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Related Experiment Video

Updated: May 3, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

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Published on: April 12, 2024

Optimized imaging using non-rigid registration.

Benjamin Berkels¹, Peter Binev², Douglas A Blom³

¹Interdisciplinary Mathematics Institute, 1523 Greene Street, University of South Carolina, Columbia, SC 29208, USA.

Ultramicroscopy

|February 11, 2014

Summary

This summary is machine-generated.

We developed a new image processing method to extract more information from microscopy data, overcoming challenges like motion blur and low signal. This technique enhances data analysis for advanced imaging applications.

Keywords:

Diffusion registration HAADF-STEM IQ-factor Non-rigid registration Si-Y zeolite

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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

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Published on: November 23, 2019

Area of Science:

Materials Science
Microscopy
Image Processing

Background:

Modern imaging devices provide rich data, but current processing methods fail to capture its full potential.
Complex motion during data acquisition introduces spatial uncertainty, hindering accurate analysis.
Low signal-to-noise ratios are common in advanced microscopy techniques.

Purpose of the Study:

To propose a novel methodology for enhanced information extraction from imaging data.
To address challenges posed by spatial uncertainty and low signal-to-noise ratios.
To develop objective quality measures for automated image processing.

Main Methods:

A non-rigid pixel-wise registration method was employed to handle complex multiscale motion.
The methodology processes a series of datasets to increase information content.
Objective quality measures were formulated to replace manual inspection.

Main Results:

The proposed method successfully extracts increased information from challenging datasets.
The technique effectively manages spatial uncertainty caused by complex motion.
Objective quality measures provide reliable assessment of image processing outcomes.

Conclusions:

The developed methodology significantly enhances information extraction from advanced microscopy data.
This approach overcomes limitations of current image processing techniques.
The method is validated using scanning transmission electron microscopy of zeolite materials.