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Cyclic 2.5D Perceptual Loss for Cross‐Modal 3D Medical Image Synthesis: T1w MRI to Tau PET
Junho Moon, Symac Kim, Haejun Chung, Ikbeom Jang, Alzheimer's Disease Neuroimaging Initiative. Human Brain Mapping, 2026-04-01
Topic: Brain proteins, microglia and lab models
ABSTRACT Positron emission tomography (PET) provides an in vivo molecular marker for various diseases, including Alzheimer's disease and related dementias (ADRD). PET has become increasingly integrated into diagnostic decision‐making, disease staging, and clinical trial enrichment. However, its widespread use remains constrained by high costs, government regulations, and the invasiveness of radiotracer injection. Modern diagnostic frameworks emphasize the importance of multimodal biomarker assessment, such as the “amyloid/tau/neurodegeneration” (A/T/N) framework for Alzheimer's disease; however, they are constrained by these barriers. Medical image synthesis or translation offers a potential solution by enabling the reconstruction of unavailable modalities. The clinical utility of PET depends on accurately capturing regional uptake patterns rather than exact voxel‐wise intensities, motivating the use of perceptual loss functions to assess higher‐level semantic features in generative models. While 2D, 3D, and 2.5D perceptual losses are utilized in 3D synthesis, each encounters challenges, including limited volumetric context, the scarcity of pretrained 3D models, and difficulty balancing optimization across anatomical planes. In this work, we address cross‐modal synthesis of tau PET from structural magnetic resonance imaging (MRI), generating 3D pseudo‐[ 18 F]flortaucipir standardized uptake value ratio (SUVR) maps from 3D T1‐weighted MR images. We propose a cyclic 2.5D perceptual loss that cyclically optimizes the axial, coronal, and sagittal planes over training phases, thereby enhancing volumetric consistency. Furthermore, we standardize PET SUVRs by scanner manufacturer, reducing inter‐manufacturer variability and better preserving high‐uptake regions. We evaluate the proposed approach on cohorts spanning the ADRD spectrum using data from the Alzheimer's Disease Neuroimaging Initiative and the Standardized Centralized Alzheimer's Disease and Related Dementias Neuroimaging cohort. Our approach is broadly applicable across various generative frameworks and achieves high quantitative and qualitative performance on diverse architectures, including U‐Net, UNETR, SwinUNETR, CycleGAN, and Pix2Pix. Notably, it achieves better agreement between synthesized SUVRs and measured PET scans in key brain regions relevant to Alzheimer‐type tau pathology. The code is publicly available at https://github.com/labhai/Cyclic‐2.5D‐Perceptual‐Loss .
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Moon, J., Kim, S., Chung, H., Jang, I., & Alzheimer's Disease Neuroimaging Initiative. (2026). Cyclic 2.5D Perceptual Loss for Cross‐Modal 3D Medical Image Synthesis: T1w MRI to Tau PET. Human Brain Mapping. https://doi.org/10.1002/hbm.70508
Vancouver
Moon J, Kim S, Chung H, Jang I, Alzheimer's Disease Neuroimaging Initiative. Cyclic 2.5D Perceptual Loss for Cross‐Modal 3D Medical Image Synthesis: T1w MRI to Tau PET. Human Brain Mapping. 2026. doi:10.1002/hbm.70508. PMID: 41968275.
BibTeX
@article{moon2026-41968275,
author = {Moon, Junho and Kim, Symac and Chung, Haejun and Jang, Ikbeom and {Alzheimer's Disease Neuroimaging Initiative}},
title = {{Cyclic 2.5D Perceptual Loss for Cross‐Modal 3D Medical Image Synthesis: T1w MRI to Tau PET}},
journal = {Human Brain Mapping},
year = {2026},
doi = {10.1002/hbm.70508},
pmid = {41968275},
url = {https://doi.org/10.1002/hbm.70508}
}
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Key findings
- Furthermore, we standardize PET SUVRs by scanner manufacturer, reducing inter‐manufacturer variability and better preserving high‐uptake regions.
- We evaluate the proposed approach on cohorts spanning the ADRD spectrum using data from the Alzheimer's Disease Neuroimaging Initiative and the Standardized Centralized Alzheimer's Disease and Related Dementias Neuroimaging cohort.
- Our approach is broadly applicable across various generative frameworks and achieves high quantitative and qualitative performance on diverse architectures, including U‐Net, UNETR, SwinUNETR, CycleGAN, and Pix2Pix.
- Notably, it achieves better agreement between synthesized SUVRs and measured PET scans in key brain regions relevant to Alzheimer‐type tau pathology.
- The code is publicly available at https://github.com/labhai/Cyclic‐2.5D‐Perceptual‐Loss .
Why it matters
This study developed a method to generate synthetic tau PET images from MRI scans using deep learning, which could help when PET is unavailable. It is a technical image synthesis study and does not test any treatment, cure, or prevention.
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Study details
- Evidence strengthModerate
How this study scored 34/100
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 senior author's track record, so that was left out: it earned 33 of the 96 points we could judge it on, which is 34 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Human Brain Mapping) |
| Journal's citation record | 3 / 4 | Human Brain Mapping: journal h-index 271 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 5 / 5 | 2026 — within 1 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 | 0 / 7 | Cited 0 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.0 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | 0 / 6 | Cited 0.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | 0 / 3 | Hanyang University: institution h-index 548 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41968275) |
| Free to read | 2 / 2 | Free to read |
| Points | 33 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 33 of the 96 points we could judge it on is 34 out of 100. |
| Total | 34 / 100 | — |
What each of these 20 components measures, and what this score is not →
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