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Record W1508745515

Improving Elastography using SURF Imaging for Suppression of Reverberations

2010· dissertation· en· W1508745515 on OpenAlexaboutno aff
Jørgen Grythe

Bibliographic record

VenueBIBSYS Brage (BIBSYS (Norway)) · 2010
Typedissertation
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsnot available
Fundersnot available
KeywordsElastographyRadiologyMedicineUltrasound
DOInot available

Abstract

fetched live from OpenAlex

For some of the applications of the Second-order UltRasound Field (SURF) imaging technique, a real-time delay-estimation algorithm has been developed for estimating spatially range-varying delays in RF signals. This algorithm is a phase-based approach for subsample delay estimation, and makes no assumption on the local delay variation. Any parametric model can be used for modeling the local delay variation. The phase-based delay estimator uses estimates of the instantaneous frequency and the phase difference and the relationship between the two to estimate the delay. The estimated delay may be used to calculate an improved estimate of the instantaneous frequency, which in turn may be used to calculate new, updated values for the delay using an iterative scheme. Although an iterative scheme introduces a larger bias, the estimated delay values have a significantly lowered standard deviation in comparison to the original method. The delay estimator originally developed for estimating propagation delays for SURF imaging, can also be used for elastography purposes. By not being restricted to locally constant delays, the delay estimator is able to more robustly estimate sharp changes in tissue stiffness, and in estimating small differences in strain more closely. Two different parametric models for the local delay have been tried, one linear, and one polynomial of the first degree. The two various models have been tested on an elastography recording provided by the Ultrasonix company (Ultrasonix Medical Corporation, Vancouver, Canada), and in vitro. Using a polynomial of the second degree as parametric model for the delay is better than a linear model in detecting edges of inclusions located at a depth where the strain is lower than closer to the transducer surface. The differences may be further emphasized by performing spatial filtering with a median filter. The downside of updating the model is an increased computational time of approximately 50%. Multiple reflections, also known as reverberations, appear as acoustic noise in ultrasound images and may greatly impair time-delay estimation, particularly in elastography. Today reverberation suppression is achieved by second harmonic imaging, but this method has the disadvantage of low penetration, and little or no signal in the near field. The SURF imaging technique has the advantages of reverberation suppression in addition to imaging in the fundamental frequency. A reverberation model has been established, and the effect reverberations have on estimated elastography images is studied. When using a layered silicon plate as reverberation model, and imaging through this initial reverberation model placed on top of the imaging phantom, elastography images were not obtained as the quality of the recording was degraded as a result of power loss. By adding reverberations by computer simulations after a recording with a SURF probe with reverberation suppression was performed, a markedly difference between elastography estimates done on the image with reverberations, and the image with reverberations and reverberation suppression was observed. Estimating on a signal with reverberations, the phase-based time-delay algorithm was unable to distinguish any differences in elasticity at all. Estimating time delays on a signal with reverberations and SURF reverberation suppression however, the algorithm was able to clearly estimate differences in strain, and display the presence of an inclusion.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.643
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.229
Teacher spread0.222 · 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.

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

Citations1
Published2010
Admission routes1
Has abstractyes

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