A real-time 3D large field-of-view MRI system with interactive table motion
Bibliographic record
Abstract
Data acquisition using a continuously moving table (CMT) is a new method that is capable of generating three-dimensional (3D) large field-of-view (FOV) MR images. To obtain artifact-free large FOV images or arterial-phase contrast-enhanced large FOV angiograms, a major challenge in CMT is to acquire all of the necessary MR data while matching the table motion to the time-varying images and the physiological dynamics of interest. Rather than restrict CMT techniques to a constant table translation rate and offline reconstruction, here we discuss and implement a more general real-time approach in which the table motion is decoupled from the MR data acquisition. In our implementation, the table moves interactively in response to real-time preview images to ensure optimal imaging conditions. We accomplished this objective by designing and integrating a high-resolution, MR-compatible table position-encoding system to a novel, general-purpose, real-time MR imaging system on a clinical MR scanner. Various technical issues and their practical solutions related to implementation are presented. Experimental results obtained from a lower extremity vascular phantom and five healthy volunteers demonstrate that our proposed approach is robust and able to rapidly and optimally acquire large continuous 3D images by interactively moving the table in response to real-time data reconstruction. We anticipate that the implemented system and its components will be useful for a variety of MR applications, including whole-body screening, angiographic runoff studies, and real-time imaging. © 2006 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 29B: 28–41, 2006
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".