Optimizing contrast agent concentration and spoiled gradient echo pulse sequence parameters for catheter visualization in MR‐guided interventional procedures: An analytic solution
Bibliographic record
Abstract
PURPOSE: A critical requirement of MR-guided interventions is the visualization of an instrument (e.g., catheter, needle) during the procedure. One approach is to fill the instrument with a contrast agent. Previously, the optimization of contrast agent visualization was performed only empirically. In the present study, an analytic optimization of contrast agent SNR efficiency was performed for a spoiled gradient echo pulse sequence. METHODS: Optimal flip angle, repetition time, echo time, and contrast agent concentration were derived analytically. The solution is valid for any contrast agent, provided the relationship between T1 , T2 , and doping concentration is known. RESULTS: Phantom experiments validated the analytic optimization for Gd- and MnCl2 -based contrast agents. Results showed excellent agreement between experimentally predicted and theoretically observed magnetization behavior. In vivo experiments demonstrated optimized contrast agent visualization in brain, heart, and prostate applications. The results demonstrated the large SNR that can be achieved with analytic optimization. As a practical guideline, an 11% dilution of 500 mMol/L Gd-DTPA solution, repetition time ≈ 4 ms, echo time ≈ 1 ms, and θ ≈ 65° was found to provide a large SNR. CONCLUSION: This study derived and validated a method for analytically optimizing contrast agent SNR efficiency. This information may be useful for visualizing instruments during MR-guided interventions.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".