Bibliographic record
Abstract
The principles of contrast mechanisms and fast pulse sequences underlying neurological and neuroradiological application of MRI are introduced in this part of a two-part review. In particular, the relaxation time constants T(1), T(2), and T(2) (*) are introduced, along with pulse sequences harnessing their contrast, spin-echo, fast spin echo (FSE), inversion recovery (IR), gradient recalled echo (GRE), and echo planar imaging (EPI). The use of gadolinium (Gd)-based contrast is discussed in both T(1)- and T(2) (*)-weighted sequences. Tradeoffs between speed and image quality are discussed, particularly in the context of long echo train sequences (FSE imaging and EPI). The influence of parallel imaging strategies is also discussed. T(2) (*) sensitivity is discussed as both a source of artifact as well as a contrast mechanism (perfusion imaging with contrast agents, susceptibility weighted imaging [SWI], and blood oxygenation level dependent [BOLD] imaging). Finally, the contrast mechanism of diffusion is introduced, as well as the concept of anisotropy. From these principles, the other part of this two-part review draws upon the pulse sequences and contrast mechanisms to design disease and indication-specific protocols for state-of-the-art clinical use.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.011 |
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".