Multi-granularity Adversarial Generation Integrated Consistency Representation for Chest Low-Contrast-Enhanced CT Synthesis
Abstract
Contrast-enhanced CT (CECT) is essential for clinical evaluation of vessel structures and function. However, high contrast agent dose increases the risk of renal injury. Reducing contrast agent dose decreases the contrast between vessels and surrounding tissues, which complicates diagnosis. Despite their potential in CECT synthesis, existing methods often suffer from edge unclarity, contrast anomalies, and texture distortion, limiting their clinical applicability. This paper proposes a novel Multi-granularity Adversarial Generation Integrated Consistency Representation (MAGIC) for high-quality synthesis from low-contrast-enhanced CT to clinical usable CECT. MAGIC addresses current problems through four innovations: 1) Multi-Granularity Refined Booster (MRB) introduces contextual refinement and cross granularity boosting mechanisms for mining the multi-granularity contextual information to enhance feature representation capability, thus improving tissue edge clarity. 2) Supervised Contrast Enhancement Module (SCEM) imbues MAGIC with the ability to enhance tissue contrast, which leverages supervised images to adaptively adjust the contrast information of soft tissue structures and vessels, effectively overcoming the challenge of contrast anomalies. 3) Hierarchical Harmonized Consistency Representation (HHCR) utilizes domain consistency to construct a novel auxiliary loss for harmonizing the semantic and content relationships of multi-level hierarchical features to improve tissue texture performance, ensuring accurate restoration of real textures. 4) Dual-path Dynamic Collaborative Discriminator (DDCD) is designed with complementary strategies and injects content priors to dynamically collaborate the discrimination process, thereby comprehensively evaluating the fidelity of the synthesized results. Qualitative and quantitative results demonstrate that MAGIC significantly outperforms existing methods in edge clarity, image contrast, and texture restoration, underscoring its substantial clinical potential.
Related
- MSPRP: multi-granularity symptom-aware probabilistic representation prompting for target-domain-free plant disease detection
- Concept-aware contrastive representation learning for cross-translation semantic consistency modeling of the Analects
- What to randomize matters: a controlled study of volume, axis and representation in low-data molecular generation
- PREpiBind: Protein Representation-integrated Epitope--MHC Class II Binding Prediction
- snpXplorer: haplotype-block representation of GWAS signals for integrated variant and cross-trait interpretation
BibTeX
@article{zhao2026multigranularity,
title = {{Multi-granularity Adversarial Generation Integrated Consistency Representation for Chest Low-Contrast-Enhanced CT Synthesis}},
author = {Lin Zhao and Shangwen Yang and Dianlin Hu and Zhan Wu and Huazhong Shu and Chunfeng Yang and Jean-Louis Coatrieux and Yang Chen},
journal = {IEEE Transactions on Medical Imaging},
year = {2026},
month = sep,
doi = {10.1109/tmi.2026.3677497},
url = {https://hal.science/hal-05585579v1},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Multi-granularity Adversarial Generation Integrated Consistency Representation for Chest Low-Contrast-Enhanced CT Synthesis
AU - Lin Zhao
AU - Shangwen Yang
AU - Dianlin Hu
AU - Zhan Wu
AU - Huazhong Shu
AU - Chunfeng Yang
AU - Jean-Louis Coatrieux
AU - Yang Chen
PY - 2026
DA - 2026/09/07
JO - IEEE Transactions on Medical Imaging
DO - 10.1109/tmi.2026.3677497
UR - https://hal.science/hal-05585579v1
AB - Contrast-enhanced CT (CECT) is essential for clinical evaluation of vessel structures and function. However, high contrast agent dose increases the risk of renal injury. Reducing contrast agent dose decreases the contrast between vessels and surrounding tissues, which complicates diagnosis. Despite their potential in CECT synthesis, existing methods often suffer from edge unclarity, contrast anomalies, and texture distortion, limiting their clinical applicability. This paper proposes a novel Multi-granularity Adversarial Generation Integrated Consistency Representation (MAGIC) for high-quality synthesis from low-contrast-enhanced CT to clinical usable CECT. MAGIC addresses current problems through four innovations: 1) Multi-Granularity Refined Booster (MRB) introduces contextual refinement and cross granularity boosting mechanisms for mining the multi-granularity contextual information to enhance feature representation capability, thus improving tissue edge clarity. 2) Supervised Contrast Enhancement Module (SCEM) imbues MAGIC with the ability to enhance tissue contrast, which leverages supervised images to adaptively adjust the contrast information of soft tissue structures and vessels, effectively overcoming the challenge of contrast anomalies. 3) Hierarchical Harmonized Consistency Representation (HHCR) utilizes domain consistency to construct a novel auxiliary loss for harmonizing the semantic and content relationships of multi-level hierarchical features to improve tissue texture performance, ensuring accurate restoration of real textures. 4) Dual-path Dynamic Collaborative Discriminator (DDCD) is designed with complementary strategies and injects content priors to dynamically collaborate the discrimination process, thereby comprehensively evaluating the fidelity of the synthesized results. Qualitative and quantitative results demonstrate that MAGIC significantly outperforms existing methods in edge clarity, image contrast, and texture restoration, underscoring its substantial clinical potential.
SN - 0278-0062
ER -