Steady-State MR Imaging Sequences: Physics, Classification, and Clinical Applications
Bibliographic record
Abstract
Steady-state sequences are a class of rapid magnetic resonance (MR) imaging techniques based on fast gradient-echo acquisitions in which both longitudinal magnetization (LM) and transverse magnetization (TM) are kept constant. Both LM and TM reach a nonzero steady state through the use of a repetition time that is shorter than the T2 relaxation time of tissue. When TM is maintained as multiple radiofrequency excitation pulses are applied, two types of signal are formed once steady state is reached: preexcitation signal (S-) from echo reformation; and postexcitation signal (S+), which consists of free induction decay. Depending on the signal sampled and used to form an image, steady-state sequences can be classified as (a) postexcitation refocused (only S+ is sampled), (b) preexcitation refocused (only S- is sampled), and (c) fully refocused (both S+ and S- are sampled) sequences. All tissues with a reasonably long T2 relaxation time will show additional signals due to various refocused echo paths. Steady-state sequences have revolutionized cardiac imaging and have become the standard for anatomic functional cardiac imaging and for the assessment of myocardial viability because of their good signal-to-noise ratio and contrast-to-noise ratio and increased speed of acquisition. They are also useful in abdominal and fetal imaging and hold promise for interventional MR imaging. Because steady-state sequences are now commonly used in MR imaging, radiologists will benefit from understanding the underlying physics, classification, and clinical applications of these sequences.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".