Deep learning assesses additional radiation dose in overscanning

Following the COVID-19 pandemic, the number of chest CT examinations has dramatically increased, which will undeniably impact public medical exposure. Overscanning, i.e., scanning unnecessary regions in the axial field-of-view, causes noticeable excessive radiation dose to patients undergoing chest CT examinations. The manual procedure of selecting the scan range based on anterior-posterior or lateral localizers is prone to human error in most cases.

In this work, we developed an automated workflow for task-specific scan range selection and retrospective evaluation of overscanning on a large cohort of a multi-centric and multi-purpose clinical database consisting of 20,820 thoracic CT examinations. The impact of overscanning on patients’ effective dose was investigated through personalized dosimetry of the considered cohort. A deep learning-assisted segmentation of the lungs was adopted through lung segmentation on the localizer images to enable choosing the exact scan range to optimize patients’ radiation dose associated with CT examinations. We evaluated our methodology with respect to accuracy in range selection and radiation dose reduction.

A significant overscanning range (31±24) mm was observed in a clinical setting for over 95% of the cases; more considerable in the inferior direction. Our proposed method’s error was -0.7±4.08 and 0.01±14.97 mm for superior and inferior directions, respectively. The evaluation of a sizeable multi-centric chest CT dataset revealed an unnecessary effective dose (ED) of more than two mSv per scan and a 67% increase in the thyroid absorbed dose, which can be eliminated by excluding unjustified body parts from the CT scan range employing this automated deep learning (DL) based method.

Key points

  • Overscanning is a common problem (more than 95% of the cases) occurring mostly in the inferior direction in clinical practice, leading to additional unnecessary radiation dose in chest CT.
  • We developed an accurate and robust automated method for scan range delimitation trained on a large dataset with acceptable reproducibility.
  • Our proposed deep learning-guided algorithm could potentially reduce patient’s radiation dose by up to 21%.

Article: Deep learning-based fully automated Z-axis coverage range definition from scout scans to eliminate overscanning in chest CT imaging

Authors: Yazdan Salimi, Isaac Shiri, Azadeh Akhavanallaf, Zahra Mansouri, Abdollah Saberi Manesh, Amirhossein Sanaat, Masoumeh Pakbin, Dariush Askari, Saleh Sandoughdaran, Ehsan Sharifipour, Hossein Arabi & Habib Zaidi


  • Yazdan Salimi

    Division of Nuclear Medicine and Molecular Imaging, Geneva University, Geneva, Switzerland

Latest posts

Become A Member Today!

You will have access to a wide range of benefits that can help you advance your career and stay up-to-date with the latest developments in the field of radiology. These benefits include access to educational resources, networking opportunities with other professionals in the field, opportunities to participate in research projects and clinical trials, and access to the latest technologies and techniques. 

Check out our different membership options.

If you don’t find a fitting membership send us an email here.


for radiologists, radiology residents, professionals of allied sciences (including radiographers/radiological technologists, nuclear medicine physicians, medical physicists, and data scientists) & professionals of allied sciences in training residing within the boundaries of Europe

  • Reduced registration fees for ECR 1
  • Reduced fees for the European School of Radiology (ESOR) 2
  • Exclusive option to participate in the European Diploma. 3
  • Free electronic access to the journal European Radiology 4
  • Content e-mails for all ESR journals
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

€ 11 /year

Yes! That is less than €1 per month.

Free membership

for radiologists, radiology residents or professionals of allied sciences engaged in practice, teaching or research residing outside Europe as well as individual qualified professionals with an interest in radiology and medical imaging who do not fulfil individual or all requirements for any other ESR membership category & former full members who have retired from all clinical practice
  • Reduced registration fees for ECR 1
  • Free electronic access to the journal European Radiology
  • Content e-mails for all 3 ESR journals 4
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

€ 0

The best things in life are free.

ESR Friends

For students, company representatives or hospital managers etc.

  • Content e-mails for all 3 ESR journals 4
  • Updates on offers & events through our newsletters

€ 0

Friendship doesn’t cost a thing.

The membership type best fitting for you will be selected automatically during the application process.



Reduced registration fees for ECR 2024:
Provided that ESR 2023 membership is activated and approved by August 31, 2023.

Reduced registration fees for ECR 2025:
Provided that ESR 2024 membership is activated and approved by August 31, 2024.

Not all activities included
Examination based on the ESR European Training Curriculum (radiologists or radiology residents).
European Radiology, Insights into Imaging, European Radiology Experimental.