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Record W4391448580 · doi:10.1161/str.55.suppl_1.128

Abstract 128: Multi-Direction Diffusion Weighted Imaging on Portable, Low-Field Magnetic Resonance Imaging

2024· article· en· W4391448580 on OpenAlexaff
Annabel Sorby‐Adams, John E. Kirsch, Jennifer D. Guo, Olivia Nelson, Joshua N. Goldstein, John Pitts, Rafael O’Halloran, Pamela W. Schaefer, Otto Rapalino, Matthew S. Rosen, Juan Eugenio Iglesias, Kevin N. Sheth, W. Taylor Kimberly

Bibliographic record

VenueStroke · 2024
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsHyperion Technologies (Canada)
Fundersnot available
KeywordsMedicineMagnetic resonance imagingDiffusion MRIDiffusion imagingNuclear medicineEffective diffusion coefficientAcute strokeWhite matterRadiologyStroke (engine)Internal medicine

Abstract

fetched live from OpenAlex

Background and aims: Portable, low-field (LF) MRI has the potential to improve access to expeditious, definitive brain imaging and facilitate diagnosis of acute stroke. Currently available diffusion-weighted imaging (DWI) protocols at LF are limited to a single diffusion direction due to acquisition duration. However, single-direction diffusion has reduced sensitivity for detecting acute ischemic infarcts, particularly small lesions residing in white matter tracts. The purpose of this study was to establish the feasibility of acquiring multi-direction DWI compared with single-direction counterparts on LF-MRI. Methods: Patients presenting with a diagnosis of acute ischemic stroke between July and September 2023 were eligible. Consented patients underwent DWI acquisition on a 0.064T LF-MRI (Mk1.9; Hyperfine Research Inc). Three diffusion directions (x, y, and z) were acquired with a b weighting of 900 s/mm 2 and a single acquisition with a b 0 s/mm 2 . The b 900 images were co-registered to the b 0, trace and apparent diffusion coefficient (ADC) maps calculated, and the final images interpolated at 1 mm 3 . Results: Ten patients presenting to the Massachusetts General Hospital with acute ischemic stroke were consented and imaged within 72 hours of last known well. The total acquisition time was 14 minutes, with all subjects able to tolerate the scan duration. Ischemic lesions as small as 0.1 mL were detectable on the LF-MRI (17.5 +/- 18.2 mL). An example of each diffusion direction individually, the combined trace, and corresponding ADC maps are shown in Figure 1, compared with conventional high-field (HF) diffusion images acquired within 30 minutes of the LF acquisition. Conclusion: Multi-direction DWI imaging is feasible on a 0.064T LF-MRI scanner. Our experience suggests further modifications to the pulse sequence and scanner configuration may facilitate a reduction in acquisition time, improve resolution, or both.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.003

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 head0.009
GPT teacher head0.297
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2024
Admission routes1
Has abstractyes

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