Flexible real-time magnetic resonance imaging framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The extension of MR imaging to new applications has demonstrated the limitations of the architecture of current real-time systems. Traditional real-time implementations provide continuous acquisition of data and modification of basic sequence parameters on the fly. We have extended the concept of real-time MRI by designing a system that drives the examinations from a real-time localizer and then gets reconfigured for different imaging modes. Upon operator request or automatic feedback the system can immediately generate a new pulse sequence or change fundamental aspects of the acquisition such as gradient waveforms excitation pulses and scan planes. This framework has been implemented by connecting a data processing and control workstation to a conventional clinical scanner. Key components on the design of this framework are the data communication and control mechanisms, reconstruction algorithms optimized for real-time and adaptability, flexible user interface and extensible user interaction. In this paper we describe the various components that comprise this system. Some of the applications implemented in this framework include real-time catheter tracking embedded in high frame rate real-time imaging and immediate switching between real-time localizer and high-resolution volume imaging for coronary angiography applications.
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.
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) | 0.002 | 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 it