Motion compensation using principal component analysis and projection onto dipole fields for abdominal magnetic resonance thermometry
Bibliographic record
Abstract
PURPOSE: High intensity focused ultrasound (HIFU) has the potential to locally and non-invasively treat cancer with fewer side effects than alternative therapies. However, motion and tissue heterogeneity in the abdomen can compromise the HIFU focus and confound current thermometry methods. METHODS: The proposed thermometry method combines principal component analysis (PCA), as a multi-baseline technique, and projection onto dipole fields (PDF), as a near-referenceless method. PCA forgoes tracking tools by projecting incoming images onto a subspace spanning the motion history. PDF is subsequently used to synthesize the naturally feasible components of the residual phase using a magnetic dipole model. This leaves only the phase shifts that are induced by HIFU. RESULTS: With in vivo measurements, in porcine and human kidneys, the mean pixel-wise temperature SD was 0.86 ± 0.41°C in selected regions of interest (ROIs) across all data sets, without any user-interaction or supplementary tracking tools. This is an improvement over a benchmark hybrid method, which scored 1.36 ± 1.20°C on the same data. Uncorrected subtraction of the data yielded a score of 3.02 ± 2.87°C. CONCLUSION: The PCA-PDF hybrid method achieves superior artifact correction by exploiting the motion history and intrinsic magnetic susceptibility of the underlying tissue.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".