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Record W2059604234 · doi:10.1186/1532-429x-14-s1-w49

Simultaneous wall and blood-flow phase-contrast imaging using a single low VENC

2012· article· en· W2059604234 on OpenAlexaff
Junmin Liu, James A. White, Maria Drangova

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2012
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsAngiologyMedicinePhase contrast microscopyContrast (vision)Blood flowRadiologyMedical physicsInternal medicineArtificial intelligenceComputer scienceOptics

Abstract

fetched live from OpenAlex

Intra-ventricular blood flow and regional myocardial motion are two key components used in assessing cardiac function. Both can be quantified using phase contrast MRI, but typically require two imaging sequences to be acquired - one with a high velocity encoding value (VENC) and one with a low VENC, selected to optimize velocity sensitivity while avoiding aliasing. In an effort to obtain velocity information from both ventricular blood and myocardial motion in one acquisition, dual-VENC techniques (Buchenberg, ISMRM 2011) have been proposed and evaluated. MR imaging was performed on a 3.0-T whole-body scanner (MR 750, GE Medical Systems). Phase-contrast images with through-plane velocity-encoding were acquired in the short-axis plane with a retrospectively triggered 2D fast cine phase contrast pulse sequence (segmented k-space gradient-echo; TR/TE, 7.3/4.4 ms; flip angle 15 degree, slice thickness 8 mm) with first-order flow compensation in all dimensions to minimize artifacts from flow and motion. Three VENCs - 75, 20 and 10 cm/s - were used and the images acquired with VENC = 75 cm/s were used as a reference. The acquisition time (per VENC) was about 15 seconds, enabling acquisition within a single breath-hold. Thirty images were reconstructed per cardiac cycle. All images were analyzed off-line using algorithms developed using MATLAB. Phase unwrapping of the velocity data was achieved using an algorithm developed in our lab, which uses an orthogonal recursive approach to remove streaks that result following conventional 2D phase unwrapping. Mid-ventricular phase-contrast images corresponding to peak systole and early filling are shown in Figures 1 and 2 , respectively. Severe phase aliasing is seen within the LV blood pool when VENCs of 20 and 10 cm/s were used (Figures 1b and 2b ). Our technique successfully unwrapped the images acquired with VENC = 20 cm/s but failed with VENC = 10 cm/s for the early-filling stage. The flow velocity from phase images acquired using VENC = 20 cm/s are quantitatively similar to the reference phase images but with lower noise in heart wall compared to that of VENC = 75 cm/s (submit to SCMR 2012). Peak systolic stage: magnitude (a), measured phase (b) and unwrapped phase (c). The phase images have been corrected for background phase (based on the mean phase value within the boxes drawn) and are scaled in cm/s according to the scale on the right. Early-filling stage: magnitude (a), measured phase (b) and unwrapped phase (c). The phase images have been corrected for background phase (based on the mean phase value within the boxes drawn) and are scaled in cm/s according to the scale on the right. The results suggest that single low-VENC (>= 20 cm/s) acquisitions can be successfully used to measure intra-ventricular flow and wall motion simultaneously.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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

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 head0.013
GPT teacher head0.271
Teacher spread0.259 · 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".

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Citations0
Published2012
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Has abstractyes

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