaussian-decayed attention mechanism, ensuring smooth and physiologically plausible transitions across phases. Furthermore, a constraint for temporal classification consistency (TCC) aligns the lesion classification output with the evolution of the physiological signal, further enhancing diagnostic reliability. Extensive experiments on two independent liver MRI datasets demonstrate that T-CACE outperforms state-of-the-art methods in image synthesis, segmentation, and lesion classification. This framework offers a clinically relevant and efficient alternative to traditional contrast-enhanced imaging, improving safety, diagnostic efficiency, and reliability for the assessment of liver lesion. The implementation of T-CACE is publicly available at: https://github.com/xiaojiao929/T-CACE.

Related papers

• Structure-constrained Language-informed Diffusion Model for Unpaired Low-dose Computed Tomography Angiography Reconstruction [72.80209358480424]
  overdose of iodinated contrast media (ICM) can cause kidney damage and life-threatening allergic reactions.<n>Deep learning methods can generate CT images of normal-dose ICM from low-dose ICM, reducing the required dose.<n>We propose a Structure-constrained Language-informed Diffusion Model (SLDM) that integrates structural synergy and spatial intelligence.
  arXiv  Detail & Related papers  (2026-01-28T06:54:06Z)
• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• SmaRT: Style-Modulated Robust Test-Time Adaptation for Cross-Domain Brain Tumor Segmentation in MRI [15.54859394044201]
  We propose SmaRT, a style-modulated robust test-time adaptation framework.<n>SmaRT integrates style-aware augmentation to mitigate appearance discrepancies, a dual-branch momentum strategy for stable pseudo-label refinement, and structural priors enforcing consistency, integrity, and connectivity.<n>In evaluations on sub-Saharan Africa and pediatric glioma datasets, SmaRT consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-09-22T15:50:59Z)
• LGE-Guided Cross-Modality Contrastive Learning for Gadolinium-Free Cardiomyopathy Screening in Cine CMR [51.11296719862485]
  We propose a Contrastive Learning and Cross-Modal alignment framework for gadolinium-free cardiomyopathy screening using cine CMR sequences.<n>By aligning the latent spaces of cine CMR and Late Gadolinium Enhancement (LGE) sequences, our model encodes fibrosis-specific pathology into cine CMR embeddings.
  arXiv  Detail & Related papers  (2025-08-23T07:21:23Z)
• CLAIM: Clinically-Guided LGE Augmentation for Realistic and Diverse Myocardial Scar Synthesis and Segmentation [3.052913696182197]
  We introduce CLAIM: textbfClinically-Guided textbfLGE textbfAugmentation for Realtextbfiiyocardial Scar Synthesis and framework.<n>At its core is the SMILE module, which conditions a diffusion-based generator on the clinically adopted AHA 17-segment model to synthesize images with anatomically consistent and spatially diverse scar patterns.<n> Experimental results show that CLAIM produces anatomically coherent scar patterns and achieves higher Dice similarity with real scar distributions compared to baseline models.
  arXiv  Detail & Related papers  (2025-06-18T15:21:34Z)
• HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models [33.7054351451505]
  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.
  arXiv  Detail & Related papers  (2025-04-25T15:01:09Z)
• Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networks [2.4634168876565177]
  We present a method for virtual contrast enhancement in breast MRI, offering a promising non-invasive alternative to traditional contrast agent-based DCE-MRI acquisition.<n>Using a conditional generative adversarial network, we predict DCE-MRI images, including jointly-generated sequences of multiple corresponding DCE-MRI timepoints.<n>Our approach demonstrates promising results in generating realistic and useful DCE-MRI sequences, highlighting the potential of virtual contrast enhancement for improving breast cancer diagnosis and treatment.
  arXiv  Detail & Related papers  (2024-09-27T16:08:52Z)
• 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)
• 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)
• Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis [44.45598796591008]
  Brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment analysis. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model.
  arXiv  Detail & Related papers  (2023-05-18T06:54:56Z)
• Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications [22.410543483471915]
  Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central nervous system. Traditionally, MS lesions have been manually annotated on 2D MRI slices. Deep learning techniques have achieved remarkable breakthroughs in the MS lesion segmentation task.
  arXiv  Detail & Related papers  (2021-04-20T15:08:51Z)
• Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation [48.504790189796836]
  We present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe) We propose a versatile framework that fuses appearance based semi-supervision, mask based adversarial domain adaptation, and pseudo-labeling. CHASe can further improve pathological liver mask Dice-Sorensen coefficients by ranges of $4.2% sim 9.4%$.
  arXiv  Detail & Related papers  (2020-05-27T06:58:39Z)
• TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks [43.95869213955351]
  We propose a Multi-Stage Temporal Convolutional Network (MS-TCN) that performs hierarchical prediction refinement for surgical phase recognition. Our method is thoroughly evaluated on two datasets of laparoscopic cholecystectomy videos with and without the use of additional surgical tool information.
  arXiv  Detail & Related papers  (2020-03-24T10:12:30Z)
• Detecting Pancreatic Ductal Adenocarcinoma in Multi-phase CT Scans via Alignment Ensemble [77.5625174267105]
  Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers among the population. Multiple phases provide more information than single phase, but they are unaligned and inhomogeneous in texture. We suggest an ensemble of all these alignments as a promising way to boost the performance of PDAC detection.
  arXiv  Detail & Related papers  (2020-03-18T19:06:27Z)

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.