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Generation of Structural MR Images from Amyloid PET: Application to MR-Less Quantification
Choi H, Lee DS, Alzheimer’s Disease Neuroimaging Initiative.. Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2017-12-07
Topic: Amyloid, tau and blood-test diagnosis
Generated MR images from florbetapir PET showed signal patterns that were visually similar to the real MR. The structural similarity index between real and generated MR was 0.91 ± 0.04. The mean absolute error of SUVR of cortical composite regions estimated by the generated MR-based method was 0.04 ± 0.03, which was significantly smaller than other MR-less methods (0.29 ± 0.12 for the normal PET template, 0.12 ± 0.07 for the multiatlas PET template, and 0.08 ± 0.06 for the PET segmentation-based methods). Bland-Altman plots revealed that the generated MR-based SUVR quantification was the closest to the SUVRs estimated by the real MR-based method. Structural MR images were successfully generated from amyloid PET images using deep generative networks. Generated MR images could be used as templates for accurate and precise amyloid quantification. This generative method might be used to generate multimodal images of various organs for further quantitative analyses.
Key findings
- Generated MR images from florbetapir PET showed signal patterns that were visually similar to the real MR.
- The structural similarity index between real and generated MR was 0.91 ± 0.04.
- The mean absolute error of SUVR of cortical composite regions estimated by the generated MR-based method was 0.04 ± 0.03, which was significantly smaller than other MR-less methods (0.29 ± 0.12 for the normal PET template, 0.12 ± 0.07 for the multiatlas PET template, and 0.08 ± 0.06 for the PET segmentation-based methods).
- Bland-Altman plots revealed that the generated MR-based SUVR quantification was the closest to the SUVRs estimated by the real MR-based method.
- Structural MR images were successfully generated from amyloid PET images using deep generative networks.
Why it matters
This study used deep generative networks to create structural MR images from amyloid PET scans, allowing accurate amyloid quantification without a real MR. It is a technical imaging method and does not test any treatment, cure or prevention.
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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.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of nuclear medicine : official publication, Society of Nuclear Medicine) |
| Journal's citation record | 2 / 4 | Journal of Nuclear Medicine: journal h-index 230 (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 | 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 | 2 / 5 | 2018 — within 10 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 | 5 / 7 | Cited 121 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 12.1 citations a year over 10 year(s) since 2018 |
| Impact for its field and year | 3 / 6 | Cited 7.8× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 2 / 4 | Cited 31 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Dong Soo Lee: h-index 68 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Seoul National University: institution h-index 1,053 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 29217736) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 43 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
Full text: free to read — PubMed Central lists this copy as free to read: the link above (checked 10 Oct 2026).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Choi, H., Lee, D. S., & Alzheimer’s Disease Neuroimaging Initiative. (2018). Generation of Structural MR Images from Amyloid PET: Application to MR-Less Quantification. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. https://doi.org/10.2967/jnumed.117.199414
Vancouver
Choi H, Lee DS, Alzheimer’s Disease Neuroimaging Initiative. Generation of Structural MR Images from Amyloid PET: Application to MR-Less Quantification. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2018. doi:10.2967/jnumed.117.199414. PMID: 29217736.
BibTeX
@article{choi2018-29217736,
author = {Choi, H. and Lee, D. S. and {Alzheimer’s Disease Neuroimaging Initiative}},
title = {{Generation of Structural MR Images from Amyloid PET: Application to MR-Less Quantification}},
journal = {Journal of nuclear medicine : official publication, Society of Nuclear Medicine},
year = {2018},
doi = {10.2967/jnumed.117.199414},
pmid = {29217736},
url = {https://doi.org/10.2967/jnumed.117.199414}
}
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