MRA: Current Applications in Body Vascular Imaging
Bibliographic record
Abstract
Computed tomography (CT) angiography (CTA) and magnetic resonance (MR) angiography (MRA) are 2 modalities that have revolutionized the field of diagnostic vascular imaging. Conventional catheter angiography, which was once the gold standard, is now being replaced by CTA and MRA because of their lower cost and noninvasiveness. Advancements in cross-sectional imaging include higher spatial and temporal resolution, as well as the ability to construct three-dimensional (3D) images from volumetric data and to view vessels from multiple angles [1]. CTA is traditionally more widely used than MRA, mainly because of availability and greater CT expertise. Other advantages of CTA over MRA include faster acquisition times, higher spatial resolution, and utility in patients with contraindications to MR imaging, including certain aneurysm clips, cochlear implants, pacemakers, and claustrophobia [1,2]. Current uses of CTA are many, including perioperative imaging and planning of endovascular aneurysm repairs (EVAR) and imaging of the abdominal aorta and visceral vessels [3]. Disadvantages of CTA include the use of ionizing radiation and iodinated nephrotoxic contrast material, 2 factors that make MRA a more desirable modality. Using MR to delineate vascular anatomy has changed dramatically since first described in 1985 by Wedeen et al [4]. Technology continuously evolves and provides more advanced equipment and complex software, which is faster and provides more detailed information. MR sequences such as phase-contrast (PC) MRA (PC-MRA) and time-of-flight (TOF) MRA (TOF-MRA) provide reasonable depictions of the vascular anatomy without contrast. Research into nonnephrotoxic, gadolinium-based contrast agents has paved the way for contrast-enhanced MRA (CE-MRA), which today is widely used in clinical practice [5]. MRA is gaining popularity as applications increase image quality and decrease acquisition time. Dynamic MRA
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.017 |
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".