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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on biomedical engineering Ročník 72; číslo 6; s. 1897 - 1908
Hlavní autoři:	Kaushik, Anuj, Fabiilli, Mario L., Myers, Daniel D., Fowlkes, J. Brian, Aliabouzar, Mitra
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.06.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0018-9294 1558-2531 1558-2531
DOI:	10.1109/TBME.2025.3527141