1709 IMPROVED DETECTION OF KIDNEY STONE TWINKLING USING AUTOREGRESSIVE SIGNAL PROCESSING METHOD
Notice bibliographique
Résumé
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 ...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».