TY - GEN
T1 - Total Nuclear Variation Spectral Log Difference for Ultrasonic Attenuation Images
AU - Miranda, Edmundo A.
AU - Basarab, Adrian
AU - Lavarello, Roberto
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Quantitative Ultrasound (QUS) is a non-invasive imaging modality that characterizes tissues numerically. A well-known QUS parameter is the attenuation coefficient slope (ACS). A previous work proposed a regularized spectral log difference method (RSLD) to estimate the ACS, yet the ACS and the backscatter component were computed as independent parameters using a single channel total variation with no joint prior exploited. This work proposes a joint reconstruction method named the Total Nuclear Variation SLD (TNV-SLD). It couples geometrical information of the ACS and the backscatter component to enhance the quality of the images, measured by the mean percentage error (MPE) and contrast-to-noise ratio (CNR). Metrics are compared to the RSLD with data from a simulated and a physical phantom. Initial results show that TNV-SLD can provide comparable CNR values than RSLD but with lower MPE values. In the simulation, RSLD achieved a MPE of 25.4% (inclusion) and 8.1% (background), while TNV-SLD obtained MPE of 15.9% (inclusion) and 2.8% (background). In the real phantom, RSLD achieved a MPE of 37.7% (inclusion) and 1.9% (background), while TNV-SLD obtained MPE of 22.5% (inclusion) and 1.8% (background). Furthermore, TNV-SLD was more robust in terms of the regularization parameter μ, maintaining a more s table MPE and a higher CNR than RSLD for a broader range of μ values, thus surpassing the risk of over-regularizing the images.
AB - Quantitative Ultrasound (QUS) is a non-invasive imaging modality that characterizes tissues numerically. A well-known QUS parameter is the attenuation coefficient slope (ACS). A previous work proposed a regularized spectral log difference method (RSLD) to estimate the ACS, yet the ACS and the backscatter component were computed as independent parameters using a single channel total variation with no joint prior exploited. This work proposes a joint reconstruction method named the Total Nuclear Variation SLD (TNV-SLD). It couples geometrical information of the ACS and the backscatter component to enhance the quality of the images, measured by the mean percentage error (MPE) and contrast-to-noise ratio (CNR). Metrics are compared to the RSLD with data from a simulated and a physical phantom. Initial results show that TNV-SLD can provide comparable CNR values than RSLD but with lower MPE values. In the simulation, RSLD achieved a MPE of 25.4% (inclusion) and 8.1% (background), while TNV-SLD obtained MPE of 15.9% (inclusion) and 2.8% (background). In the real phantom, RSLD achieved a MPE of 37.7% (inclusion) and 1.9% (background), while TNV-SLD obtained MPE of 22.5% (inclusion) and 1.8% (background). Furthermore, TNV-SLD was more robust in terms of the regularization parameter μ, maintaining a more s table MPE and a higher CNR than RSLD for a broader range of μ values, thus surpassing the risk of over-regularizing the images.
KW - joint reconstruction
KW - quantitative ultrasound
KW - total nuclear variation
KW - ultrasonic attenuation imaging
UR - http://www.scopus.com/inward/record.url?scp=85172116289&partnerID=8YFLogxK
U2 - 10.1109/ISBI53787.2023.10230802
DO - 10.1109/ISBI53787.2023.10230802
M3 - Conference contribution
AN - SCOPUS:85172116289
T3 - Proceedings - International Symposium on Biomedical Imaging
BT - 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
PB - IEEE Computer Society
T2 - 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
Y2 - 18 April 2023 through 21 April 2023
ER -