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Bidirectional Mapping of Brain MRI and PET With 3D Reversible GAN for the Diagnosis of Alzheimer's Disease
Lin W, Lin W, Chen G, Zhang H, Gao Q, Huang Y, Tong T, Du M et al.. Frontiers in neuroscience, 2021-04-09
Topic: Brain imaging, connectivity and assessment
Combining multi-modality data for brain disease diagnosis such as Alzheimer's disease (AD) commonly leads to improved performance than those using a single modality. However, it is still challenging to train a multi-modality model since it is difficult in clinical practice to obtain complete data that includes all modality data. Generally speaking, it is difficult to obtain both magnetic resonance images (MRI) and positron emission tomography (PET) images of a single patient. PET is expensive and requires the injection of radioactive substances into the patient's body, while MR images are cheaper, safer, and more widely used in practice. Discarding samples without PET data is a common method in previous studies, but the reduction in the number of samples will result in a decrease in model performance. To take advantage of multi-modal complementary information, we first adopt the Reversible Generative Adversarial Network (RevGAN) model to reconstruct the missing data. After that, a 3D convolutional neural network (CNN) classification model with multi-modality input was proposed to perform AD diagnosis. We have evaluated our method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and compared the performance of the proposed method with those using state-of-the-art methods. The experimental results show that the structural and functional information of brain tissue can be mapped well and that the image synthesized by our method is close to the real image. In addition, the use of synthetic data is beneficial for the diagnosis and prediction of Alzheimer's disease, demonstrating the effectiveness of the proposed framework.
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APA 7th
Lin, W., Chen, G., Zhang, H., Gao, Q., Huang, Y., Tong, T., et al. (2021). Bidirectional Mapping of Brain MRI and PET With 3D Reversible GAN for the Diagnosis of Alzheimer's Disease. Frontiers in neuroscience. https://doi.org/10.3389/fnins.2021.646013
Vancouver
Lin W, Chen G, Zhang H, Gao Q, Huang Y, Tong T, et al. Bidirectional Mapping of Brain MRI and PET With 3D Reversible GAN for the Diagnosis of Alzheimer's Disease. Frontiers in neuroscience. 2021. doi:10.3389/fnins.2021.646013. PMID: 33935634.
BibTeX
@article{lin2021-33935634,
author = {Lin, W. and Chen, G. and Zhang, H. and Gao, Q. and Huang, Y. and Tong, T. and Du, M. and others},
title = {{Bidirectional Mapping of Brain MRI and PET With 3D Reversible GAN for the Diagnosis of Alzheimer's Disease}},
journal = {Frontiers in neuroscience},
year = {2021},
doi = {10.3389/fnins.2021.646013},
pmid = {33935634},
url = {https://doi.org/10.3389/fnins.2021.646013}
}
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Key findings
- Discarding samples without PET data is a common method in previous studies, but the reduction in the number of samples will result in a decrease in model performance.
- The experimental results show that the structural and functional information of brain tissue can be mapped well and that the image synthesized by our method is close to the real image.
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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 randomized / 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 and multicenter collaboration, so those were left out: it earned 25 of the 58 points we could judge it on, which is 43 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Frontiers in neuroscience) |
| 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 | 3 / 5 | 2021 — 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) |
| Randomized / blinded | 0 / 2 | No randomization 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 |
| Multicenter collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 33935634) |
| Free to read | 2 / 2 | Free to read |
| Points | 25 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 25 of the 58 points we could judge it on is 43 out of 100. |
| Total | 43 / 100 | — |
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