Approach to Brain Magnetic Resonance Imaging for Non-Radiologists
Bibliographic record
Abstract
The goal of this review is to provide a guide to magnetic resonance imaging (MRI) reading for non-radiologists. A thorough literature search was conducted using the keywords “MRI”, “CT”, “Non-radiologist” and “MRI interpretation” to develop an approach to MRI reading for non-radiologists. Common indications for a brain MRI include workup of an intracranial tumor, chronic headache, seizure disorder, and confirmation of a stroke. When assessing for an intracranial tumor, MRI is the preferred diagnostic modality. Computed tomography (CT) has much lower resolution and is typically reserved for the emergency setting. T1 weighted images provide anatomically relevant images of the brain parenchyma that will be familiar to non-radiologists. In contrast to T1 weighted images, fluid is bright in T2 and white matter will appear darker than gray matter. Fluid attenuation inversion recovery (FLAIR) is most sensitive for edema and parenchymal abnormalities like a low-grade glioma. The main purpose of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) sequences are to visualize acute ischemic stroke. Although non-radiologists generally have a greater exposure to head CT images, the same foundational principles of CT head interpretation can apply to brain MRI reading. Benefits of brain imaging by MRI includes obtaining a multi-planar assessment of the brain, highly detailed images of the brain, and using different MRI sequences to assess for different pathology. J Neurol Res. 2020;10(5):173-176 doi: https://doi.org/10.14740/jnr628
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.015 |
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".