2018
DOI: 10.1109/tuffc.2017.2785230
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Investigation of Physical Phenomena Underlying Temporal-Enhanced Ultrasound as a New Diagnostic Imaging Technique: Theory and Simulations

Abstract: Temporal-enhanced ultrasound (TeUS) is a novel noninvasive imaging paradigm that captures information from a temporal sequence of backscattered US radio frequency data obtained from a fixed tissue location. This technology has been shown to be effective for classification of various in vivo and ex vivo tissue types including prostate cancer from benign tissue. Our previous studies have indicated two primary phenomena that influence TeUS: 1) changes in tissue temperature due to acoustic absorption and 2) micro … Show more

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Cited by 17 publications
(6 citation statements)
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“…Echointensities vary over time due to changes in the microstructure of scatterers (cell nuclei) induced by external or internal vibrations such as pulsation. 3 TeUS signals have been shown to carry tissue-specific information relayed by the patterns of change in echointensity over time. 19 Fig.…”
Section: Data Collectionmentioning
confidence: 99%
“…Echointensities vary over time due to changes in the microstructure of scatterers (cell nuclei) induced by external or internal vibrations such as pulsation. 3 TeUS signals have been shown to carry tissue-specific information relayed by the patterns of change in echointensity over time. 19 Fig.…”
Section: Data Collectionmentioning
confidence: 99%
“…Ultrasonic RF echo time series capture the microstructure information from tissue, such as density of scatterers, variation of scatterers arrangement, etc. 102 Tissue characterization can be carried out by extracting features from RF time series. Imani et al 103 first used this method to realize the thermal ablation monitoring of chicken breast in vitro.…”
Section: Theory and Methodsmentioning
confidence: 99%
“…Temporal‐enhanced US extracts information from the temporal sequence of backscattered US RF data of the ROI 62,63 . In 2016, Azizi et al used a DBN to learn the high‐level latent features of RF data and a SVM classifier to differentiate cancerous versus benign tissue 64 .…”
Section: Ai Applications In Qusmentioning
confidence: 99%