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

Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

249
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
249
Fast Fourier Transform01:10

Fast Fourier Transform

229
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
229
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

80
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
80
Discrete Fourier Transform01:15

Discrete Fourier Transform

194
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
194
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

151
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
151
Computed Tomography01:10

Computed Tomography

4.2K
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...
4.2K

You might also read

Related Articles

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

Sort by
Same author

Multi-domain information fusion diffusion model (MDIF-DM) for limited-angle computed tomography.

Journal of X-ray science and technology·2025
Same author

Basic acceleration technique with theoretical analysis on iterative algorithms for image reconstruction.

Journal of X-ray science and technology·2025
Same author

An iterative-FBP dual-spectral CT reconstruction algorithm based on scatter modeling.

Journal of X-ray science and technology·2025
Same author

Dual-domain Wasserstein Generative Adversarial Network with Hybrid Loss for Low-dose CT Imaging.

Physics in medicine and biology·2025
Same author

A model-based direct inversion network (MDIN) for dual spectral computed tomography.

Physics in medicine and biology·2024
Same author

Correction and removal of expression of concern: Natural steroid-based cationic copolymers cholesterol/diosgenin-<i>r</i>-PDMAEMAs and their pDNA nanoplexes: impact of steroid structures and hydrophobic/hydrophilic ratios on pDNA delivery.

RSC advances·2023

Related Experiment Video

Updated: May 16, 2025

Preparation and Observation of Thick Biological Samples by Scanning Transmission Electron Tomography
08:04

Preparation and Observation of Thick Biological Samples by Scanning Transmission Electron Tomography

Published on: March 12, 2017

9.2K

Fourier-enhanced high-order total variation (FeHOT) iterative network for interior tomography.

Genwei Ma1, Xing Zhao2, Yining Zhu2

  • 1The Academy for Multidisciplinary Studies, Captial Normal University, Beijing, People's Republic of China.

Physics in Medicine and Biology
|April 3, 2025
PubMed
Summary

This study introduces the Fourier-enhanced HOT (FeHOT) network for high-precision interior computed tomography (CT) reconstruction from truncated projection data. FeHOT significantly improves image quality and detail preservation, offering a faster and more accurate solution for medical imaging.

Keywords:
Fourier transformdeep learninghigh order total variationinterior tomography

More Related Videos

Using Tomoauto: A Protocol for High-throughput Automated Cryo-electron Tomography
11:33

Using Tomoauto: A Protocol for High-throughput Automated Cryo-electron Tomography

Published on: January 30, 2016

10.9K
Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions
13:43

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

Published on: June 24, 2013

13.9K

Related Experiment Videos

Last Updated: May 16, 2025

Preparation and Observation of Thick Biological Samples by Scanning Transmission Electron Tomography
08:04

Preparation and Observation of Thick Biological Samples by Scanning Transmission Electron Tomography

Published on: March 12, 2017

9.2K
Using Tomoauto: A Protocol for High-throughput Automated Cryo-electron Tomography
11:33

Using Tomoauto: A Protocol for High-throughput Automated Cryo-electron Tomography

Published on: January 30, 2016

10.9K
Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions
13:43

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

Published on: June 24, 2013

13.9K

Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Image Reconstruction

Background:

  • Traditional computed tomography (CT) methods like Filtered Back-Projection (FBP) struggle with low contrast and detail loss.
  • Deep learning approaches for CT reconstruction often face challenges with data consistency and may over-smooth images.
  • Interior tomography reconstruction from truncated projection data remains a significant challenge, impacting image quality and diagnostic accuracy.

Purpose of the Study:

  • To develop a high-precision interior tomography reconstruction method using high-order total variation (HOT) regularization and Fourier-based frequency domain enhancement.
  • To overcome limitations of existing methods, including slow convergence, over-smoothing, and loss of high-frequency details.
  • To achieve accurate reconstruction from truncated projection data, enhancing both contrast and edge preservation.

Main Methods:

  • Proposed a Fourier-enhanced HOT (FeHOT) network utilizing a coarse-to-fine strategy.
  • Employed a HOT-based unrolled iterative network with a learned primal-dual algorithm for data consistency and high-order gradient constraints.
  • Integrated a Fourier-enhanced U-Net module to selectively process frequency components, preserving edge and texture details from Filtered Back-Projection (FBP) results.

Main Results:

  • FeHOT demonstrated superior performance over FBP, HOT, AG-Net, and PD-Net on AAPM and clinical medical datasets.
  • Achieved high Peak Signal-to-Noise Ratio (PSNR) values (e.g., 41.17 noise-free, 39.24 noisy on medical data), significantly outperforming existing methods.
  • Showcased significant improvements in edge preservation (e.g., SSIM increase from 0.9877 to 0.9976) and high-quality reconstruction within five iterations.

Conclusions:

  • FeHOT represents a significant advancement in interior tomography by integrating classical HOT theory with deep learning.
  • The introduction of frequency-domain operations effectively addresses limitations associated with piecewise-constant assumptions in CT images.
  • FeHOT offers a computationally efficient and accurate solution for high-quality interior tomography reconstruction, suitable for low-dose imaging applications.