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Record W1964359621 · doi:10.1186/1532-429x-14-s1-p241

Simultaneous measurement of blood-flow velocity and regional wall motion with phase unwrapping

2012· article· en· W1964359621 on OpenAlexafffund
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
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAngiologyPhase contrast microscopyBlood flowMedicinePhase unwrappingFlow (mathematics)Doppler effectPhase (matter)Biomedical engineeringDoppler imagingFlow velocityCardiac cycleRadiologyCardiologyInternal medicineMathematicsPhysicsOpticsBlood pressureGeometryDiastole

Abstract

fetched live from OpenAlex

Doppler echocardiography-based techniques are routinely used to assess the heart and ventricular function by measuring blood flow and cardiac tissue velocity profiles. While these measures may similarly be obtained by phase contrast MRI, each require separate acquisitions and subsequent analysis to accommodate for vastly different velocities and therefore velocity encoding (VENC) values. 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 (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. 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. The unwrapped aliasing-free phase-contrast images (VENC = 20 cm/s) are shown in Fig. 1 . The algorithm was insufficient to resolve phase wrapping produced form 10 cm/s acquired datasets, specifically during the early filling stage (detail not presented). The profiles of blood-flow in the LV and RV (VENC = 20cm/s) are close to that acquired with 75 cm/s (Fig. 2a ). The results of mid-ventricula regional wall motion (Fig 2b ) are similar to those reported by other groups. Additionally, we include the results of RV wall motion. The odd-numbered time frames of the 30 unwrapped mid-ventricular phase images (VENC = 20 cm/s). The phase images have been corrected for background phase and are colour-coded in cm/s according to the scale on the right. (a) The blood flow velocity in the LV (BLV) and RV (BRV). (b) Plots of regional wall motion of the middle ventricular plane. WRV denotes the lateral wall of the RV. Our preliminary results suggest that phase unwrapping using an orthogonal recursive technique can resolve phase wrap artifact from datasets acquired at VENC settings as low as 20 cm/s. This approach allows for simultaneous quantification of tissue and blood flow velocities within a single image acquisition and may be of value for the rapid assessment of diastolic dysfunction and left atrial pressures.

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)0.003
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.022
GPT teacher head0.262
Teacher spread0.240 · 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
Admission routes2
Has abstractyes

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