Max‐IDEAL: A max‐flow based approach for IDEAL water/fat separation
Bibliographic record
Abstract
PURPOSE: To propose a novel approach to water/fat separation using a unique smoothness constraint. THEORY AND METHODS: Chemical-shift based water/fat separation is an established noninvasive imaging tool for the visualization of body fat in various anatomies. Nevertheless, B0 magnetic field inhomogeneities can hamper the water/fat separation process. In this work, B0 variations are mapped using a convex-relaxed labeling model which produces a coarse estimate of the field map, while considering T2* decay during the labeling process. Fat and water components are subsequently resolved using T2*-IDEAL. An adaptive spatial filtering (ASF) was introduced to improve the robustness of the estimate. The method was tested on cardiac and abdominal datasets from healthy volunteers and nonalcoholic fatty liver disease (NAFLD) patients. RESULTS: Out of 168 cardiac and abdominal images, only 1 case has shown water/fat swaps that can hinder the clinical interpretation of the underlying anatomy. CONCLUSION: This work demonstrates a new water/fat separation approach that prevents the occurrence of water/fat swaps, by means of a unique smoothness constraint. Incorporating T2* effect in the labeling procedure and including the ASF processing enhance the robustness of the proposed approach and permit the procedure to handle abrupt B0 variations within the field of view.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".