Haowen, Pang and Xu, Siyao and Zhang, Xinru and An, Fengping and Zhang, Xiaofeng and Wang, Ying and Liu, Fujun and Yan, Tianyi and Marais, Patrick and Qian, Yinfeng and Liu, Yaou and Ye, Chuyang (2026) DRCAM: Dose Reduction via Contrast Amplification Modeling for brain contrast-enhanced magnetic resonance images, Biomedical Signal Processing and Control, 113, 109074, Science Direct.
Full text not available from this repository. (Use alternate locations listed below)Abstract
Contrast agent dose reduction is desired for the acquisition of contrast-enhanced magnetic resonance images (CEMRIs), but it can degrade image contrast and increase the difficulty of image interpretation. To address the problem, previous works synthesize virtual full-dose CEMRIs (FD-CEMRIs) with desired image contrast based on low-dose CEMRIs (LD-CEMRIs) and non-contrast magnetic resonance images (NCMRIs). However, these methods use generic image synthesis models and overlook the signal relationship between NCMRIs, LD-CEMRIs, and FD-CEMRIs, which can serve as valuable prior knowledge for the synthesis problem. In this work, we propose DRCAM (Dose Reduction via Contrast Amplification Modeling), which integrates signal relationships into the synthesis of FD-CEMRIs from LD-CEMRIs and NCMRIs. Assuming that the difference between the FD-CEMRI and LD-CEMRI/NCMRI is the amplified difference between the LD-CEMRI and NCMRI, we convert the original synthesis problem into amplification ratio estimation. To suppress image noise that is also magnified in the amplification, our synthesis model comprises a dual-stream contrast amplification modeling module followed by a denoising component. The dual-stream amplification obtains two versions of initial FD-CEMRIs by amplifying the signal difference based on the LD-CEMRI and NCMRI separately. They are fused to reduce the impact of noise and fed into the denoising component to produce the final result. Moreover, we propose to indicate potential enhanced regions as an auxiliary input via intensity alignment between the NCMRI and LD-CEMRI. DRCAM was validated on three datasets, and experimental results show that DRCAM improves the quality of synthesized FD-CEMRIs compared to state-of-the-art methods.
| Item Type: | Journal article (paginated) |
|---|---|
| Additional Information: | https://doi.org/10.1016/j.bspc.2025.109074 |
| Subjects: | Computing methodologies > Artificial intelligence > Computer vision Computing methodologies > Machine learning |
| Date Deposited: | 08 Sep 2026 07:45 |
| Last Modified: | 08 Sep 2026 07:45 |
| URI: | https://pubs.cs.uct.ac.za/id/eprint/1799 |
Actions (login required)
 |
View Item |