Document Type


Publication Date



Lippincott, Williams & Wilkins

Source Publication

Journal of Computer Assisted Tomography

Source ISSN


Original Item ID

DOI: 10.1097/RCT.0000000000001312



This work aimed to retrospectively evaluate the potential of dose reduction on chest computed tomography (CT) examinations by reducing the longitudinal scan length for patients positive for coronavirus disease 2019 (COVID-19).


This study used the Personalized Rapid Estimation of Dose in CT (PREDICT) tool to estimate patient-specific organ doses from CT image data. The PREDICT is a research tool that combines a linear Boltzmann transport equation solver for radiation dose map generation with deep learning algorithms for organ contouring. Computed tomography images from 74 subjects in the Medical Imaging Data Resource Center–RSNA International COVID-19 Open Radiology Database data set (chest CT of adult patients positive for COVID-19), which included expert annotations including “infectious opacities,” were analyzed. First, the full z-scan length of the CT image data set was evaluated. Next, the z-scan length was reduced from the left hemidiaphragm to the top of the aortic arch. Generic dose reduction based on dose length product (DLP) and patient-specific organ dose reductions were calculated. The percentage of infectious opacities excluded from the reduced z-scan length was used to quantify the effect on diagnostic utility.


Generic dose reduction, based on DLP, was 69%. The organ dose reduction ranged from approximately equal to 18% (breasts) to approximately equal to 64% (bone surface and bone marrow). On average, 12.4% of the infectious opacities were not included in the reduced z-coverage, per patient, of which 5.1% were above the top of the arch and 7.5% below the left hemidiaphragm.


Limiting z-scan length of chest CTs reduced radiation dose without significantly compromising diagnostic utility in COVID-19 patients. The PREDICT demonstrated that patient-specific organ dose reductions varied from generic dose reduction based on DLP.


Accepted version. Journal of Computer-Assisted Tomography, Vol. 46, No. 4 (July/August 2022): 576-583. DOI. © 2022 Lippincott Williams & Wilkins, Inc. Used with permission.

gilat-schmidt_15847acc.docx (568 kB)
ADA Accessible Version