A flexible approach for fat-water separation with bipolar readouts and correction of gradient-induced phase and amplitude effects
Bibliographic record
Abstract
Purpose: To develop a fat-water separation approach that corrects bipolar readout gradient induced effects, without additional scans, that is compatible with any fat-water separation method. Theory and Methods: The proposed approach combines joint fat-water separation of the odd and even echoes of a bipolar multi-echo gradient echo acquisition with an inverse problem to find least-squares estimates for phase and amplitude corrections to eliminate bipolar-induced effects. Optimization of sequence parameter selection through the calculation of the number of signal averages (NSA) with Cramér-Rao Bound theory (CRB) is presented. The application of the proposed approach is demonstrated with a graph cut optimization and further characterization of the accuracy was performed via Monte Carlo Simulations (MC). The proposed approach was tested in phantoms and in vivo. Proton density fat fraction maps (PDFF) were evaluated to quantify performance. Results: NSA calculations suggest short TE1 and ΔTE=1.5 ms as optimal alternatives for fat-water separation. MC simulations demonstrated accurate estimation of fat and water complex signals, ψ, and R_2^* with mean relative error within 1%. In phantoms and in vivo, the proposed approach effectively eliminated effects induced by bipolar readout gradients, improving the outcome of the fat-water separation. Conclusion: We proposed an approach to correct bipolar readout-induced effects that are detrimental for fat-water separation. This approach can extend the use of existing fat-water separation techniques designed for data acquired using unipolar readout gradients to data collected with bipolar readout gradients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".