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Related Concept Videos

Upsampling01:22

Upsampling

180
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
180
Downsampling01:20

Downsampling

117
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
117
Aliasing01:18

Aliasing

104
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
104
Bandpass Sampling01:17

Bandpass Sampling

147
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
147
Sampling Theorem01:15

Sampling Theorem

271
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
271

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Updated: May 21, 2025

Three-dimensional Optical-resolution Photoacoustic Microscopy
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Method for Selecting the Down-Sampling Factor of Photoacoustic Image by Using Cumulative Power Difference in

Shihao Tang1, Min Wan1, Yameng Zhang2

  • 1Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China.

Journal of Biophotonics
|March 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method, cumulative power difference (CPD), to quickly select optimal settings for photoacoustic microscopy (PAM) imaging. CPD helps improve image quality assessment and reduces the need for extensive prior testing.

Keywords:
optical imagingphotoacoustic techniquessampling studiesspectrum analysis

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Area of Science:

  • Biomedical Imaging
  • Optical Imaging
  • Photoacoustics

Background:

  • Photoacoustic microscopy (PAM) is a non-invasive imaging technique.
  • A key limitation of PAM is its slow imaging speed due to sparse spatial sampling.
  • Selecting optimal down-sampling factors for PAM requires extensive prior experimentation.

Purpose of the Study:

  • To propose a novel frequency-domain evaluation index, cumulative power difference (CPD).
  • To enable rapid selection of the optimal down-sampling factor for PAM.
  • To overcome the limitations of slow imaging speed and extensive parameter tuning in PAM.

Main Methods:

  • Developed and applied the cumulative power difference (CPD) evaluation index.
  • Utilized CPD for analyzing down-sampled photoacoustic images of mouse ear and brain.
  • Correlated CPD with established image quality metrics like PCC, MSE, and SSIM.

Main Results:

  • A decreasing trend in image quality was observed as the down-sampling factor increased.
  • CPD showed a significant correlation with PCC/MSE/SSIM (p < 0.001).
  • CPD effectively evaluates photoacoustic image quality and quantifies quality loss from down-sampling.

Conclusions:

  • CPD enables rapid, accurate selection of optimal down-sampling factors for PAM.
  • This method facilitates quality assessment of down-sampled images without prior inspection.
  • The study expands PAM's application range and supports its clinical potential.