Spatial Resolution Measurements
Spatial Resolution Measurements - Related Videos
08:52
Spatial Molecular Imaging of the Glycome Using Mass Spectrometry
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Cited by 1 •
2025
This protocol details the key steps to enable rigorous and reproducible measurement of both glycogen and N-linked glycans using mass spectrometry imaging.
09:00
Spatial Measurements of Perfusion, Interstitial Fluid Pressure and Liposomes Accumulation in Solid Tumors
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Cited by 6 •
2016
The heterogeneous intra-tumoral accumulation of liposomes has been linked to an abnormal tumor microenvironment. Herein methods are presented to measure tumor microcirculation by perfusion imaging and elevated interstitial fluid pressure (IFP) using an image-guided robotic system. Measurements are compared to the intra-tumoral accumulation of liposomes, determined using volumetric micro-CT imaging.
11:57
Measuring Spatially- and Directionally-varying Light Scattering from Biological Material
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Cited by 5 •
2013
We present a non-destructive method for sampling spatial variation in the direction of light scattered from structurally complex materials. By keeping the material intact, we preserve gross-scale scattering behavior, while concurrently capturing fine-scale directional contributions with high-resolution imaging. Results are visualized in software at biologically-relevant positions and scales.
08:59
An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice
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Cited by 2 •
2023
Here, we present a simplified open-source hardware and software setup for investigating mouse spatial learning using virtual reality (VR). This system displays a virtual linear track to a head-restrained mouse running on a wheel by utilizing a network of microcontrollers and a single-board computer running an easy-to-use Python graphical software package.
09:36
Measurement of Spatial Stability in Precision Grip
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Cited by 2 •
2020
The goal of this protocol is to measure the center of pressure (COP) replacement using a high spatial resolution sensor sheet to reflect the spatial stability in a precision grip. The use of this protocol could contribute to greater understanding of the physiology and pathophysiology of grasping.
FAQs
Optics determine how sharply scene details are formed, while detector size limits how finely those details are recorded. A system may therefore lose the ability to separate nearby features even when its optical design is strong, if the detector cannot sample the image finely enough. Measuring under defined conditions helps distinguish limitations caused by the imaging path from those introduced during detection.
Calibration targets, line pairs, edge responses, and point spread functions provide different ways to analyze imaging performance. Line pairs directly test separation of nearby features, edge responses show how sharply a transition is reproduced, and a point spread function characterizes the response to a localized feature. Selecting among these approaches allows engineers to examine resolution according to the instrument and measurement objective.
Spatial resolution measurements depend on operating conditions, not only on instrument design. Poor focus can blur fine structures, insufficient sampling can limit the spatial information recorded, and weak signal quality can obscure distinctions between features. Controlling or documenting these factors makes the result more reliable and helps engineers determine whether an observed limitation comes from focus, sampling, or the available signal.
The systems should be evaluated with comparable targets and defined operating conditions, then their ability to represent fine features can be examined using the same type of response or pattern. This controlled comparison reduces ambiguity caused by different test setups. The resulting data support system comparison by showing how optics, detector size, sampling rate, focus, and signal quality affect the recorded detail.
A basic workflow places a suitable calibration target or other test feature within the instrument's field of view, operates the system under defined conditions, and records the resulting image or measurement response. The data are then analyzed through line pairs, an edge response, or a point spread function. Repeating this approach consistently supports calibration and quality-control decisions.
Engineers use spatial resolution measurements when selecting, calibrating, comparing, or checking imaging and measurement systems. The results can reveal whether a camera, microscope, scanner, display, or remote-sensing system represents small structures adequately. They also provide evidence for design decisions and quality control when accurate spatial representation matters, rather than relying only on nominal instrument specifications.
The same measurement principle can be adapted to different systems by choosing an appropriate target or response analysis and maintaining defined operating conditions. For cameras and microscopes, the result reflects image formation and detection; for scanners and displays, it characterizes how spatial information is recorded or represented. This common framework supports meaningful characterization across varied engineering instruments.