Bibliographic record
Abstract
Clinical MRI depends on a symbiosis between MR physics and clinical requirements. The imaging solutions are based on a balance between the "palette" of available image contrasts derived from nuclear spin physics and tissue biophysics, and clinical determinants such as the anticipated pathology and efficient use of imaging time. Imaging is therefore optimized to maximize diagnostic sensitivity and specificity through the development of protocols organized along the lines of major disease categories. In the other part of this two-part review, the primary determinants of image contrast, including T1, T2, and T2*, were highlighted. The development of pulse sequences designed to optimize each of these image contrasts was discussed and the impact of technological innovation (parallel imaging and high-field systems) on the manner in which these sequences could be modified to improve clinical efficacy was further emphasized. The scope of that discussion was broadened to include the application of: 1) water diffusion imaging used primarily for detection of pathologies that restrict the free movement of water in the tissues and for defining fiber tracts in the brain; 2) the intravenous administration of exogenous contrast agents (gadolinium-diethylene triamine pentaacetic acid [GdDTPA]) for assessment of blood-brain-barrier (BBB) defects and brain blood flow; and 3) MR spectroscopy (MRS) for assessment of brain metabolites. The goal of this part is to discuss how these acquisitions are combined into specific protocols that can effectively detect and characterize, or in keeping with our artistic analogy, "paint" each of the major diseases affecting the central nervous system (CNS). This work concludes with a discussion of image artifacts and pitfalls in image interpretation, which, in spite our best efforts to minimize or eliminate them, continue to occur. Much of the ensuing discussion is based on our own institutional experience. Protocols, therefore, do not necessarily match those from other institutions due to variability in clinical emphasis, MR instruments, and available software. An attempt was made to focus on basic clinical sequences that are available on most modern MR systems, with protocols employing generally accepted clinical imaging philosophies.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.209 | 0.250 |
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".