Leveraging Multi-Visit Information for Magnetic Resonance Image Reconstruction: Pilot Study on a Cohort of Glioblastoma Subjects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it