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PLASMA-CYCLEGAN: PLASMA BIOMARKER-GUIDED MRI TO PET CROSS-MODALITY TRANSLATION USING CONDITIONAL CYCLEGAN
Yanxi Chen, Yi Su, Celine Dumitrascu, Kewei Chen, David Weidman, Richard J Caselli, Nicholas Ashton, Eric M Reiman et al.. Proceedings. IEEE International Symposium on Biomedical Imaging, 2025-05-12
Topic: Plasma and spinal-fluid biomarkers
Cross-modality translation between MRI and PET imaging is challenging due to the distinct mechanisms underlying these modalities. Blood-based biomarkers (BBBMs) are revolutionizing Alzheimer's disease (AD) detection by identifying patients and quantifying brain amyloid levels. However, the potential of BBBMs to enhance PET image synthesis remains unexplored. In this paper, we performed a thorough study on the effect of incorporating BBBM into deep generative models. By evaluating three widely used cross-modality translation models, we found that BBBMs integration consistently enhances the generative quality across all models. By visual inspection of the generated results, we observed that PET images generated by CycleGAN exhibit the best visual fidelity. Based on these findings, we propose Plasma-CycleGAN, a novel generative model based on CycleGAN, to synthesize PET images from MRI using BBBMs as conditions. This is the first approach to integrate BBBMs in conditional cross-modality translation between MRI and PET.
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APA 7th
Chen, Y., Su, Y., Dumitrascu, C., Chen, K., Weidman, D., Caselli, R. J., et al. (2025). PLASMA-CYCLEGAN: PLASMA BIOMARKER-GUIDED MRI TO PET CROSS-MODALITY TRANSLATION USING CONDITIONAL CYCLEGAN. Proceedings. IEEE International Symposium on Biomedical Imaging. https://doi.org/10.1109/isbi60581.2025.10980900
Vancouver
Chen Y, Su Y, Dumitrascu C, Chen K, Weidman D, Caselli RJ, et al. PLASMA-CYCLEGAN: PLASMA BIOMARKER-GUIDED MRI TO PET CROSS-MODALITY TRANSLATION USING CONDITIONAL CYCLEGAN. Proceedings. IEEE International Symposium on Biomedical Imaging. 2025. doi:10.1109/isbi60581.2025.10980900. PMID: 40822978.
BibTeX
@article{chen2025-40822978,
author = {Chen, Yanxi and Su, Yi and Dumitrascu, Celine and Chen, Kewei and Weidman, David and Caselli, Richard J and Ashton, Nicholas and Reiman, Eric M and others},
title = {{PLASMA-CYCLEGAN: PLASMA BIOMARKER-GUIDED MRI TO PET CROSS-MODALITY TRANSLATION USING CONDITIONAL CYCLEGAN}},
journal = {Proceedings. IEEE International Symposium on Biomedical Imaging},
year = {2025},
doi = {10.1109/isbi60581.2025.10980900},
pmid = {40822978},
url = {https://doi.org/10.1109/isbi60581.2025.10980900}
}
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Key findings
- By evaluating three widely used cross-modality translation models, we found that BBBMs integration consistently enhances the generative quality across all models.
- By visual inspection of the generated results, we observed that PET images generated by CycleGAN exhibit the best visual fidelity.
- Based on these findings, we propose Plasma-CycleGAN, a novel generative model based on CycleGAN, to synthesize PET images from MRI using BBBMs as conditions.
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Study details
- Evidence strengthLow
How this study scored 43/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record, multi-centre collaboration and free to read, so those were left out: it earned 24 of the 56 points we could judge it on, which is 43 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Proceedings. IEEE International Symposium on Biomedical Imaging) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40822978) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 24 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 24 of the 56 points we could judge it on is 43 out of 100. |
| Total | 43 / 100 | — |
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