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

A multi-modal flow phantom for quantitative PET/Spectral CT.

bioRxiv : the preprint server for biology·2026

Same author

Quantitative imaging of the central lymphatic system with spectral CT iodine mapping: a feasibility study in swine.

medRxiv : the preprint server for health sciences·2026

Same author

Efficient and Practical Framework for Bias Estimation in Spectral CT.

medRxiv : the preprint server for health sciences·2026

Same author

Transformer-based Fusion of Longitudinal Multimodal Radiomic Features from Chest Radiography and CT in COVID-19.

Radiology. Artificial intelligence·2026

Same author

Double-Bowtie Filter Design for Pediatric Spectral CT Imaging.

medRxiv : the preprint server for health sciences·2026

Same author

Evaluation of a CZT-based photon-counting detector CT prototype for low-dose lung cancer screening using patient-specific lung phantoms.

medRxiv : the preprint server for health sciences·2026

Search research articles

Related Experiment Video

Updated: Jun 28, 2025

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Published on: January 18, 2021

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction

Jessica Y Im^1,2, Sandra S Halliburton³, Kai Mei¹

¹Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States of America.

Physics in Medicine and Biology

|April 11, 2024

Summary

Deep learning reconstruction (DLR) significantly improves CT image quality, enabling substantial radiation dose reduction. A novel 3D-printed lung phantom validated DLR

Keywords:

3D-printed phantom 3D-printing CT imaging phantoms deep learning reconstruction dose reduction image quality assessment

More Related Videos

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Published on: July 14, 2020

Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement

Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement

Published on: July 29, 2013

Related Experiment Videos

Last Updated: Jun 28, 2025

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Published on: January 18, 2021

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Published on: July 14, 2020

Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement

Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement

Published on: July 29, 2013

Area of Science:

Medical Imaging
Radiology
Artificial Intelligence in Medicine

Background:

Deep learning reconstruction (DLR) algorithms have object-dependent performance, challenging traditional phantom evaluations.
Existing CT phantoms may not accurately represent clinical imaging scenarios for DLR.

Purpose of the Study:

To evaluate a commercial DLR algorithm using a patient-derived 3D-printed lung phantom.
To assess DLR performance across various radiation dose levels and phantom sizes.
To compare DLR against traditional reconstruction methods (FBP, iterative reconstruction).

Main Methods:

A 3D-printed lung phantom with realistic pathologies (ground glass opacities) was created using PixelPrint technology.
Scans were performed on a conventional CT scanner at doses ranging from 0.5 to 20 mGy.
Images were reconstructed using FBP, iterative reconstruction, and DLR with five denoising levels.

Main Results:

DLR outperformed FBP and iterative reconstruction across all image quality metrics (noise, CNR, RMSE, SSIM, MS SSIM).
DLR demonstrated superior performance with increased denoising levels.
Estimated dose reduction with DLR ranged from 25%-83% without compromising image quality.

Conclusions:

DLR can achieve diagnostic image quality at significantly reduced radiation doses (up to 83%).
The PixelPrint phantom provides a more realistic evaluation environment than traditional phantoms.
DLR enhances the clinical utility of low-dose CT scans.