Magnetic Resonance Imaging and Computed Tomography of the Brain—50 Years of Innovation, With a Focus on the Future
Bibliographic record
Abstract
This review focuses specifically on the developments in brain imaging, as opposed to the spine, and specifically conventional, clinical, cross-sectional imaging, looking primarily at advances in magnetic resonance imaging (MRI) and computed tomography (CT). These fields are viewed from a perspective of landmark publications in the last 50 years and subsequently more in depth using sentinel publications from the last 5 years. It is also written from a personal perspective, with the authors having witnessed the evolution of both fields from their initial clinical introduction to their current state. Both CT and MRI have made tremendous advances during this time, regarding not only sensitivity and spatial resolution, but also in terms of the speed of image acquisition. Advances in CT in recent years have focused in part on reduced radiation dose, an important topic for the years to come. Magnetic resonance imaging has seen the development of a plethora of scan techniques, with marked superiority to CT in terms of tissue contrast due to the many parameters that can be assessed, and their intrinsic sensitivity. Future advances in MRI for clinical practice will likely focus both on new acquisition techniques that offer advances in speed and resolution, for example, simultaneous multislice imaging and data sparsity, and on standardization and further automation of image acquisition and analysis. Functional imaging techniques including specifically perfusion and functional magnetic resonance imaging will be further integrated into the workflow to provide pathophysiologic information that influence differential diagnosis, assist treatment decision and planning, and identify and follow treatment-related changes.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".