A Theoretical Approach in Applying High-Frequency Acoustic and Elasticity Microscopy to Assess Cells and Tissues.
Frank Winterroth, Jing Wang, Onno Wink, Bart Carelsen, Jeremy Dahl, Avnesh S Thakor
Author Information
Frank Winterroth: 1Philips Research, Eindhoven, The Netherlands; email: frank.winterroth@gmail.com.
Jing Wang: 2Interventional Regenerative Medicine and Imaging Laboratory, Department of Radiology, Stanford University School of Medicine, Palo Alto, California, USA.
Onno Wink: 1Philips Research, Eindhoven, The Netherlands; email: frank.winterroth@gmail.com.
Bart Carelsen: 1Philips Research, Eindhoven, The Netherlands; email: frank.winterroth@gmail.com.
Jeremy Dahl: 2Interventional Regenerative Medicine and Imaging Laboratory, Department of Radiology, Stanford University School of Medicine, Palo Alto, California, USA.
Avnesh S Thakor: 2Interventional Regenerative Medicine and Imaging Laboratory, Department of Radiology, Stanford University School of Medicine, Palo Alto, California, USA.
Medical ultrasound is a diagnostic imaging modality used for visualizing internal organs; the frequencies typically used are 2-10 MHz. Scanning acoustic microscopy (SAM) is a form of ultrasound where frequencies typically exceed 50 MHz. Increasing the acoustic frequency increases the specimen's spatial resolution but reduces the imaging depth. The advantages of using SAM over conventional light and electron microscopy include the ability to image cells and tissues without any preparation that could kill or alter them, providing a more accurate representation of the specimen. After scanning the specimen, acoustic signals are merged into an image on the basis of changes in the impedance mismatch between the immersion fluid and the specimens. The acoustic parameters determining the image quality are absorption and scattering. Surface scans can assess surface characteristics of the specimen. SAM is also capable of elastography, that is, studying elastic properties to discern differences between healthy and affected tissues. SAM has significant potential for detection/analysis in research and clinical studies.
