Magnetic Particle Imaging for High Temporal Resolution Assessment of Aneurysm Hemodynamics
Bibliographic record
Abstract
PURPOSE: The purpose of this work was to demonstrate the capability of magnetic particle imaging (MPI) to assess the hemodynamics in a realistic 3D aneurysm model obtained by additive manufacturing. MPI was compared with magnetic resonance imaging (MRI) and dynamic digital subtraction angiography (DSA). MATERIALS AND METHODS: The aneurysm model was of saccular morphology (7 mm dome height, 5 mm cross-section, 3-4 mm neck, 3.5 mm parent artery diameter) and connected to a peristaltic pump delivering a physiological flow (250 mL/min) and pulsation rate (70/min). High-resolution (4 h long) 4D phase contrast flow quantification (4D pc-fq) MRI was used to directly assess the hemodynamics of the model. Dynamic MPI, MRI, and DSA were performed with contrast agent injections (3 mL volume in 3 s) through a proximally placed catheter. RESULTS AND DISCUSSION: 4D pc-fq measurements showed distinct pulsatile flow velocities (20-80 cm/s) as well as lower flow velocities and a vortex inside the aneurysm. All three dynamic methods (MPI, MRI, and DSA) also showed a clear pulsation pattern as well as delayed contrast agent dynamics within the aneurysm, which is most likely caused by the vortex within the aneurysm. Due to the high temporal resolution of MPI and DSA, it was possible to track the contrast agent bolus through the model and to estimate the average flow velocity (about 60 cm/s), which is in accordance with the 4D pc-fq measurements. CONCLUSIONS: The ionizing radiation free, 4D high resolution MPI method is a very promising tool for imaging and characterization of hemodynamics in human. It carries the possibility of overcoming certain disadvantages of other modalities like considerably lower temporal resolution of dynamic MRI and limited 2D characteristics of DSA. Furthermore, additive manufacturing is the key for translating powerful pre-clinical techniques into the clinic.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".