Magnetic resonance imaging sequence evaluation of an MR Linac system; early clinical experience
Bibliographic record
Abstract
OBJECTIVES: To systematically identify the preferred magnetic resonance imaging (MRI) sequences following volunteer imaging on a 1.5 Tesla (T) MR-Linear Accelerator (MR Linac) for future protocol development. METHODS: Non-patient volunteers were recruited to a Research and Ethics committee approved prospective MR-only imaging study on a 1.5T MR Linac system. Volunteers attended 1-3 imaging sessions that included a combination of mDixon, T1w, T2w sequences using 2-dimensional (2D) and 3-dimensional (3D) acquisitions. Each sequence was acquired over 2-7 minutes and reviewed by a panel of 3 observers to evaluate image quality using a visual grading analysis based on a 4-point Likert scale. Sequences were acquired and modified iteratively until deemed fit for purpose (online image matching or re-planning) and all observers agreed they were suitable in 3 volunteers. RESULTS: 26 volunteers underwent 31 imaging sessions of six general anatomical regions. Images were acquired in one or two of six general anatomical regions: male pelvis (n = 9), female pelvis (n = 4), chestwall/breast (n = 5), lung/oesophagus (n = 5), abdomen (n = 3) and head and neck (n = 5). Images were acquired using a pre-defined exam-card that on average, included six sequences (range 2-10), with a maximum scan time of approximately one hour. The majority of observers preferred T2-weighted sequences. The thorax teams were the only groups to prefer T1-weighted imaging. CONCLUSIONS: An iterative process identified sequence agreement in all anatomical regions. These sequences will now be evaluated in patient volunteers. ADVANCES IN KNOWLEDGE: This manuscript is the first publication sharing the results of the first systematic selection of MRI sequences for use in on-board MRI-guided radiotherapy by end-users (therapeutic radiographers and clinical oncologists) in healthy volunteers.
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.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".