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

You might also read

Related Articles

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

Sort by
Same author

Green radiology (part 1): environmental sustainability and energy consumption in medical imaging, with perspectives from Japan.

Japanese journal of radiology·2026
Same author

Beyond stenosis: Can artificial intelligence redefine stroke risk assessment in contrast-enhanced carotid ultrasound?

European radiology·2026
Same author

Optimizing efficiency in CT services: a data-driven approach using action logs from the radiology information system.

Japanese journal of radiology·2026
Same author

Detection ability of model observer for diagnosis of active cardiac sarcoidosis on <sup>18</sup>F-FDG-PET.

Annals of nuclear medicine·2026
Same author

Facilitating early diagnosis of chronic thromboembolic pulmonary hypertension with dynamic chest radiography: Protocol for a multicenter, assessor-blinded, case-wise randomized superiority reader study (FIND-DCR).

PloS one·2026
Same author

Performance of a self-attention-based model in the task of differentiating clear cell renal cell carcinoma from other renal tumors: variable Vision Transformer (vViT).

The British journal of radiology·2026

Related Experiment Video

Updated: Jan 9, 2026

A Whole Body Dosimetry Protocol for Peptide-Receptor Radionuclide Therapy PRRT: 2D Planar Image and Hybrid 2D+3D SPECT/CT Image Methods
09:49

A Whole Body Dosimetry Protocol for Peptide-Receptor Radionuclide Therapy PRRT: 2D Planar Image and Hybrid 2D+3D SPECT/CT Image Methods

Published on: April 24, 2020

10.4K

A practical slice averaged image method for precise CT size specific dose estimates.

Yutaka Dendo1, Keisuke Abe2, Shu Onodera2

  • 1Department of Radiological Technology, Tohoku University Hospital, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan. yutaka.dendo.c7@tohoku.ac.jp.

Scientific Reports
|December 1, 2025
PubMed
Summary
This summary is machine-generated.

A new method, SSDE slice-averaged image (SSDESAI), simplifies radiation dose estimation in computed tomography (CT). This approach accurately predicts patient radiation dose, offering a practical alternative for clinical settings.

Keywords:
Computed tomographyDose optimizationRadiation doseSize-specific dose estimateWater-equivalent diameter

More Related Videos

Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement
06:33

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

Published on: July 29, 2013

11.7K
Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

3.1K

Related Experiment Videos

Last Updated: Jan 9, 2026

A Whole Body Dosimetry Protocol for Peptide-Receptor Radionuclide Therapy PRRT: 2D Planar Image and Hybrid 2D+3D SPECT/CT Image Methods
09:49

A Whole Body Dosimetry Protocol for Peptide-Receptor Radionuclide Therapy PRRT: 2D Planar Image and Hybrid 2D+3D SPECT/CT Image Methods

Published on: April 24, 2020

10.4K
Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement
06:33

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

Published on: July 29, 2013

11.7K
Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

3.1K

Area of Science:

  • Medical Imaging
  • Radiology
  • Radiation Dosimetry

Background:

  • Computed tomography (CT) is essential for diagnosis, but anatomical variations complicate radiation dose estimation.
  • Conventional dose indices like CTDIvol and DLP are less effective than size-specific dose estimates (SSDE).
  • Calculating water-equivalent diameter (Dw) for each CT slice is laborious and impractical for routine use.

Purpose of the Study:

  • To introduce a novel, simplified method for calculating size-specific dose estimates (SSDE) in CT scans.
  • To evaluate the accuracy and practicality of the SSDE slice-averaged image (SSDESAI) method compared to existing approaches.
  • To assess the SSDESAI method's performance across different anatomical regions.

Main Methods:

  • Developed the SSDE slice-averaged image (SSDESAI) method, calculating Dw from a single averaged CT image.
  • Retrospectively analyzed CT data from 282 adult patients across chest, abdomen-pelvis, and combined chest-abdomen-pelvis regions.
  • Compared SSDESAI with SSDEcenter and mean SSDE using regression analysis and RMSE.

Main Results:

  • SSDESAI demonstrated stronger agreement with mean SSDE than SSDEcenter across all scan regions.
  • Achieved high R2 values (up to 0.991) and lower root mean square error (RMSE) with SSDESAI.
  • The SSDESAI method effectively captures anatomical variability while reducing calculation complexity.

Conclusions:

  • SSDESAI offers a more advanced and practical approach for radiation dose prediction in CT.
  • This method provides a viable alternative to complex calculations for routine clinical application.
  • The findings support the clinical utility of SSDESAI for accurate and efficient dose assessment.