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Fast and Accurate Amyloid Brain PET Quantification Without MRI Using Deep Neural Networks
Kang SK, Kim D, Shin SA, Kim YK, Choi H, Lee JS.. Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2022-11-03
Topic: Amyloid, tau and blood-test diagnosis
Quantification results using the proposed deep learning-based method showed stronger correlations with the FreeSurfer estimates than SPM-based SN using MRI did. For example, the slope, y -intercept, and R 2 values between SPM and FreeSurfer for the global cortex were 0.869, 0.113, and 0.946, respectively. In contrast, the slope, y -intercept, and R 2 values between the proposed deep learning-based method and FreeSurfer were 1.019, -0.016, and 0.986, respectively. The external validation study also demonstrated better performance for the proposed method without MR images than for SPM with MRI. In most brain regions, the proposed method outperformed SPM SN in terms of linear regression parameters and intraclass correlation coefficients. We evaluated a novel deep learning-based SN method that allows quantitative analysis of amyloid brain PET images without structural MRI. The quantification results using the proposed method showed a strong correlation with MRI-parcellation-based quantification using FreeSurfer for all clinical amyloid radiotracers. Therefore, the proposed method will be useful for investigating Alzheimer disease and related brain disorders using amyloid PET scans.
Key findings
- Quantification results using the proposed deep learning-based method showed stronger correlations with the FreeSurfer estimates than SPM-based SN using MRI did.
- For example, the slope, y -intercept, and R 2 values between SPM and FreeSurfer for the global cortex were 0.869, 0.113, and 0.946, respectively.
- In contrast, the slope, y -intercept, and R 2 values between the proposed deep learning-based method and FreeSurfer were 1.019, -0.016, and 0.986, respectively.
- The external validation study also demonstrated better performance for the proposed method without MR images than for SPM with MRI.
- In most brain regions, the proposed method outperformed SPM SN in terms of linear regression parameters and intraclass correlation coefficients.
Why it matters
A deep learning method quantified amyloid brain PET scans without needing an MRI, with results closely matching FreeSurfer-based estimates. This is a technical tool for analyzing scans and does not itself treat, cure or prevent Alzheimer disease.
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Study details
- Evidence strengthLow
How this study scored 44/100
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 | 3 / 5 | 2023 — within 6 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 | 3 / 7 | Cited 39 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 7.8 citations a year over 5 year(s) since 2023 |
| Impact for its field and year | 3 / 6 | Cited 3.8× 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 38 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Jae Sung Lee: h-index 52 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Seoul National University: institution h-index 1,053 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | PubMed indexes it as a multicentre study |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 36328490) |
| Free to read | 2 / 2 | Free to read |
| Total | 44 / 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
Kang, S. K., Kim, D., Shin, S. A., Kim, Y. K., Choi, H., & Lee, J. S. (2023). Fast and Accurate Amyloid Brain PET Quantification Without MRI Using Deep Neural Networks. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. https://doi.org/10.2967/jnumed.122.264414
Vancouver
Kang SK, Kim D, Shin SA, Kim YK, Choi H, Lee JS. Fast and Accurate Amyloid Brain PET Quantification Without MRI Using Deep Neural Networks. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2023. doi:10.2967/jnumed.122.264414. PMID: 36328490.
BibTeX
@article{kang2023-36328490,
author = {Kang, S. K. and Kim, D. and Shin, S. A. and Kim, Y. K. and Choi, H. and Lee, J. S.},
title = {{Fast and Accurate Amyloid Brain PET Quantification Without MRI Using Deep Neural Networks}},
journal = {Journal of nuclear medicine : official publication, Society of Nuclear Medicine},
year = {2023},
doi = {10.2967/jnumed.122.264414},
pmid = {36328490},
url = {https://doi.org/10.2967/jnumed.122.264414}
}
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