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

Erratum to: Glomerular filtration rate estimated by Cockcroft-Gault formula better predicts anti-Xa levels than Modification of the diet in renal disease equation in older patients with prophylactic enoxaparin.

The journal of nutrition, health & aging·2024
Same author

Spacecraft sample collection and subsurface excavation of asteroid (101955) Bennu.

Science (New York, N.Y.)·2022
Same author

Cross-Instrument Comparison of MapCam and OVIRS on OSIRIS-REx.

Space science reviews·2022
Same author

Prospective multicenter study on personalized and optimized MDCT contrast protocols: results on liver enhancement.

European radiology·2021
Same author

Meniscal lesion detection and characterization in adult knee MRI: A deep learning model approach with external validation.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)·2021
Same author

Bennu's near-Earth lifetime of 1.75 million years inferred from craters on its boulders.

Nature·2020
Same journal

Discrimination of plaque from sluggish-flow-related hyperintense artifact on high-resolution magnetic resonance vessel wall imaging.

European journal of radiology·2026
Same journal

MRI-based quantification of intratumoral heterogeneity for differentiating glioblastoma from solitary brain metastasis: a two-center study.

European journal of radiology·2026
Same journal

MRI/MRCP and endoscopic ultrasound in pancreatobiliary disease: defining complementary roles in diagnostic and therapeutic decision-making.

European journal of radiology·2026
Same journal

Left atrial geometry in atrial Fibrillation: A comparison between electroanatomical mapping and computed tomography.

European journal of radiology·2026
Same journal

Enhancing pancreatic imaging in CT - prospective comparison of fixed versus individualized post-trigger delay in bolus tracking.

European journal of radiology·2026
Same journal

Ultrasound elastography of the liver and spleen in postoperative monitoring after abdominal surgery: A radiological perspective.

European journal of radiology·2026
See all related articles

Related Experiment Video

Updated: Nov 2, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.5K

Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compared to

D Racine1, H G Brat2, B Dufour2

  • 1Institute of Radiation Physics (IRA), Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Rue du Grand-Pré 1, 1007 Lausanne, Switzerland.

European Journal of Radiology
|June 13, 2021
PubMed
Summary
This summary is machine-generated.

Deep learning (True Fidelity, TF) image reconstruction preserves noise texture and improves low contrast lesion detectability compared to ASiR-V. TF allows significant radiation dose reduction while maintaining diagnostic image quality for liver lesion detection.

Keywords:
Computed tomographyDeep learningImage qualityModel observerRadiation dose

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.1K
Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease
08:41

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

Published on: March 24, 2023

1.4K

Related Experiment Videos

Last Updated: Nov 2, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.5K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.1K
Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease
08:41

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

Published on: March 24, 2023

1.4K

Area of Science:

  • Medical Imaging
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Iterative reconstruction algorithms are crucial for optimizing CT image quality and radiation dose.
  • Deep learning (DL) based reconstruction methods offer potential advantages over traditional model-based approaches.
  • Evaluating novel DL algorithms like True Fidelity (TF) against established methods such as Adaptive Statistical Iterative Reconstruction - V (ASiR-V) is essential for clinical adoption.

Purpose of the Study:

  • To compare the performance of True Fidelity (TF), a deep learning algorithm, against Adaptive Statistical Iterative Reconstruction - V (ASiR-V) for liver lesion detection.
  • To assess image texture, low contrast lesion detectability, and potential radiation dose reduction offered by TF compared to ASiR-V.
  • To evaluate the impact of different reconstruction strengths on image quality and detectability.

Main Methods:

  • Anthropomorphic phantoms with simulated liver lesions were scanned at various dose levels (2-20 mGy).
  • Images were reconstructed using ASiR-V (0%, 60%) and TF at different strengths.
  • Noise texture was analyzed using Noise Power Spectrum (NPS), Peak Frequency Difference (PFD), and Root Mean Square Deviation (RMSD).
  • Low contrast detectability was evaluated using a channelized Hotelling observer (CHO) and Area Under the Receiver Operating Characteristic curve (AUC).

Main Results:

  • TF preserved FBP-like noise texture more effectively than ASiR-V, resulting in sharper images (lower PFD and RMSD with TF).
  • Area Under the ROC curve (AUC) values were consistently higher with TF reconstruction across all tested strengths.
  • TF demonstrated a potential radiation dose reduction of 7%, 25%, and 33% compared to ASiR-V at low, medium, and high strengths, respectively, while maintaining similar detectability.

Conclusions:

  • True Fidelity (TF) reconstruction, particularly at high strength, effectively maintains noise texture and low contrast liver lesion detectability.
  • TF offers a significant potential for radiation dose reduction in CT liver lesion detection compared to ASiR-V.
  • Deep learning-based reconstruction shows promise for improving diagnostic performance and patient safety in abdominal CT imaging.