Interactive continuously moving table (iCMT) large field‐of‐view real‐time MRI
Bibliographic record
Abstract
Continuously moving table (CMT) MRI is a new method that is capable of generating 3D, seamless, large field-of-view (FOV) images by acquiring readouts along the patient superior-inferior axis as the subject is translated through the scanner. For applications that require artifact-free images, such as arterial-phase contrast-enhanced (CE) angiography of the legs, a major challenge is to match the MR data acquisition and patient table motion with the dynamics of blood flow in the region of interest (ROI). Instead of restricting the CMT to predetermined constant table speeds, we adopted a more general approach in which the table motion is decoupled from the phase-encoding order. In our approach the table moves adaptively and in response to operator-provided feedback obtained from viewing real-time preview (or fluoroscopic) images. This interactivity is accomplished by integrating high temporal-spatial resolution encoding of the table position with real-time hybrid-space filling and image reconstruction. Experimental results obtained using our prototype interactive CMT (iCMT) system on a peripheral vascular phantom and five healthy volunteers demonstrate the feasibility of this robust and rapid imaging method for acquiring 3D large-FOV continuous images with patient-specific adaptive table motion profiles.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".