Quantum Mechanical Simulations In Diffusion MRI
Bibliographic record
Abstract
Background: Various magnetic resonance imaging simulation packages rely on Bloch equations, BlochTorrey equations and the Liouville–von Neumann equation is which a dynamical formulation to simulate a voltage bias across a molecular system and to model a time-dependent current in terms of classical or quantum treatments of magnetic resonance imaging respectively. Method: The problems in these equations cannot address spin dynamic such as j-coupling and spatial dynamics such as diffusion and flow at the same level. In this study, the Fokker-Planck formalism was used to simulate phantoms that deal with diffusion and flow on the spatial dynamics side and j-coupling in the spin dynamic side using the Spinach simulation package. Result: The numerical simulation of magnetic resonance imaging has two limits in terms of research. First, a complicated spin system is associated with simple diffusion and flow, such as in spatially encoded NMR experiments. Second, a simple spin system is associated with high dimensional diffusion and flow. Conclusion: A unique simulation package that deals with the quantum mechanics treatment of spin dynamics and the classical description of diffusion and flow in three dimensions are presented in this work. Funding Statement: The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this study.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".