Trade-off between the radiation parameters and image quality using iterative reconstruction techniques in head computed tomography: a phantom study

Elīza Lāce, Reza Mohammadian (Corresponding Author), Ainārs Āboltiņš, Dāvis Sosārs, Ilze Apine

Research output: Contribution to journalArticlepeer-review

Abstract

Background
Iterative reconstruction techniques (IRTs) are commonly used in computed tomography (CT) and help to reduce image noise.
Purpose
To determine the minimum radiation dose while preserving image quality in head CT using IRTs.
Material and Methods
The anthropomorphic phantom was used to scan nine head CT image series with varied radiation parameters. CT dose parameters, including volume CT dose index (CTDIvol [in mGy]) and dose length product (DLP [in mGy/cm]), were recorded for each scan series. Different noise levels (iDoseL1-6) were used in IRT reconstructions for soft and bone tissues. In total, 15 measurements were taken from five regions of interest (ROI) with an area of 10 mm2. The signal-to-noise ratio (SNR) and noise values obtained at different ROIs were compared among various reconstruction methods with repeated measures of statistical analysis.
Results
In the head CT scan, applying IRT iDoseL5 had the lowest noise and highest SNR for soft tissue (P < 0.05), and increased iDose can decrease CT dose by 54.6% without compromising image quality. While for bone tissue reconstruction, no clear association was found between the level of iDose and noise. However, when CTDIvol is >20 mGy, iDoseL4 is slightly superior to other reconstruction methods (P < 0.065).
Conclusion
Using IRTs in head CTs reduces radiation dose while maintaining image quality. IDoseL5 provided optimal balance for soft tissue.
Original languageEnglish
Pages (from-to)2618-2626
Number of pages9
JournalActa Radiologica
Volume64
Issue number9
DOIs
Publication statusPublished - Sept 2023

Keywords*

  • Dose optimization
  • iDose
  • iterative reconstruction techniques

Field of Science*

  • 3.2 Clinical medicine

Publication Type*

  • 1.1. Scientific article indexed in Web of Science and/or Scopus database

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