1709 IMPROVED DETECTION OF KIDNEY STONE TWINKLING USING AUTOREGRESSIVE SIGNAL PROCESSING METHOD
Bibliographic record
Abstract
You have accessJournal of UrologyStone Disease: New Technology/SWL, Ureteroscopic or Percutaneous Stone Removal II1 Apr 20121709 IMPROVED DETECTION OF KIDNEY STONE TWINKLING USING AUTOREGRESSIVE SIGNAL PROCESSING METHOD John Kucewicz, Barbrina Dunmire, and Michael R. Bailey John KucewiczJohn Kucewicz Seattle, WA More articles by this author , Barbrina DunmireBarbrina Dunmire Seattle, WA More articles by this author , and Michael R. BaileyMichael R. Bailey Seattle, WA More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2012.02.1646AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The Twinkling Artifact (TA) is the rapidly changing, random pattern of colors on and deep to kidney stones during ultrasound color Doppler imaging. The potential clinical benefit of TA to urology is well documented, but the use of TA is not without limitations. The origin of TA is not well understood, it appears to be influenced by multiple system settings, and without a specific knob to control it, twinkling can be intermittent. Furthermore, twinkling can be misinterpreted as true blood flow reducing its sensitivity to the detection of stones. The objective of this work is to develop signal processing methods that are highly sensitive to kidney stones and insensitive to blood flow. METHODS Conventional Doppler autocorrelation (AC) methods are optimized to detect blood flow based primarily on backscattered power and Doppler frequency shift. Tissue is typically high power and low frequency, and blood flow is typically low power and high frequency. Twinkling is characteristically high power with broad frequency content, i.e. high amplitude noise. An autoregressive(AR) method has been developed that is able to differentiate between narrowband, coherent signals from tissue and blood flow and broadband, incoherent signals characteristic of TA. Ultrasound data were collected from a tissue phantom containing a human ex-vivo kidney stone and a 5mm flow channel. Water with cellulose was pumped through the flow channel to simulate blood flow. Ultrasound data prior to any Doppler-specific signal and image processing were collected with an Ultrasonix RP (Ultrasonix Medical Corporation, Canada) while varying acoustic output and receiver gain. Twinkling was measured using an AC Doppler method and our AR method. RESULTS AR power was typically 0 to 2dB less from the stone and 5 to 10 dB less from the flow channel relative to power measured by AC. The relative difference between the AR and AC powers was able to differentiate stone twinkling and flow with a sensitivity of 0.94 and a specificity of 0.89. CONCLUSIONS Twinkling is a potentially useful method of imaging kidney stones with ultrasound but its value will remain limited without an imaging mode optimized for the unique ultrasound signals that typify stones. Autoregression is a simple, computationally efficient alternative signal processing method to conventional autocorrelation processing that addresses the limitation of ambiguity between kidney stone twinkling and true blood flow. © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187 Issue 4S April 2012 Page: e689-e690 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.Metrics Author Information John Kucewicz Seattle, WA More articles by this author Barbrina Dunmire Seattle, WA More articles by this author Michael R. Bailey Seattle, WA More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".