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Principal Investigator
Name
Anton Schreuder
Degrees
M.D.
Institution
Radboudumc
Position Title
M.D. Ph.D. student
Email
About this Project
Study
NLST (Learn more about this study)
Project ID
NLST-422
Title
Normalizing emphysema quantification on CT using a new reconstruction algorithm
Summary

Beyond the detection of lung cancer, objective assessment of emphysema on chest CT is also of great interest. Emphysema severity is associated with poor lung function and an increased risk of lung cancer and death. However, automatic emphysema quantification on CT is known to vary with radiation dose, reconstruction algorithms, slice thickness, vender, type of scanner, and respiration. Therefore, it is impossible to make meaningful comparisons between emphysema quantifications from scans obtained with different parameters and scanners for longitudinal and multi-center studies.
Recently, a new reconstruction algorithm has been developed to reduce image noise and maintain image quality in a novel way compared to previous algorithms. Using our reconstruction algorithm, we aim to test and validate its performance in CT image normalization for emphysema quantification.

Aims

• To examine whether normalization of emphysema quantification is possible using our new reconstruction algorithm.
• To compare our normalization method with existing normalization methods of emphysema quantification.
• To test the lung cancer and all-cause mortality prediction accuracy of our new emphysema score.

Collaborators

Wataru Fukumoto, Radiology and nuclear medicine, Radboud University Medical Center, the Netherlands
Bram van Ginneken, Diagnostic Image Analysis Group, Radboud University Medical Center Nijmegen, Netherlands
Joep Kamps, Diagnostic Image Analysis Group, Radboud University Medical Center Nijmegen, Netherlands
Mathias Prokop, Radiology and nuclear medicine, Radboud University Medical Center, the Netherlands
Cornelia Schaefer-Prokop, Radiology and nuclear medicine, Radboud University Medical Center, the Netherlands
Colin Jacobs, Diagnostic Image Analysis Group, Radboud University Medical

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