Bibliographic record
Abstract
Magnetic Resonance Imaging (MRI) is becoming a widely used method for non-invasively imaging biological tissues. The MRI technique, however, is slower and much costlier than its competing medical imaging technique, computed tomography (CT), which uses ionizing radiation. In the last 10 years, many improvements in parallel MRI (pMRI) techniques have been developed for fast acquisition of MRI data for making MRI a versatile research as well as clinical diagnostic tool. These pMRI techniques have the disadvantage of reducing the signal-to-noise ratio (SNR) and thus the quality of the reconstructed image. MRI data acquisition is an extremely complex process where radio frequency pulse sequences in a magnetic field allow recording of changes in the magnetic field from the protons in biological tissues in the Fourier domain known as k-space. A hybrid wavelet and Fourier encoding of the k-space has been shown to be successful in reconstructing high quality sparse images. Based on the compressibility of images in the hybrid wavelet domain, an average wavelet coefficient significance map can be generated and fast acquisition of any MRI data may be accomplished when combined with an appropriate pMRI technique. The feasibility of generating an average significance map from 20 brains from McGill University Simulated Brain Database has been demonstrated and validated.
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.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".