Personalized CT Organ Dose Estimation from Scout Images

Abdullah Al Zubaer Imran, Sen Wang, Debashish Pal, Sandeep Dutta, Bhavik Patel, Evan Zucker, Adam Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


With the rapid increase of CT usage, radiation dose across patient populations is also increasing. Therefore, it is desirable to reduce the CT radiation dose. However, the reduction in dose also incurs additional noise and with the degraded image quality, diagnostic performance can be compromised. Existing routine dosimetric quantities are usually based on absorbed dose within cylindrical phantoms and do not appropriately represent the actual patient dose. More comprehensive dose metrics such as effective dose require estimation of patient-specific dose at an organ level. Unfortunately, currently available systems are quite far from achieving this goal as well as limited by a number of manual adjustments, time-consuming and inefficient procedures. To overcome all these challenges in achieving the goal of patient safety through reduced dose without compromising image quality, we devise a fully-automated, end-to-end deep learning-based solution to perform real-time, patient-specific, organ-level dosimetric prediction of CT scans. Leveraging the 2D scout (frontal and lateral) images of the actual patients, which are routinely acquired prior to the CT scan, our proposed Scout-Net model estimates the patient-specific mean dose in real-time for six different organs. Our experimental evaluation on real patient data demonstrates the effectiveness of our Scout-Net model not only in real-time dose estimation (only 11 ms on average per scan), but also as a potential tool for optimizing CT radiation dose in specific patients.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
EditorsMarleen de Bruijne, Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
Number of pages11
StatePublished - 2021
Event24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: Sep 27 2021Oct 1 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12904 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • CNN
  • Computed tomography
  • Ionizing radiation
  • Organ dose
  • Scout images
  • Segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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