Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Convolution Properties II01:17

Convolution Properties II

581
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
581
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

819
Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
819
Retrieval01:12

Retrieval

422
Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
422
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

294
Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
294
Convolution Properties I01:20

Convolution Properties I

559
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
559
Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

405
Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
405

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

MUFOLD-DB: a processed protein structure database for protein structure prediction and analysis.

BMC genomics·2015
Same author

The I-TASSER Suite: protein structure and function prediction.

Nature methods·2014
Same author

Genome-wide expression analysis of soybean NF-Y genes reveals potential function in development and drought response.

Molecular genetics and genomics : MGG·2014
Same author

Classification of lung cancer using ensemble-based feature selection and machine learning methods.

Molecular bioSystems·2014
Same author

Resveratrol possesses protective effects in a pristane-induced lupus mouse model.

PloS one·2014
Same author

Protein-losing enteropathy in systemic lupus erythematosus: 12 years experience from a Chinese academic center.

PloS one·2014
Same journal

From Geometry to Intensity: A Coarse-to-Fine Pipeline for Unsupervised 3D Ultrasound Stitching.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

AVA: Automated Viewability Analysis for Ureteroscopic Intrarenal Surgery.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Kidney Endoscopy Video to Preoperative CT Alignment for Depth Estimation.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Deep learning‑based cell type prediction in lung tissue from brightfield histology using CODEX-derived labels.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Reconstructing physiological signals from fMRI across the adult lifespan.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Axially Swept Light-Sheet Microscopy using scattering and fluorescence contrast mechanisms.

Proceedings of SPIE--the International Society for Optical Engineering·2026
See all related articles

Related Experiment Video

Updated: Jan 20, 2026

High Resolution 3D Imaging of Ex-Vivo Biological Samples by Micro CT
08:57

High Resolution 3D Imaging of Ex-Vivo Biological Samples by Micro CT

Published on: June 21, 2011

19.2K

High-resolution CT Image Retrieval Using Sparse Convolutional Neural Network.

Yang Lei1, Dong Xu2, Zhengyang Zhou3

  • 1Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.

Proceedings of Spie--The International Society for Optical Engineering
|August 29, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel sparse convolutional neural network method to enhance low-resolution CT images into high-resolution ones. This technique improves image quality, potentially reducing patient radiation exposure and enhancing dose calculation accuracy.

Keywords:
CTHigh-resolution image retrievalconvolutional neural network

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K

Related Experiment Videos

Last Updated: Jan 20, 2026

High Resolution 3D Imaging of Ex-Vivo Biological Samples by Micro CT
08:57

High Resolution 3D Imaging of Ex-Vivo Biological Samples by Micro CT

Published on: June 21, 2011

19.2K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Computed Tomography (CT) imaging is crucial for medical diagnosis.
  • Enhancing the resolution of low-quality CT scans is essential for accurate interpretation.
  • Current methods for CT image enhancement face limitations in detail reconstruction.

Purpose of the Study:

  • To develop a high-resolution CT image retrieval method using a sparse convolutional neural network.
  • To train an end-to-end framework for mapping low-resolution CT images to high-resolution counterparts.
  • To assess the potential of the proposed method in improving radiation dose calculation and reducing patient exposure.

Main Methods:

  • Extracted patch-wise features from low-resolution CT images.
  • Utilized a sparse representation with a convolutional layer and an iterative shrinkage threshold framework.
  • Employed a Restricted Linear Unit for non-linear mapping and an adaptive dictionary for signature construction.
  • Reconstructed high-resolution patches and averaged overlapping patches for final image generation.
  • Optimized the end-to-end neural network using a loss function comparing reconstructed and ground truth images.

Main Results:

  • The proposed method successfully enhanced image resolution from low-resolution CT inputs.
  • Quantitative assessment using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Absolute Error (MAE) demonstrated effectiveness.
  • The technique was validated on both brain and lung CT images, showing improved image quality.

Conclusions:

  • The developed sparse convolutional neural network effectively generates high-resolution CT images from low-resolution inputs.
  • This approach holds significant potential for enhancing radiation dose calculation and delivery accuracy.
  • The method offers a promising way to decrease radiation exposure for patients undergoing CT scans.