Dynamic phantom with heart, lung, and blood motion for initial validation of MRI techniques
Bibliographic record
Abstract
PURPOSE: To develop an anthropomorphic phantom to simulate heart, lung, and blood motion. Magnetic resonance imaging (MRI) is sensitive to image distortion and artifacts caused by motion. Imaging phantoms are used to test new sequences, but generally, these phantoms lack physiological motion. For the validation of new MR-based endovascular interventional and other techniques, we developed a dynamic motion phantom that is suitable for initial in vitro and more realistic validation studies that should occur before animal experiments. MATERIALS AND METHODS: An anthropomorphic phantom was constructed to model the thoracic cavity, including respiratory and cardiac motions, and moving blood. Several MRI methods were used to validate the phantom performance: anatomical scanning, rapid temporal imaging, digital subtraction angiography, and endovascular tracking. The quality and nature of the motion artifact in these images were compared with in vivo images. RESULTS: The closed-loop motion phantom correctly represented key features in the thorax, was MR-compatible, and was able to reproduce similar motion artifacts and effects as seen in in vivo images. The phantom provided enough physiological realism that it was able to ensure a suitable challenge in an in vitro catheter tracking experiment. CONCLUSION: A phantom was created and used for testing interventional catheter guiding. The images produced had similar qualities to those found in vivo. This phantom had a high degree of appropriate anthropomorphic and physiological qualities. Ethically, use of this phantom is highly appropriate when first testing new MRI techniques prior to conducting animal studies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".