Pulsatile motion effects on 3D magnetic resonance angiography: Implications for evaluating carotid artery stenoses
Bibliographic record
Abstract
In-plane carotid artery motion during a 3D MR angiography (MRA) scan can significantly degrade the resulting image resolution. This study characterizes the effect of cardiac pulsatility on 3D contrast-enhanced (CE) MRA with elliptical centric acquisitions using a point-spread function (PSF) analysis. Internal carotid artery (ICA) motion was collected from volunteers and patients using both MR and ultrasound (US) scans. After measuring the carotid artery motion displacement, a simulation was performed which calculated the blurring effects for three different protocols: nongated and two different cardiac gating schemes. The motion sensitivity of each protocol was evaluated for different spatial resolutions. The selection of optimal imaging parameters for a given scan time was investigated. The final results showed that cardiac-gated acquisitions only over a limited region of k-space high spatial frequencies are more time-efficient than cardiac gating for the entire k-space, as it allows for higher resolutions to be achieved and for capturing the arterial phase with low spatial frequencies. Selecting the optimal gating parameters depends directly on the motion characteristics of each individual. Our initial clinical experience is presented, and the need for a real-time tool that characterizes motion behavior for each individual as a prescan protocol is discussed.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".