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

You might also read

Related Articles

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

Sort by
Same author

E2D-Unroll: efficient equivariant deformable unrolling networks for cardiac cine MRI reconstruction.

Physics in medicine and biology·2026
Same author

Study of Gd beam filtration for dual-layer flat panel detector based CBCT imaging: proof-of-concept study with 160 mm diameter phantom.

Physics in medicine and biology·2026
Same author

Integration of Conventional and Radiomic Features From Fluorine-18 Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Imaging for Multimodal Prediction of Symptomatic Carotid Atherosclerotic Plaques.

Journal of the American Heart Association·2026
Same author

Near-isotropic super-resolution CBCT imaging with a dual-layer flat panel detector.

Physics in medicine and biology·2025
Same author

Fast and Accurate Abdominal PDFF and R2* Mapping With Model-Fitted Flip Angle Modulation and Simultaneous Multi-Slice (SMS) 2D Imaging.

Magnetic resonance in medicine·2025
Same author

Reconstruction of total-body multi parametric images with shortened-duration dynamic [<sup>68</sup>Ga]Ga-PSMA-11 and [<sup>68</sup>Ga]Ga-FAPI-04 PET scans.

Physics in medicine and biology·2025
Same journal

Deep learning-based dose prediction to enhance planning efficiency in cervical brachytherapy with hybrid applicators.

Physics in medicine and biology·2026
Same journal

Corrigendum: Referenceless MR thermometry-a comparison of five methods (2017<i>Phys. Med. Biol</i>.<b>62</b>1-16).

Physics in medicine and biology·2026
Same journal

Corrigendum: Measured and Monte Carlo simulated electron backscatter to the monitor chamber for the varian TrueBeam linac (2016<i>Phys. Med. Biol</i>.<b>61</b>8779).

Physics in medicine and biology·2026
Same journal

Corrigendum: 3D range-modulator for scanned particle therapy: development, Monte Carlo simulations and experimental evaluation (2017<i>Phys. Med. Biol</i>.<b>62</b>7075).

Physics in medicine and biology·2026
Same journal

Recent progress in applications of computing to radiotherapy (ICCR 2016).

Physics in medicine and biology·2026
Same journal

Novel TMS coils designed using an inverse boundary element method.

Physics in medicine and biology·2026
See all related articles

Related Experiment Video

Updated: Jul 9, 2025

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

1.8K

Super-resolution dual-layer CBCT imaging with model-guided deep learning.

Jiongtao Zhu1, Ting Su2, Xin Zhang2

  • 1Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, People's Republic of China.

Physics in Medicine and Biology
|December 4, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces suRi-Net, a novel deep learning method for super-resolution Cone Beam CT (CBCT) imaging using dual-layer flat panel detectors (DL-FPD). The approach significantly enhances spatial resolution in dual-energy imaging for CBCT systems.

Keywords:
dual-energy imagingdual-layer flat panel detectorhigh-resolution imagingimaging model

More Related Videos

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

867
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K

Related Experiment Videos

Last Updated: Jul 9, 2025

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

1.8K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

867
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K

Area of Science:

  • Medical Imaging
  • Radiology
  • Computer Vision

Background:

  • Dual-layer flat panel detectors (DL-FPD) offer potential for advanced imaging.
  • Current CBCT systems face limitations in spatial resolution for dual-energy imaging.

Purpose of the Study:

  • To investigate a novel super-resolution CBCT imaging approach using DL-FPD.
  • To develop and validate a deep learning method for enhancing spatial resolution in DL-FPD based CBCT.

Main Methods:

  • A mathematical model was developed to describe signal formation in DL-FPD.
  • A recurrent neural network, suRi-Net, was designed to retrieve high-resolution dual-energy information.
  • Physical benchtop experiments were performed for validation.

Main Results:

  • suRi-Net successfully retrieved high spatial resolution information from low-resolution projections.
  • Spatial resolution increased by approximately 45% in the top detector layer and 54% in the bottom layer.
  • The method demonstrated accurate retrieval of high-resolution dual-energy information.

Conclusions:

  • The suRi-Net method provides an effective approach for super-resolution CBCT imaging with DL-FPD.
  • This technique significantly improves spatial resolution in dual-energy CBCT.
  • suRi-Net offers a promising new direction for high-resolution dual-energy imaging in CBCT systems.