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Related Concept Videos

Upsampling01:22

Upsampling

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...
Downsampling01:20

Downsampling

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...
Sampling Theorem01:15

Sampling Theorem

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.
Sampling Methods: Overview01:06

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of sampling...
Bandpass Sampling01:17

Bandpass Sampling

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. The spectrum...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

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Optimal image sampling schedule: a new effective way to reduce dynamic image storage space and functional image

X Li1, D Feng, K Chen

  • 1Dept. of Comput. Sci., Sydney Univ., NSW.

IEEE Transactions on Medical Imaging
|January 1, 1996
PubMed
Summary

A new optimal image sampling schedule for positron emission tomography (PET) studies significantly reduces data processing and storage needs. This method maintains high precision in parameter estimation for glucose metabolism, proving robust for diverse subjects.

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Area of Science:

  • Nuclear Medicine
  • Medical Imaging
  • Biophysics

Background:

  • Positron Emission Tomography (PET) dynamic studies require efficient image acquisition protocols.
  • Accurate estimation of metabolic rates, like local cerebral metabolic rate of glucose (LCMRGlc), is crucial for clinical diagnosis.
  • Current sampling schedules can be data-intensive, impacting processing time and storage.

Purpose of the Study:

  • To propose an optimized image sampling schedule for dynamic PET studies.
  • To reduce data storage and processing time while maintaining estimation precision.
  • To evaluate the robustness of the proposed schedule against parameter variations.

Main Methods:

  • Development of a novel cost function and application of the D-optimal criterion for schedule design.
  • Case study using fluorodeoxyglucose (FDG) tracer and a four-parameter FDG model for LCMRGlc estimation.
  • Computer simulations to assess the impact of intersubject and intrasubject parameter variations.

Main Results:

  • The proposed schedule requires only four dynamic images, substantially decreasing storage and processing demands.
  • Parameter estimation precision is comparable to commonly used, more data-intensive schedules.
  • The optimal sampling schedule demonstrated robustness against variations in subject parameters.

Conclusions:

  • The developed optimal sampling schedule is efficient for dynamic PET studies.
  • It offers significant advantages in data management without compromising accuracy.
  • The schedule is suitable for both regional and image-wide parameter estimation across different subjects.