Development of a platform for co-registered ultrasound and MR contrast imaging<i>in vivo</i>
Bibliographic record
Abstract
Imaging of the microvasculature is often performed using contrast agents in combination with either ultrasound (US) or magnetic resonance (MR) imaging. Contrast agents are used to enhance medical imaging by highlighting microvascular properties and function. Dynamic signal changes arising from the passage of contrast agents through the microvasculature can be used to characterize different pathologies; however, comparisons across modalities are difficult due to differences in the interactions of contrast agents with the microvasculature. Better knowledge of the relationship of contrast enhancement patterns with both modalities could enable better characterization of tissue microvasculature. We developed a co-registration platform for multi-modal US and MR imaging using clinical imaging systems in order to study the relationship between US and MR contrast enhancement. A preliminary validation study was performed in phantoms to determine the registration accuracy of the platform. In phantoms, the in-plane registration accuracy was measured to be 0.2 ± 0.2 and 0.3 ± 0.2 mm, in the lateral and axial directions, respectively. The out-of-plane registration accuracy was estimated to be 0.5 mm ±0.1. Co-registered US and MR imaging was performed in a rabbit model to evaluate contrast kinetics in different tissue types after bolus injections of US and MR contrast agents. The arrival time of the contrast agent in the plane of imaging was relatively similar for both modalities. We studied three different tissue types: muscle, large vessels and fat. In US, the temporal kinetics of signal enhancement were not strongly dependent on tissue type. In MR, however, due to the different amounts of agent extravasation in each tissue type, tissue-specific contrast kinetics were observed. This study demonstrates the feasibility of performing in vivo co-registered contrast US and MR imaging to study the relationships of the enhancement patterns with each modality.
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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".