Relaxation‐compensated fast multislice amide proton transfer (APT) imaging of acute ischemic stroke
Bibliographic record
Abstract
Amide proton transfer (APT) imaging is a variant form of chemical exchange saturation transfer (CEST) imaging that is based on the magnetization exchange between bulk water and labile endogenous amide protons. Given that chemical exchange is pH-dependent, APT imaging has been shown capable of imaging ischemic tissue acidosis, and as such, may serve as a surrogate metabolic imaging marker complementary to perfusion and diffusion MRI. In order for APT imaging to properly diagnose heterogeneous pathologies such as stroke and cancer, fast volumetric APT imaging has to be developed. In this study the evolution of CEST contrast after RF irradiation was solved showing that although the CEST steady state is reached by the apparent longitudinal relaxation rate, the decreases of CEST contrast after irradiation is governed by the intrinsic relaxation constant. A volumetric APT imaging sequence is proposed that acquires multislice images immediately after a single long continuous wave (CW) RF irradiation, wherein the relaxation-induced loss of CEST contrast is compensated for during postprocessing. The proposed technique was verified by numerical simulation, a tissue-like dual-pH phantom, and demonstrated on an embolic stroke animal model. In summary, our study has established a fast volumetric pH-weighted APT imaging technique, allowing further investigation to fully evaluate its diagnostic power.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".