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

Deconvolution01:20

Deconvolution

601
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
601
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

1.2K
Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
1.2K
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

921
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...
921
Improving Translational Accuracy02:07

Improving Translational Accuracy

15.0K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
15.0K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.7K
3.7K
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

383
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...
383

You might also read

Related Articles

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

Sort by
Same author

Self-supervised learning for CT image denoising and reconstruction: a review.

Biomedical engineering letters·2024
Same author

Self-supervised denoising of projection data for low-dose cone-beam CT.

Medical physics·2023
Same author

A Weak and Semi-supervised Segmentation Method for Prostate Cancer in TRUS Images.

Journal of digital imaging·2020
Same author

The Classification of Renal Cancer in 3-Phase CT Images Using a Deep Learning Method.

Journal of digital imaging·2019
Same journal

RETRACTION: An IoMT-Based Approach for Real-Time Monitoring Using Wearable Neuro-Sensors.

Journal of healthcare engineering·2026
Same journal

RETRACTION: Learning to Discriminate Adversarial Examples by Sensitivity Inconsistency in IoHT Systems.

Journal of healthcare engineering·2026
Same journal

RETRACTION: Multi-Chaos-Based Lightweight Image Encryption-Compression for Secure Occupancy Monitoring.

Journal of healthcare engineering·2026
Same journal

RETRACTION: Image Risk Assessment of the Thyroid Cancer Model Based on Discriminant Analysis and the Value of TAP and CEA Combined Detection.

Journal of healthcare engineering·2026
Same journal

RETRACTION: Meta-Analysis of the Prognostic Value of Narcotrend Monitoring of Different Depths of Anesthesia and Different Bispectral Index (BIS) Values for Cognitive Dysfunction after Tumor Surgery in Elderly Patients.

Journal of healthcare engineering·2026
Same journal

Correction to "Representation of Differential Learning Method for Mitosis Detection".

Journal of healthcare engineering·2026
See all related articles

Related Experiment Video

Updated: Feb 12, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.2K

A Subband-Specific Deconvolution Model for MTF Improvement in CT.

Seokmin Han1, Kihwan Choi2, Sang Wook Yoo2

  • 1Korea National University of Transportation, Chungju, Republic of Korea.

Journal of Healthcare Engineering
|March 27, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for uniform spatial resolution in computed tomography (CT) images. By applying subband-wise deconvolution, CT images achieve consistent resolution across the field of view without hardware changes.

More Related Videos

Analysis of SEC-SAXS data via EFA deconvolution and Scatter
10:59

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

9.9K
Implantation and Monitoring by PET/CT of an Orthotopic Model of Human Pleural Mesothelioma in Athymic Mice
07:54

Implantation and Monitoring by PET/CT of an Orthotopic Model of Human Pleural Mesothelioma in Athymic Mice

Published on: December 21, 2019

7.3K

Related Experiment Videos

Last Updated: Feb 12, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.2K
Analysis of SEC-SAXS data via EFA deconvolution and Scatter
10:59

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

9.9K
Implantation and Monitoring by PET/CT of an Orthotopic Model of Human Pleural Mesothelioma in Athymic Mice
07:54

Implantation and Monitoring by PET/CT of an Orthotopic Model of Human Pleural Mesothelioma in Athymic Mice

Published on: December 21, 2019

7.3K

Area of Science:

  • Medical Imaging
  • Image Processing
  • Computational Imaging

Background:

  • Computed tomography (CT) imaging often suffers from non-uniform spatial resolution across the field of view.
  • This variation can impact diagnostic accuracy and image quality.
  • Existing solutions typically require hardware modifications or complex calibration procedures.

Purpose of the Study:

  • To develop a computational method for achieving uniform spatial resolution in CT images.
  • To eliminate the need for hardware modifications in CT systems.
  • To improve the overall image quality and consistency of CT scans.

Main Methods:

  • A geometry optics model was used to derive blurring point spread function (PSF) kernels that vary with distance.
  • The field of view (FOV) was divided into 11 subband regions based on X-ray source distance.
  • Each subband was deconvolved using distinct deconvolution kernels tailored to the specific PSF.

Main Results:

  • Subband-wise deconvolution successfully rendered spatial resolution (measured by modulation transfer function - MTF) more uniform across the entire FOV.
  • The method demonstrated significant improvement in resolution uniformity without necessitating additional equipment.
  • The chosen 11 subband setting balanced noise reduction with effective MTF enhancement.

Conclusions:

  • Uniform spatial resolution in CT images is achievable through computational post-processing.
  • The proposed subband-wise deconvolution algorithm is applicable to various CT systems with known parameters.
  • This technique offers a promising approach to enhance CT image quality by improving resolution consistency and mitigating noise amplification.