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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Veröffentlicht in:	IEEE transactions on biomedical engineering Jg. 72; H. 6; S. 1897 - 1908
Hauptverfasser:	Kaushik, Anuj, Fabiilli, Mario L., Myers, Daniel D., Fowlkes, J. Brian, Aliabouzar, Mitra
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.06.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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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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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