Progress in Multimodality Imaging: Truly Simultaneous Ultrasound and Magnetic Resonance Imaging
Bibliographic record
Abstract
Multimodality medical imaging takes advantage of the strengths of different imaging modalities to provide a more complete picture of the anatomy under investigation. Many complementary modalities have been combined to form such systems and some are gaining use clinically. One combination that has not been developed, in large part due to technical difficulties, is a combined magnetic resonance (MR) and ultrasound (US) imaging system. Such a system offers the potential to combine the strengths of these modalities in a wide range of diagnostic and therapeutic applications. The goal of this study was to evaluate the feasibility of performing simultaneous multimodality US and MR imaging. An US imaging system capable of operation in a clinical MR imager was developed, and methods to perform simultaneous imaging were investigated. Simultaneous imaging was feasible without any mutual interference by either filtering the transmitted and received US signal, or by synchronizing data acquisition between the two imaging systems. Spatial registration between the two modalities was achieved by using a reference phantom with implanted glass beads in orthogonal planes. Excellent agreement was observed between spatial measurements of an object made with both modalities, and the feasibility of using this system in vivo was demonstrated in a rabbit model. Simultaneous US and MR imaging is achievable, and can provide complementary information about an object under investigation. This demonstration of technical feasibility and the development of a prototype system open up the potential to investigate the promising clinical applications of this combined technology.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".