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Variation in Hounsfield unit calculated using dual-energy computed tomography: comparison of dual-layer, dual-source, and fast kilovoltage switching technique.

Shingo OhiraJunji MochizukiTatsunori NiwaKazuyuki EndoMasanari MinamitaniHideomi YamashitaAtsuto KatanoToshikazu ImaeTeiji NishioMasahiko KoizumiKeiichi Nakagawa
Published in: Radiological physics and technology (2024)
The purpose of the study is to investigate the variation in Hounsfield unit (HU) values calculated using dual-energy computed tomography (DECT) scanners. A tissue characterization phantom inserting 16 reference materials were scanned three times using DECT scanners [dual-layer CT (DLCT), dual-source CT (DSCT), and fast kilovoltage switching CT (FKSCT)] changing scanning conditions. The single-energy CT images (120 or 140 kVp), and virtual monochromatic images at 70 keV (VMI 70 ) and 140 keV (VMI 140 ) were reconstructed, and the HU values of each reference material were measured. The difference in HU values was larger when the phantom was scanned using the half dose with wrapping with rubber (strong beam-hardening effect) compared with the full dose without the rubber (reference condition), and the difference was larger as the electron density increased. For SECT, the difference in HU values against the reference condition measured by the DSCT (3.2 ± 5.0 HU) was significantly smaller (p < 0.05) than that using DLCT with 120 kVp (22.4 ± 23.8 HU), DLCT with 140 kVp (11.4 ± 12.8 HU), and FKSCT (13.4 ± 14.3 HU). The respective difference in HU values in the VMI 70 and VMI 140 measured using the DSCT (10.8 ± 17.1 and 3.5 ± 4.1 HU) and FKSCT (11.5 ± 21.8 and 5.5 ± 10.4 HU) were significantly smaller than those measured using the DLCT 120 (23.1 ± 27.5 and 12.4 ± 9.4 HU) and DLCT 140 (22.3 ± 28.6 and 13.1 ± 11.4 HU). The HU values and the susceptibility to beam-hardening effects varied widely depending on the DECT scanners.
Keyphrases
  • dual energy
  • computed tomography
  • image quality
  • positron emission tomography
  • contrast enhanced
  • magnetic resonance imaging
  • deep learning
  • magnetic resonance
  • convolutional neural network