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Sampling Methods: Overview

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...

Sampling Methods: Sample Types

Sampling Methods: Sample Types

Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...

Sampling Plans

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...

Sampling Theorem

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.

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

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Related Experiment Video

Updated: Jul 10, 2026

Continuous Blood Sampling in Small Animal Positron Emission Tomography/Computed Tomography Enables the Measurement of the Arterial Input Function

Continuous Blood Sampling in Small Animal Positron Emission Tomography/Computed Tomography Enables the Measurement of the Arterial Input Function

Published on: August 8, 2019

Monte Carlo simulations verifying sampling schemes in multislice mode PET.

Yannick Grondin¹, Laurent Desbat

¹TIMC-IMAG, Institut d'Ingénierie de l'Information de Santé (In3S), Faculté de Médecine, La Tronche cedex, France. yannick.grondin@imag.fr

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 16, 2007

Summary

This study validated sampling schemes for multislice positron emission tomography (PET) using GATE Monte Carlo simulations. Results align with theoretical expectations, confirming simulation accuracy for PET scanner performance.

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Area of Science:

Medical Imaging
Nuclear Physics
Computational Science

Background:

Positron Emission Tomography (PET) imaging relies on accurate data sampling for optimal image reconstruction.
Multislice mode in PET scanners introduces complexities in data acquisition and requires robust verification methods.

Purpose of the Study:

To validate sampling schemes employed in multislice mode PET imaging.
To assess the accuracy of GATE Monte Carlo simulations for PET applications.

Main Methods:

Utilized GATE Monte Carlo software for simulating PET scanners.
Investigated two distinct scanner designs.
Incorporated realistic phantoms to evaluate the impact of attenuation and scatter effects.

Main Results:

Simulation results for sampling schemes in multislice PET mode were verified.
The impact of attenuation and scatter on image quality was accurately modeled.
Observed outcomes were consistent with theoretical predictions.

Conclusions:

GATE Monte Carlo simulations provide a reliable method for verifying PET sampling schemes.
The simulation accurately reflects physical processes like attenuation and scatter.
This work supports the use of advanced simulations in PET scanner development and validation.