Dynamic T2-mapping during magnetic resonance guided high intensity focused ultrasound ablation of bone marrow
Bibliographic record
Abstract
Focal bone tumor treatments include amputation, limb-sparing surgical excision with bone reconstruction, and high-dose external-beam radiation therapy. Magnetic resonance guided high intensity focused ultrasound (MR-HIFU) is an effective non-invasive thermotherapy for palliative management of bone metastases pain. MR thermometry (MRT) measures the proton resonance frequency shift (PRFS) of water molecules and produces accurate (<1°C) and dynamic (<5s) thermal maps in soft tissues. PRFS-MRT is ineffective in fatty tissues such as yellow bone marrow and, since accurate temperature measurements are required in the bone to ensure adequate thermal dose, MR-HIFU is not indicated for primary bone tumor treatments. Magnetic relaxation times are sensitive to lipid temperature and we hypothesize that bone marrow temperature can be determined accurately by measuring changes in T2, since T2 increases linearly in fat during heating. T2-mapping using dual echo times during a dynamic turbo spin-echo pulse sequence enabled rapid measurement of T2. Calibration of T2-based thermal maps involved heating the marrow in a bovine femur and simultaneously measuring T2 and temperature with a thermocouple. A positive T2 temperature dependence in bone marrow of 20 ms/°C was observed. Dynamic T2-mapping should enable accurate temperature monitoring during MR-HIFU treatment of bone marrow and shows promise for improving the safety and reducing the invasiveness of pediatric bone tumor treatments.
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 | 0.000 | 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) | 0.001 | 0.000 |
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".