Use of blood pool agents with steady‐state MRI to assess the vascular system
Bibliographic record
Abstract
Over the past two decades there have been significant advances in the use of magnetic resonance imaging (MRI) to assess the vascular system. New imaging sequences and improvements in magnet design have enabled the creation of higher spatial resolution images. MRI is now a viable alternative imaging modality when compared to both invasive angiography and computed tomographic angiography. The use of blood pool agents has further facilitated the use of MR angiography (MRA); their high molecular weight allows for lower doses of contrast medium administration while their prolonged presence in the blood stream allows for repeated high-quality volumetric imaging of both the arterial and venous circulation. As such, MRA is now no longer constrained by the tight windows for first-pass arterial and venous enhancement, which has resulted in the ability to assess and diagnose a large range of vascular pathologies in both arterial and venous systems. The intent of this review is to highlight MRI findings in common vascular pathologies including peripheral arterial disease (PAD), abnormalities of the abdominal aortic branches, postendovascular aortic aneurysm repair (EVAR) endoleak assessment, popliteal artery entrapment syndrome (PAES), deep venous thrombosis (DVT), vascular thoracic outlet syndrome (TOS), and vascular malformations. In addition, the latest MRI techniques currently used to optimally assess each of these pathologies will be discussed. LEVEL OF EVIDENCE: 5 Technical Efficacy: Stage 3 J. MAGN. RESON. IMAGING 2017;45:1559-1572.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".