A multiparametric volumetric quantitative ultrasound imaging technique for soft tissue characterization

• Published in: Medical image analysis 2021-12, Vol.74, p.102245-102245, Article 102245
• Main Authors: Deeba, Farah, Schneider, Caitlin, Mohammed, Shahed, Honarvar, Mohammad, Lobo, Julio, Tam, Edward, Salcudean, Septimiu, Rohling, Robert
• Format: Article
• Language: English
• Subjects: Attenuation coefficient estimate; Attenuation coefficients; Backscatter coefficient; Backscattering; Diameters; Effective scatterer diameter; Fatty liver; Hepatic steatosis; Homogeneity; Humans; Image reconstruction; Imaging techniques; In vivo methods and tests; Liver; Liver - diagnostic imaging; Parameters; Phantoms, Imaging; Quantitative ultrasound; Regularization; Regularization methods; Soft tissues; Spatial distribution; Tissues; Ultrasonic imaging; Ultrasonic testing; Ultrasonography; Ultrasound
• ISSN: 1361-8415; 1361-8423
• DOI: 10.1016/j.media.2021.102245

•3D QUS reconstructs multiple quantitative ultrasound parameters.•3D weighted total variation regularization improves reconstruction quality.•Validation on in-vivo liver shows promise for detection of hepatic steatosis. [Display omitted] Quantitative ultrasound (QUS) offers a non-invasive and objective way to quantify tissue health. We recently presented a spatially adaptive regularization method for reconstruction of a single QUS parameter, limited to a two dimensional region. That proof-of-concept study showed that regularization using homogeneity prior improves the fundamental precision-resolution trade-off in QUS estimation. Based on the weighted regularization scheme, we now present a multiparametric 3D weighted QUS (3D QUS) method, involving the reconstruction of three QUS parameters: attenuation coefficient estimate (ACE), integrated backscatter coefficient (IBC) and effective scatterer diameter (ESD). With the phantom studies, we demonstrate that our proposed method accurately reconstructs QUS parameters, resulting in high reconstruction contrast and therefore improved diagnostic utility. Additionally, the proposed method offers the ability to analyze the spatial distribution of QUS parameters in 3D, which allows for superior tissue characterization. We apply a three-dimensional total variation regularization method for the volumetric QUS reconstruction. The 3D regularization involving N planes results in a high QUS estimation precision, with an improvement of standard deviation over the theoretical 1/N rate achievable by compounding N independent realizations. In the in vivo liver study, we demonstrate the advantage of adopting a multiparametric approach over the single parametric counterpart, where a simple quadratic discriminant classifier using feature combination of three QUS parameters was able to attain a perfect classification performance to distinguish between normal and fatty liver cases.