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Creating a virtual ultra low-dose NLST data set and assessing the impact of dose reduction on nodule detection and characterization

Principal Investigator
Name
Jong Hyo Kim
Degrees
Ph.D.
Institution
Seoul National University
Position Title
Associate Professor
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-105
Initial CDAS Request Approval
Dec 22, 2014
Title
Creating a virtual ultra low-dose NLST data set and assessing the impact of dose reduction on nodule detection and characterization
Summary
We have developed a synthetic sinogram based low-dose CT simulation technique which provides realistic low-dose CT images at multiple dose levels using only D1COM CT images.
We propose to apply our simulation technique to NLST data set to create a virtual ultra low dose dataset and assess the nodule detection and characterization performance according to varying dose reduction rate.
A subset of subjects will be randomly selected and downloaded from NLST database and will be used to create a virtual ultra low-dose NLST dataset.
Dose rates of 50%, 25%, 10% will be used. Three board certified radiologists will participate in reader study for lung nodule detection and characterization with ultra low-dose NLST dataset. Detection performance will be assessed with ROC analysis.
Aims

CT examinations in lung cancer screening need to be performed according to the ALARA principle. Yet, it is difficult to determine an appropriate dose level in which radiation dose in CT examinations is reduced maximally while diagnostic performance is not compromised. Our aim is to determine an appropriate dose level for nodule detection and characterization in asymptomatic screening population using NLST dataset and applying low-dose simulation technique.

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