Impact of Noise Level on the Accuracy of Automated Measurement of CT Number Linearity on ACR CT and Computational Phantoms

Choirul Anam*, Riska Amilia, Ariij Naufal, Heri Sutanto, Yanurita Dwihapsari, Toshioh Fujibuchi, Geoff Dougherty

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Background: Methods for segmentation, i.e., Full-segmentation (FS) and Segmentation-rotation (SR), are proposed for maintaining Computed Tomography (CT) number linearity. However, their effectiveness has not yet been tested against noise. Objective: This study aimed to evaluate the influence of noise on the accuracy of CT number linearity of the FS and SR methods on American College of Radiology (ACR) CT and computational phantoms. Material and Methods: This experimental study utilized two phantoms, ACR CT and computational phantoms. An ACR CT phantom was scanned by a 128-slice CT scanner with various tube currents from 80 to 200 mA to acquire various noises, with other constant parameters. The computational phantom was added by different Gaussian noises between 20 and 120 Hounsfield Units (HU). The CT number linearity was measured by the FS and SR methods, and the accuracy of CT number linearity was computed on two phantoms. Results: The two methods successfully segmented both phantoms at low noise, i.e., less than 60 HU. However, segmentation and measurement of CT number linearity are not accurate on a computational phantom using the FS method for more than 60-HU noise. The SR method is still accurate up to 120 HU of noise. Conclusion: The SR method outperformed the FS method to measure the CT number linearity due to its endurance in extreme noise.

Original languageEnglish
Pages (from-to)353-362
Number of pages10
JournalJournal of Biomedical Physics and Engineering
Issue number4
Publication statusPublished - Aug 2023


  • ACR CT Phantom
  • CT Number Linearity
  • Computational Phantom
  • Computed Tomography Scanner
  • Diagnostic Imaging
  • Image Quality Enhancement
  • Noise
  • Quality of Health Care


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