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

Computed Tomography01:10

Computed Tomography

7.9K
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...
7.9K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

241
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
241
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

738
Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
738
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

241
Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
241

You might also read

Related Articles

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

Sort by
Same author

Correction: League of Radiologists-an End-to-End AI Framework for Scalable and Gamified Radiology Education: A Pilot Implementation in Chest Radiography.

Journal of imaging informatics in medicine·2026
Same author

League of Radiologists-an End-to-End AI Framework for Scalable and Gamified Radiology Education: A Pilot Implementation in Chest Radiography.

Journal of imaging informatics in medicine·2026
Same author

Targeted Surface-Enhanced Raman Scattering for Highly Accurate Identification of Bacterial Species and Finding Spectral Signatures with Explainable Artificial Intelligence.

ACS nano·2026
Same author

Enhancing COVID-19 Screening Models With Epidemiological and Mobility Features: Machine-Learning Model Study.

JMIR AI·2026
Same author

Mapping the AI "Mind": What the AI-STREAM Trial Reveals About Cancers Detected and Missed.

Radiology. Artificial intelligence·2025
Same author

Correction: ARANet: Adaptive Resolution Attention Network for Precise MRI-Based Segmentation and Quantification of Fetal Size and Amniotic Fluid Volume.

Journal of imaging informatics in medicine·2025
Same journal

Application of ephrin-B2 loaded glycol chitosan-silk fibroin hydrogel in the treatment of diabetic refractory wounds.

Scientific reports·2026
Same journal

International expert Delphi consensus on thromboprophylaxis in metabolic and bariatric surgery.

Scientific reports·2026
Same journal

Assessing the cross-region knowledge transfer capability of selected deep learning building vectorization methods in the context of available training datasets.

Scientific reports·2026
Same journal

Feasibility and preliminary effects of outdoor versus indoor cognitive-motor therapy in women with Alzheimer's disease: A randomized single-blind pilot study.

Scientific reports·2026
Same journal

Hallmarks of social action in the vocal turn-taking of wild common marmosets (Callithrix jacchus).

Scientific reports·2026
Same journal

Role and mechanism of AOPPs-induced NOX4-mediated ferroptosis in intervertebral disc degeneration.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Jan 4, 2026

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

8.6K

Machine Friendly Machine Learning: Interpretation of Computed Tomography Without Image Reconstruction.

Hyunkwang Lee1,2, Chao Huang1, Sehyo Yune1

  • 1Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.

Scientific Reports
|October 31, 2019
PubMed
Summary
This summary is machine-generated.

Deep learning models can now analyze raw computed tomography (CT) data directly in sinogram space, bypassing image reconstruction. This novel approach, SinoNet, shows promise for medical image analysis, especially in low-dose or field settings.

More Related Videos

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

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

Published on: November 23, 2019

8.4K
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

43.4K

Related Experiment Videos

Last Updated: Jan 4, 2026

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

8.6K
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

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

Published on: November 23, 2019

8.4K
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

43.4K

Area of Science:

  • Medical imaging
  • Deep learning
  • Computed tomography

Background:

  • Deep learning advances medical image analysis but typically uses reconstructed images.
  • Image reconstruction from raw sensor data is an intermediate step, using only partial data.
  • Current methods do not leverage the full information present in raw CT data.

Purpose of the Study:

  • To develop and evaluate a system for direct processing of raw computed tomography (CT) data in sinogram space.
  • To assess the feasibility of sinogram-space machine learning for medical image classification tasks.
  • To compare the performance of sinogram-space deep learning with conventional image-space methods.

Main Methods:

  • Development of SinoNet, a convolutional neural network optimized for sinogram interpretation.
  • Evaluation of SinoNet on two classification tasks: body region identification and intracranial hemorrhage (ICH) detection.
  • Comparison of SinoNet performance against conventional image-space deep learning models using varying scanning geometries and sparse data.

Main Results:

  • SinoNet demonstrated favorable performance in sinogram space compared to image-space systems for both classification tasks.
  • The system performed effectively regardless of scanning geometries (projections, detectors).
  • SinoNet significantly outperformed conventional networks with sparsely sampled sinograms.

Conclusions:

  • Sinogram-space machine learning, using SinoNet, offers a viable alternative to traditional image reconstruction for CT analysis.
  • This approach is particularly suitable for field applications requiring rapid triage, such as ICH detection, and scenarios where low radiation doses are preferred.
  • Deep learning's capability extends to interpreting complex sinogram data, which is challenging for human experts.