Magnetic Resonance Imaging and Computed Tomography of the Brain—50 Years of Innovation, With a Focus on the Future
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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