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Imaging Studies III: Computed Tomography

Imaging Studies III: Computed Tomography

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...

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.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Related Experiment Video

Updated: May 16, 2026

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Multiple-input ghost imaging via sparsity constraints.

Wenlin Gong¹, Shensheng Han

¹Key Laboratory for Quantum Optics and Center for Cold Atom Physics of CAS, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China. gongwl@siom.ac.cn

Journal of the Optical Society of America. A, Optics, Image Science, and Vision

|December 4, 2012

Summary

This summary is machine-generated.

We introduce multiple-input ghost imaging via sparsity constraints (MI-GISC), a novel technique using sparse-array detectors. This method enhances remote imaging quality, especially with small apertures, by leveraging target sparsity.

Related Experiment Videos

Last Updated: May 16, 2026

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Area of Science:

Optics and Photonics
Computational Imaging
Signal Processing

Background:

Standard ghost imaging typically uses a bucket detector.
Existing methods may have limitations in remote imaging scenarios with small apertures.

Purpose of the Study:

To investigate the theory and reconstruction of multiple-input ghost imaging via sparsity constraints (MI-GISC).
To analyze the properties and differences between MI-GISC and compressive ghost imaging (CGI).
To demonstrate the applicability of MI-GISC in remote imaging systems.

Main Methods:

Utilizing sparse-array single-pixel detectors as the test detector.
Incorporating the target's sparsity constraints into the ghost imaging framework.
Theoretical analysis and numerical simulations to validate the proposed method.

Main Results:

MI-GISC demonstrates improved reconstruction quality compared to standard methods.
The proposed scheme effectively handles the propagation process between object and detection planes.
Numerical simulations confirm the theoretical findings and advantages of MI-GISC.

Conclusions:

MI-GISC offers a viable approach for remote imaging, particularly with small receiving numerical apertures.
The technique enhances target reconstruction quality by exploiting sparsity.
MI-GISC presents a significant advancement over traditional ghost imaging schemes in specific applications.