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Record W4313897352 · doi:10.1002/mrm.29581

Quantification of <scp>blood–brain</scp> barrier water exchange and permeability with multidelay diffusion‐weighted <scp>pseudo‐continuous arterial spin labeling</scp>

2023· article· en· W4313897352 on OpenAlexaff
Xingfeng Shao, Chenyang Zhao, Qinyang Shou, Keith St. Lawrence, Danny J.J. Wang

Bibliographic record

VenueMagnetic Resonance in Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsLawson Health Research InstituteWestern University
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute of Neurological Disorders and StrokeNational Institutes of Health
KeywordsChemistryNuclear magnetic resonancePerfusionCerebral blood flowPulse sequenceDiffusion MRIPermeability (electromagnetism)ChromatographyAnalytical Chemistry (journal)Nuclear medicineBiomedical engineeringMagnetic resonance imagingMedicinePhysicsAnesthesiaCardiology

Abstract

fetched live from OpenAlex

Purpose To present a pulse sequence and mathematical models for quantification of blood–brain barrier water exchange and permeability. Methods Motion‐compensated diffusion‐weighted (MCDW) gradient‐and‐spin echo (GRASE) pseudo‐continuous arterial spin labeling (pCASL) sequence was proposed to acquire intravascular/extravascular perfusion signals from five postlabeling delays (PLDs, 1590–2790 ms). Experiments were performed on 11 healthy subjects at 3 T. A comprehensive set of perfusion and permeability parameters including cerebral blood flow (CBF), capillary transit time ( τ c ), and water exchange rate (k w ) were quantified, and permeability surface area product (PS w ), total extraction fraction (E w ), and capillary volume (V c ) were derived simultaneously by a three‐compartment single‐pass approximation (SPA) model on group‐averaged data. With information (i.e., V c and τ c ) obtained from three‐compartment SPA modeling, a simplified linear regression of logarithm (LRL) approach was proposed for individual k w quantification, and E w and PS w can be estimated from long PLD (2490/2790 ms) signals. MCDW‐pCASL was compared with a previously developed diffusion‐prepared (DP) pCASL sequence, which calculates k w by a two‐compartment SPA model from PLD = 1800 ms signals, to evaluate the improvements. Results Using three‐compartment SPA modeling, group‐averaged CBF = 51.5/36.8 ml/100 g/min, k w = 126.3/106.7 min −1 , PS w = 151.6/93.8 ml/100 g/min, E w = 94.7/92.2%, τ c = 1409.2/1431.8 ms, and V c = 1.2/0.9 ml/100 g in gray/white matter, respectively. Temporal SNR of MCDW‐pCASL perfusion signals increased 3‐fold, and individual k w maps calculated by the LRL method achieved higher spatial resolution (3.5 mm 3 isotropic) as compared with DP pCASL (3.5 × 3.5 × 8 mm 3 ). Conclusion MCDW‐pCASL allows visualization of intravascular/extravascular ASL signals across multiple PLDs. The three‐compartment SPA model provides a comprehensive measurement of blood–brain barrier water dynamics from group‐averaged data, and a simplified LRL method was proposed for individual k w quantification.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.797
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.286
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2023
Admission routes1
Has abstractyes

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