Advancing Acoustic Droplet Vaporization for Tissue Characterization Using Quantitative Ultrasound and Transfer Learning

Acoustic droplet vaporization (ADV) is an emerging technique with expanding applications in biomedical ultrasound. ADV-generated bubbles can function as microscale probes that provide insights into the mechanical properties of their surrounding microenvironment. This study investigated the acoustic...

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Vydané v:	IEEE transactions on biomedical engineering Ročník 72; číslo 6; s. 1897 - 1908
Hlavní autori:	Kaushik, Anuj, Fabiilli, Mario L., Myers, Daniel D., Fowlkes, J. Brian, Aliabouzar, Mitra
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.06.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Acoustic droplet vaporization Acoustic responses Acoustics Artificial neural networks Biological tissues Biomedical engineering Biomedical imaging Bubbles Cavitation Deep Learning Density Droplets Fibrin Fibrin - chemistry Homogeneity Humans Hydrogels Hydrogels - chemistry Hydrophones Image Processing, Computer-Assisted - methods Imaging techniques Lipidomics Liquids Mean Mechanical properties Microenvironments Neural networks Phantoms, Imaging phase-shift droplets Probes Radiology Texture tissue characterization Tissue engineering Transfer learning Ultrasonic imaging Ultrasonography - methods Ultrasound Vaporization Volatilization
ISSN:	0018-9294, 1558-2531, 1558-2531
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Abstract	Acoustic droplet vaporization (ADV) is an emerging technique with expanding applications in biomedical ultrasound. ADV-generated bubbles can function as microscale probes that provide insights into the mechanical properties of their surrounding microenvironment. This study investigated the acoustic and imaging characteristics of phase-shift nanodroplets in fibrin-based, tissue-mimicking hydrogels using passive cavitation detection and active imaging techniques, including B-mode and contrast-enhanced ultrasound. The findings demonstrated that the backscattered signal intensities and pronounced nonlinear acoustic responses, including subharmonic and higher harmonic frequencies, of ADV-generated bubbles correlated inversely with fibrin density. Additionally, we quantified the mean echo intensity, bubble cloud area, and second-order texture features of the generated ADV bubbles across varying fibrin densities. ADV bubbles in softer hydrogels displayed significantly higher mean echo intensities, larger bubble cloud areas, and more heterogeneous textures. In contrast, texture uniformity, characterized by variance, homogeneity, and energy, correlated directly with fibrin density. Furthermore, we incorporated transfer learning with convolutional neural networks, adapting AlexNet into two specialized models for differentiating fibrin hydrogels. The integration of deep learning techniques with ADV offers great potential, paving the way for future advancements in biomedical diagnostics.
AbstractList	Acoustic droplet vaporization (ADV) is an emerging technique with expanding applications in biomedical ultrasound. ADV-generated bubbles can function as microscale probes that provide insights into the mechanical properties of their surrounding microenvironment. This study investigated the acoustic and imaging characteristics of phase-shift nanodroplets in fibrin-based, tissue-mimicking hydrogels using passive cavitation detection and active imaging techniques, including B-mode and contrast-enhanced ultrasound. The findings demonstrated that the backscattered signal intensities and pronounced nonlinear acoustic responses, including subharmonic and higher harmonic frequencies, of ADV-generated bubbles correlated inversely with fibrin density. Additionally, we quantified the mean echo intensity, bubble cloud area, and second-order texture features of the generated ADV bubbles across varying fibrin densities. ADV bubbles in softer hydrogels displayed significantly higher mean echo intensities, larger bubble cloud areas, and more heterogeneous textures. In contrast, texture uniformity, characterized by variance, homogeneity, and energy, correlated directly with fibrin density. Furthermore, we incorporated transfer learning with convolutional neural networks, adapting AlexNet into two specialized models for differentiating fibrin hydrogels. The integration of deep learning techniques with ADV offers great potential, paving the way for future advancements in biomedical diagnostics. Acoustic droplet vaporization (ADV) is an emerging technique with expanding applications in biomedical ultrasound. ADV-generated bubbles can function as microscale probes that provide insights into the mechanical properties of their surrounding microenvironment. This study investigated the acoustic and imaging characteristics of phase-shift nanodroplets in fibrin-based, tissue-mimicking hydrogels using passive cavitation detection and active imaging techniques, including B-mode and contrast-enhanced ultrasound. The findings demonstrated that the backscattered signal intensities and pronounced nonlinear acoustic responses, including subharmonic and higher harmonic frequencies, of ADV-generated bubbles correlated inversely with fibrin density. Additionally, we quantified the mean echo intensity, bubble cloud area, and second-order texture features of the generated ADV bubbles across varying fibrin densities. ADV bubbles in softer hydrogels displayed significantly higher mean echo intensities, larger bubble cloud areas, and more heterogeneous textures. In contrast, texture uniformity, characterized by variance, homogeneity, and energy, correlated directly with fibrin density. Furthermore, we incorporated transfer learning with convolutional neural networks, adapting AlexNet into two specialized models for differentiating fibrin hydrogels. The integration of deep learning techniques with ADV offers great potential, paving the way for future advancements in biomedical diagnostics.Acoustic droplet vaporization (ADV) is an emerging technique with expanding applications in biomedical ultrasound. ADV-generated bubbles can function as microscale probes that provide insights into the mechanical properties of their surrounding microenvironment. This study investigated the acoustic and imaging characteristics of phase-shift nanodroplets in fibrin-based, tissue-mimicking hydrogels using passive cavitation detection and active imaging techniques, including B-mode and contrast-enhanced ultrasound. The findings demonstrated that the backscattered signal intensities and pronounced nonlinear acoustic responses, including subharmonic and higher harmonic frequencies, of ADV-generated bubbles correlated inversely with fibrin density. Additionally, we quantified the mean echo intensity, bubble cloud area, and second-order texture features of the generated ADV bubbles across varying fibrin densities. ADV bubbles in softer hydrogels displayed significantly higher mean echo intensities, larger bubble cloud areas, and more heterogeneous textures. In contrast, texture uniformity, characterized by variance, homogeneity, and energy, correlated directly with fibrin density. Furthermore, we incorporated transfer learning with convolutional neural networks, adapting AlexNet into two specialized models for differentiating fibrin hydrogels. The integration of deep learning techniques with ADV offers great potential, paving the way for future advancements in biomedical diagnostics.
Author	Aliabouzar, Mitra Fowlkes, J. Brian Kaushik, Anuj Myers, Daniel D. Fabiilli, Mario L.
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Cites_doi	10.1177/0161734620932 10.1016/j.compbiomed.2023.107872 10.1039/D0SM00753F 10.1121/1.5091781 10.1016/j.zemedi.2023.01.004 10.1109/TUFFC.2002.1009331 10.1109/58.981381 10.1590/0100-3984.2018.0084 10.1016/0041-624X(92)90041-J 10.3390/ijms160715997 10.3389/fonc.2023.1258970 10.1038/s41598-017-13977-x 10.1016/j.ultsonch.2016.01.019 10.1088/0031-9155/58/13/4513 10.1118/1.4747526 10.1016/j.ultrasmedbio.2024.05.019 10.1088/1361-6560/ab1a64 10.1016/j.ultrasmedbio.2010.03.015 10.21037/cdt.2017.11.05 10.1016/S0301-5629(00)00262-3 10.1016/j.protcy.2012.09.084 10.1117/1.JMI.10.S2.S22410 10.1016/j.ultrasmedbio.2011.05.001 10.1371/journal.pone.0266446 10.1121/AT.2022.18.2.14 10.1016/j.ultrasmedbio.2021.12.007 10.1088/1361-6560/ab5093 10.1016/j.actbio.2023.04.037 10.1016/j.ultrasmedbio.2022.08.008 10.1002/macp.202100366 10.1111/j.1365-2559.2010.03609.x 10.1016/j.ultsonch.2022.106090 10.1016/j.ultras.2023.106940 10.1177/01617346211009788 10.1016/j.diii.2013.02.005 10.1097/RLI.0000000000000919 10.1177/0161734616639875 10.7150/thno.18650 10.1121/1.2166708 10.1002/mp.13361 10.1016/j.ejrad.2013.05.008 10.1002/adhm.202101672 10.1109/LSENS.2023.3307102 10.1117/1.JBO.17.2.026007 10.1016/j.ultsonch.2024.106754 10.1016/j.compbiomed.2021.104407 10.1016/j.ultrasmedbio.2022.11.004 10.1242/dmm.004077 10.1109/TUFFC.2020.3032441 10.1016/j.ultrasmedbio.2019.08.018 10.1109/TSMC.1973.4309314 10.1016/j.ultsonch.2024.106984
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References	ref13 Misra (ref53) 2021 ref12 ref15 ref14 ref52 ref11 ref55 ref10 ref54 ref16 ref19 ref18 Burgess (ref17) 2015; 11 ref51 ref50 ref46 ref45 ref48 ref47 ref42 ref41 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 Kingma (ref44) 2014 ref5 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29
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SubjectTerms	Acoustic droplet vaporization Acoustic responses Acoustics Artificial neural networks Biological tissues Biomedical engineering Biomedical imaging Bubbles Cavitation Deep Learning Density Droplets Fibrin Fibrin - chemistry Homogeneity Humans Hydrogels Hydrogels - chemistry Hydrophones Image Processing, Computer-Assisted - methods Imaging techniques Lipidomics Liquids Mean Mechanical properties Microenvironments Neural networks Phantoms, Imaging phase-shift droplets Probes Radiology Texture tissue characterization Tissue engineering Transfer learning Ultrasonic imaging Ultrasonography - methods Ultrasound Vaporization Volatilization
Title	Advancing Acoustic Droplet Vaporization for Tissue Characterization Using Quantitative Ultrasound and Transfer Learning
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