Leveraging Multi-Visit Information for Magnetic Resonance Image Reconstruction: Pilot Study on a Cohort of Glioblastoma Subjects
Bibliographic record
Abstract
Deep-learning-based Magnetic Resonance (MR) imaging models can reconstruct MR images from undersampled acquisitions, leading to expedited MR examinations. Nevertheless, most MR reconstruction methods do not consider multi-visit information (i.e., past exams) that is often available. In this work, we investigated whether multi-visit information could be used to improve MR image reconstruction. This pilot study used a challenging brain MR dataset from a cohort of glioblastoma patients whose brain images are expected to present significant changes in between exams. The results of the model that leverages multi-visit information were compared against a baseline that does not use that information (i.e., single-visit reconstruction). We evaluated the results quantitatively using structural similarity (SSIM) and peak signal-to-noise (pSNR) ratio. Compared to the baseline model, the model that leverages multi-visit information increased SSIM and pSNR by 9% and 6%, respectively. Despite the anatomical changes between pairs of past and present scans, visual assessment indicates that the multi-visit reconstruction is not incorrectly biased towards the previous scan.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".