Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

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

Sort by

Same author

Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.

Diagnostics (Basel, Switzerland)·2026

Same author

Machine Learning Calibration of Smartphone-Based Infrared Thermal Cameras: Improved Bias and Persistent Random Error.

Sensors (Basel, Switzerland)·2026

Same author

Machine Learning-Based Approaches for Breast Density Estimation from Mammograms: A Comprehensive Review.

Journal of imaging·2025

Same author

Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention.

Sensors (Basel, Switzerland)·2023

Same author

Regression Analysis between the Different Breast Dose Quantities Reported in Digital Mammography and Patient Age, Breast Thickness, and Acquisition Parameters.

Journal of imaging·2022

Same author

An IoT Machine Learning-Based Mobile Sensors Unit for Visually Impaired People.

Sensors (Basel, Switzerland)·2022

Same journal

Semantic Explanation for Malaria Diagnosis: Comparing Human and Machine Generated Annotations for <i>Plasmodium</i> Species and Life-Stage Features.

IEEE open journal of engineering in medicine and biology·2026

Same journal

An Improved Beta Burst Extraction for Chip-Based Deep Brain Stimulation With Real-Time Model Updating.

IEEE open journal of engineering in medicine and biology·2026

Same journal

Transcranial Temporal Interference Stimulation: A Brief Review of Architectures, Circuits, and Application Challenges.

IEEE open journal of engineering in medicine and biology·2026

Same journal

An Intra-Body Power Transfer System via Localized Capacitive Coupling.

IEEE open journal of engineering in medicine and biology·2026

Same journal

Shared and Individual Resting-State MEG Network Signatures of Tinnitus Revealed by Holistic Graph Learning.

IEEE open journal of engineering in medicine and biology·2026

Same journal

Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation.

IEEE open journal of engineering in medicine and biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 7, 2025

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Machine Learning-Based X-Ray Projection Interpolation for Improved 4D-CBCT Reconstruction.

Jayroop Ramesh¹, Donthi Sankalpa¹, Rohan Mitra¹

¹Department of Computer Science and EngineeringAmerican University of Sharjah Sharjah 26666 UAE.

IEEE Open Journal of Engineering in Medicine and Biology

|November 20, 2024

Summary

This summary is machine-generated.

This study enhances 4D-CBCT imaging by using deep learning for better X-ray projection interpolation. The Real-Time Intermediate Flow Estimation (RIFE) model significantly improves image quality, reducing artifacts for clearer medical scans.

Keywords:

4D-CBCT reconstruction deep learning intermediate projection interpolation multi-output regression transfer learning

More Related Videos

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

Related Experiment Videos

Last Updated: Jun 7, 2025

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

Area of Science:

Medical Imaging
Radiology
Computer Vision

Background:

Respiration-correlated cone-beam computed tomography (4D-CBCT) generates dynamic volumetric images but is limited by projection data quality.
Image quality is directly influenced by the number of available CBCT projections for reconstruction.
Interpolation techniques can create intermediate projections to improve reconstruction.

Purpose of the Study:

To investigate the use of transfer learning and novel regression models for generating intermediate projections in 4D-CBCT.
To evaluate the performance of state-of-the-art deep learning video frame interpolation models for this task.
To assess the impact of interpolated projections on the final 4D-CBCT image quality.

Main Methods:

Employed pre-trained deep learning models, including the Real-Time Intermediate Flow Estimation (RIFE) algorithm, for video frame interpolation.
Developed a novel regression predictive modeling approach to generate intermediary projections.
Validated model performance using digital phantom and clinical datasets.

Main Results:

The RIFE algorithm demonstrated superior performance, achieving high SSIM (0.986 ± 0.010), PSNR (44.13 ± 2.76), and low MSE (18.86 ± 206.90).
4D-CBCT images reconstructed with interpolated projections showed reduced streaking artifacts compared to those reconstructed with original projections alone.
Transfer learning algorithms effectively enhanced 4D-CBCT image quality.

Conclusions:

Transfer learning, particularly using models like RIFE, offers a significant advantage for improving 4D-CBCT image quality.
The proposed methods successfully generated intermediate projections, leading to enhanced image clarity and artifact reduction.
This approach holds promise for improving diagnostic accuracy in 4D-CBCT imaging.