TY - GEN
T1 - Multifrequency Joint Reconstruction of Ultrasonic Attenuation Images
AU - Miranda, Edmundo A.
AU - Basarab, Adrian
AU - Lavarello, Roberto
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The estimation of attenuation coefficient slope (ACS) using the Spectral Log Difference (SLD) technique has presented high variability, leading to the use of regularization approaches. To address this issue, a previous study proposed the isolated denoising of the spectral log ratios at each frequency (TVSLD). In this study, we present a multifrequency joint method (WTNV-SLD) that leverages spatial structures from different frequencies during denoising, using a weighted total nuclear variation (WTNV) to improve the quality of ACS images. The selection of WTNV prior assumes that the spectral ratios across all frequencies are expected to exhibit the same geometrical shape. We compared the performance of the TVSLD and WTNV-SLD methods using mean percentage error (MPE) and Contrast-to-Noise Ratio (CNR) with simulated and physical phantom data. In the simulation, the results showed that WTNV-SLD outperformed TVSLD, achieving higher CNR (5.5 vs. 3.2) and lower MPE in both background (0.46% vs 1.8%) and inclusion (0.25% vs 7.1%) regions. In the physical phantom, WTNV-SLD and TVSLD obtained a similar MPE in the inclusion (0.10% and 0.41%, respectively) and in the background (6.5% and 7.9%, respectively), but achieved a higher CNR (4.1 vs. 2.6). Results suggest exploiting geometrical similarities among frequency channels improves ACS imaging, providing a better trade-off between MPE and CNR.
AB - The estimation of attenuation coefficient slope (ACS) using the Spectral Log Difference (SLD) technique has presented high variability, leading to the use of regularization approaches. To address this issue, a previous study proposed the isolated denoising of the spectral log ratios at each frequency (TVSLD). In this study, we present a multifrequency joint method (WTNV-SLD) that leverages spatial structures from different frequencies during denoising, using a weighted total nuclear variation (WTNV) to improve the quality of ACS images. The selection of WTNV prior assumes that the spectral ratios across all frequencies are expected to exhibit the same geometrical shape. We compared the performance of the TVSLD and WTNV-SLD methods using mean percentage error (MPE) and Contrast-to-Noise Ratio (CNR) with simulated and physical phantom data. In the simulation, the results showed that WTNV-SLD outperformed TVSLD, achieving higher CNR (5.5 vs. 3.2) and lower MPE in both background (0.46% vs 1.8%) and inclusion (0.25% vs 7.1%) regions. In the physical phantom, WTNV-SLD and TVSLD obtained a similar MPE in the inclusion (0.10% and 0.41%, respectively) and in the background (6.5% and 7.9%, respectively), but achieved a higher CNR (4.1 vs. 2.6). Results suggest exploiting geometrical similarities among frequency channels improves ACS imaging, providing a better trade-off between MPE and CNR.
KW - attenuation coefficient slope
KW - joint reconstruction
KW - quantitative ultrasound
KW - total nuclear variation
KW - ultrasonic attenuation images
KW - weighted nuclear norm
UR - http://www.scopus.com/inward/record.url?scp=85178621321&partnerID=8YFLogxK
U2 - 10.1109/IUS51837.2023.10307840
DO - 10.1109/IUS51837.2023.10307840
M3 - Conference contribution
AN - SCOPUS:85178621321
T3 - IEEE International Ultrasonics Symposium, IUS
BT - IUS 2023 - IEEE International Ultrasonics Symposium, Proceedings
PB - IEEE Computer Society
T2 - 2023 IEEE International Ultrasonics Symposium, IUS 2023
Y2 - 3 September 2023 through 8 September 2023
ER -