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

Microstructural Change Due to Aging and Its Effect on Fatigue Properties in Sn-Sb-Ag-Ni-Ge Alloy.

Materials (Basel, Switzerland)·2026
Same author

Contralateral sensate free anterior auricular flap with a crus helix cartilage for burn auricular deformity: A Case report.

JPRAS open·2026
Same author

Clinical utility of maximal and minimal intensity projections in T2-weighted MRI for neurosurgical planning.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia·2026
Same author

Innervated superficial circumflex iliac artery perforator flap for refractory elbow ulcer with bone exposure in Werner syndrome: A case report.

JPRAS open·2026
Same author

A recurrence-tolerant strategy for epidermolysis bullosa-related pseudosyndactyly: A microscope-assisted minimally invasive approach.

JPRAS open·2026
Same author

Effects of Scout Direction, Off-Centering, and Scout Imaging Parameters on Radiation Dose Modulation in CT.

Tomography (Ann Arbor, Mich.)·2026

Related Experiment Video

Updated: May 8, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.4K

Noise Reduction in Brain CT: A Comparative Study of Deep Learning and Hybrid Iterative Reconstruction Using Multiple

Yusuke Inoue1, Hiroyasu Itoh2, Hirofumi Hata2

  • 1Department of Diagnostic Radiology, Kitasato University School of Medicine, Sagamihara 252-0374, Japan.

Tomography (Ann Arbor, Mich.)
|December 27, 2024
PubMed
Summary

Deep learning reconstruction (DLR) reduces noise in brain CT scans, especially with thin slices. Its effectiveness varies with slice thickness, tube current, and the object being imaged, unlike hybrid iterative reconstruction (HIR).

Keywords:
braincomputed tomographydeep learning reconstructionhybrid iterative reconstructionimage noise

More Related Videos

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

47.7K
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

496

Related Experiment Videos

Last Updated: May 8, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.4K
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

47.7K
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

496

Area of Science:

  • Medical Imaging
  • Radiology
  • Image Processing

Background:

  • Computed tomography (CT) is essential for brain imaging.
  • Noise in CT images can degrade diagnostic quality.
  • Advanced reconstruction techniques aim to improve image quality and reduce noise.

Purpose of the Study:

  • To compare the noise reduction capabilities of deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) in brain CT.
  • To evaluate the influence of parameters like slice thickness and tube current on noise reduction for both methods.

Main Methods:

  • CT images of phantoms and 11 patients were reconstructed using filtered backprojection (FBP), DLR, and HIR at various slice thicknesses (0.625-5 mm).
  • Noise reduction ratio was quantified using FBP as a reference.
  • Visual assessment of image quality compared DLR and HIR for images with similar noise reduction.

Main Results:

  • Both DLR and HIR demonstrated increased noise reduction with higher reconstruction levels.
  • DLR's noise reduction was significantly dependent on slice thickness, being more effective with thinner slices.
  • HIR showed less dependence on slice thickness, and its performance varied less across different imaging objects compared to DLR.

Conclusions:

  • The noise reduction efficacy of DLR in brain CT is influenced by slice thickness, tube current, and the imaging object.
  • DLR shows particular promise for thin-slice brain CT imaging, potentially offering superior image quality.
  • Clinical application of DLR requires consideration of these influencing factors for optimal results.