TY - GEN
T1 - Visibility of noise texture changes in CT images
AU - Oostveen, L. J.
AU - Boedeker, K.
AU - Shin, D.
AU - Abbey, C. K.
AU - Sechopoulos, I.
N1 - Publisher Copyright:
© 2022 SPIE. All rights reserved.
PY - 2022
Y1 - 2022
N2 - Noise texture in CT images, commonly characterized by using the noise power spectrum (NPS), is mainly dictated by the shape of the reconstruction kernel. The peak frequency of the NPS (fpeak) is often used as a one-parameter metric for characterizing noise texture. However, if the downslope of the NPS beyond the fpeak influences noise texture visibly, then fpeak is insufficient as a single descriptor. Therefore, we investigated the human-detectable differences in NPSs having different fpeak and/or downslope parameters. NPSs were estimated using various reconstruction kernels on a commercial CT scanner. To quantify NPS downslope, half of a Gaussian function was fit through the NPS portion that lies beyond fpeak. The α of this Gaussian was used as the downslope descriptor of the NPS. A two alternative forced choice observer study was performed to determine the justnoticeable-differences (JND) in fpeak only, α only, and both simultaneously. Visibility thresholds for these changes were determined and an elliptical limiting detectability boundary was determined. The JND threshold ellipse is centered on the reference values and has a major and minor radius of 0.47 lp/cm and 0.12 lp/cm, respectively. The major radius makes an angle of 143° with the x-Axis. A change in only fpeak of 0.2 lp/cm is below the detection threshold. This number changes if the apodization part of the NPS changes simultaneously. In conclusion, both the peak frequency and the apodization section of the NPS influence the detectability of changes in image noise texture. 2022 SPIE.
AB - Noise texture in CT images, commonly characterized by using the noise power spectrum (NPS), is mainly dictated by the shape of the reconstruction kernel. The peak frequency of the NPS (fpeak) is often used as a one-parameter metric for characterizing noise texture. However, if the downslope of the NPS beyond the fpeak influences noise texture visibly, then fpeak is insufficient as a single descriptor. Therefore, we investigated the human-detectable differences in NPSs having different fpeak and/or downslope parameters. NPSs were estimated using various reconstruction kernels on a commercial CT scanner. To quantify NPS downslope, half of a Gaussian function was fit through the NPS portion that lies beyond fpeak. The α of this Gaussian was used as the downslope descriptor of the NPS. A two alternative forced choice observer study was performed to determine the justnoticeable-differences (JND) in fpeak only, α only, and both simultaneously. Visibility thresholds for these changes were determined and an elliptical limiting detectability boundary was determined. The JND threshold ellipse is centered on the reference values and has a major and minor radius of 0.47 lp/cm and 0.12 lp/cm, respectively. The major radius makes an angle of 143° with the x-Axis. A change in only fpeak of 0.2 lp/cm is below the detection threshold. This number changes if the apodization part of the NPS changes simultaneously. In conclusion, both the peak frequency and the apodization section of the NPS influence the detectability of changes in image noise texture. 2022 SPIE.
KW - CT
KW - Image perception
KW - noise power spectrum
KW - Noise texture
UR - http://www.scopus.com/inward/record.url?scp=85131881129&partnerID=8YFLogxK
U2 - 10.1117/12.2610736
DO - 10.1117/12.2610736
M3 - Conference contribution
AN - SCOPUS:85131881129
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2022
A2 - Mello-Thoms, Claudia R.
A2 - Mello-Thoms, Claudia R.
A2 - Taylor-Phillips, Sian
PB - SPIE
T2 - Medical Imaging 2022: Physics of Medical Imaging
Y2 - 21 March 2022 through 27 March 2022
ER -