Tissue thermal conductivity by magnetic resonance thermometry and focused ultrasound heating
Bibliographic record
Abstract
PURPOSE: To investigate the combined use of magnetic resonance (MR) temperature imaging and focused ultrasound (FUS) for the noninvasive determination of tissue thermal properties. MATERIALS AND METHODS: Brief, spatial impulses of temperature elevation were created in tissue using a spherical, air-backed transducer operating at 1.68 MHz and measured using MR temperature imaging in a 1.5-Tesla clinical scanner. A novel technique based on thermal washout is applied in an analysis of the acquired MR temperature images to estimate tissue thermal conductivity and perfusion. RESULTS: Numerical simulations and experiments in vitro and in vivo demonstrate that thermal conductivity can be measured to within 10% of the true value with MR thermometry at 1.5 Tesla. With the temperature precision available at 1.5 Tesla, however, robust perfusion estimation is feasible only in highly perfused organs or tumors. CONCLUSION: This study has developed a method for determining tissue thermal properties specific to the patient and organ at the site of interest, and allows repeated application. This capability is relevant in thermal therapy planning of tumor ablation using MR-guided FUS systems.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".