Advanced Topics in Quantitative Acoustic Microscopy.
Compressed sensing
Deconvolution
Machine learning
Quantitative
Super-resolution
Ultrahigh frequency
Journal
Advances in experimental medicine and biology
ISSN: 0065-2598
Titre abrégé: Adv Exp Med Biol
Pays: United States
ID NLM: 0121103
Informations de publication
Date de publication:
2023
2023
Historique:
medline:
28
7
2023
pubmed:
27
7
2023
entrez:
26
7
2023
Statut:
ppublish
Résumé
Quantitative acoustic microscopy (QAM) reconstructs two-dimensional (2D) maps of the acoustic properties of thin tissue sections. Using ultrahigh frequency transducers (≥ 100 MHz), unstained, micron-thick tissue sections affixed to glass are raster scanned to collect radiofrequency (RF) echo data and generate parametric maps with resolution approximately equal to the ultrasound wavelength. 2D maps of speed of sound, mass density, acoustic impedance, bulk modulus, and acoustic attenuation provide unique and quantitative information that is complementary to typical optical microscopy modalities. Consequently, many biomedical researchers have great interest in utilizing QAM instruments to investigate the acoustic and biomechanical properties of tissues at the micron scale. Unfortunately, current state-of-the-art QAM technology is costly, requires operation by a trained user, and is accompanied by substantial experimental challenges, many of which become more onerous as the transducer frequency is increased. In this chapter, typical QAM technology and standard image formation methods are reviewed. Then, novel experimental and signal processing approaches are presented with the specific goal of reducing QAM instrument costs and improving ease of use. These methods rely on modern techniques based on compressed sensing and sparsity-based deconvolution methods. Together, these approaches could serve as the basis of the next generation of QAM instruments that are affordable and provide high-resolution QAM images with turnkey solutions requiring nearly no training to operate.
Identifiants
pubmed: 37495922
doi: 10.1007/978-3-031-21987-0_12
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
253-277Informations de copyright
© 2023. The Author(s), under exclusive license to Springer Nature Switzerland AG.
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