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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

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Related Experiment Video

Updated: May 1, 2026

Dual-mode Imaging of Cutaneous Tissue Oxygenation and Vascular Function

Dual-mode Imaging of Cutaneous Tissue Oxygenation and Vascular Function

Published on: December 8, 2010

Dual-coded compressive hyperspectral imaging.

Xing Lin, Gordon Wetzstein, Yebin Liu

|April 2, 2014

Summary

This summary is machine-generated.

This study introduces a novel snapshot hyperspectral imaging method using dual-optical coding and compressive reconstruction. This technique efficiently captures high-resolution spectral data in a single image, enabling diverse applications.

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Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

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Published on: June 18, 2021

Area of Science:

Optics and Photonics
Computational Imaging
Spectroscopy

Background:

Hyperspectral imaging traditionally requires complex setups and slow acquisition times.
Compressive sensing offers a pathway to reduce data acquisition burdens.
Developing snapshot hyperspectral imaging techniques is crucial for real-time applications.

Purpose of the Study:

To present a new snapshot approach for hyperspectral imaging.
To demonstrate a method for coding hyperspectral data into a single sensor image.
To showcase the versatility of the approach through various applications.

Main Methods:

Utilizing dual-optical coding with two high-speed spatial light modulators.
Implementing compressive computational reconstruction for signal recovery.
Designing optical modulation functions for specific imaging tasks.

Main Results:

Successfully coded a hyperspectral datacube into a single sensor image.
Recovered high-resolution spectral information through postprocessing.
Demonstrated applications including programmable color filtering and multiplexed imaging.

Conclusions:

The proposed snapshot hyperspectral imaging approach is effective and versatile.
Dual-optical coding combined with compressive reconstruction enables efficient high-resolution spectral data acquisition.
This method holds promise for advancing various hyperspectral imaging applications.