Leveraging Deep Learning to Enhance MRI for Brain Disorders
Bibliographic record
Abstract
Abstract The limited availability and high cost of 7 Tesla (7T) structural MRI hinder its widespread application despite its superior imaging quality. This study introduces a High Frequency-Generative Adversarial Network (HF-GAN) to predict three-dimensional 7T-equivalent (P7T) images from standard 3T structural MRI scans, offering a cost-effective alternative. HF-GAN was trained on paired 3T and 7T MRI data and validated on external datasets, including STRATIFY/ESTRA (N=671) and ADNI2 (N=643), covering psychiatric and neurodegenerative disorders. Results indicate that P7T images generally exhibit enhanced contrast and preservation of fine structural details comparable to 7T and better than 3T, including improved sensitivity in detecting disease-related differences in key brain regions such as the thalamus, caudate, putamen, and frontal cortical areas. The partial η 2 values revealed that P7T explained a higher proportion of variance compared to 3T in several comparisons, highlighting its improved sensitivity to disease-related structural changes. These findings demonstrate that HF-GAN effectively enhances 3T MRI data quality, providing a scalable solution for research and clinical applications in neurodegenerative and psychiatric disorders. Additional validations in brain and other organ systems are warranted to further advance clinical translation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".