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Atomic Force Microscopy

Atomic Force Microscopy

Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
The AFM Probe
The probe is regarded as the heart of any AFM setup and comprises the...

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Updated: Jul 30, 2025

Three-dimensional Optical-resolution Photoacoustic Microscopy

Three-dimensional Optical-resolution Photoacoustic Microscopy

Published on: May 3, 2011

Deep learning-assisted frequency-domain photoacoustic microscopy.

George J Tserevelakis, Georgios D Barmparis, Nikolaos Kokosalis

|May 15, 2023

Summary

This summary is machine-generated.

Frequency-domain photoacoustic microscopy (FD-PAM) struggles with low signal-to-noise ratio (SNR). A U-Net neural network enhances FD-PAM images, improving accessibility and applicability without high power or averaging.

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Area of Science:

Biomedical Imaging
Optical Microscopy
Signal Processing

Background:

Frequency-domain photoacoustic microscopy (FD-PAM) offers cost-efficient imaging.
FD-PAM suffers from significantly lower signal-to-noise ratio (SNR) compared to time-domain systems.
Low SNR limits FD-PAM's practical applications and requires extensive signal averaging or high optical power.

Purpose of the Study:

To address the inherent SNR limitations of FD-PAM.
To improve the image quality and accessibility of FD-PAM systems.
To expand the applicability of FD-PAM to more demanding imaging scenarios.

Main Methods:

Utilized a U-Net neural network for image augmentation.
Implemented intensity-modulated laser beams for single-frequency photoacoustic wave excitation.
Focused on enhancing SNR without increasing optical power or averaging.

Main Results:

Achieved significant SNR improvement in FD-PAM images.
Demonstrated effective image augmentation using the U-Net architecture.
Maintained high image quality standards despite SNR enhancement.

Conclusions:

The U-Net approach effectively overcomes FD-PAM's SNR limitations.
This method reduces system cost and expands the utility of photoacoustic microscopy.
Improved FD-PAM accessibility and applicability for advanced imaging needs.