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Item does not contain fulltext, PURPOSE: To determine the value of two-dimensional (2D) contrast-enhanced ultrasound (CEUS) imaging and the additional value of contrast ultrasound dispersion imaging (CUDI) for the localization of clinically significant prostate cancer (csPCa). METHODS: In this multicentre study, subjects scheduled for a radical prostatectomy underwent 2D CEUS imaging preoperatively. CUDI maps were generated from the CEUS recordings. Both CEUS recordings and CUDI maps were scored on the likelihood of presenting csPCa (any Gleason≥4n+n3 and Gleason 3n+n4 larger than 0.5 mL) by five observers and compared to radical prostatectomy histopathology. An automated three-dimensional (3D) fusion protocol was used to match imaging with histopathology. Receiver operator curve (ROC) analysis was performed per observer and imaging modality. RESULTS: 133 of 216 (62%) patients were included in the final analysis. Average area under the ROC for all five readers for CEUS, CUDI and the combination was 0.78, 0.79 and 0.78, respectively. This yields a sensitivity and specificity of 81 and 64% for CEUS, 83 and 56% for CUDI and 83 and 55% for the combination. Interobserver agreement for CEUS, CUDI and the combination showed kappa values of 0.20, 0.18 and 0.18 respectively. CONCLUSION: The sensitivity and specificity of 2D CEUS and CUDI for csPCa localization are moderate. Despite compressing CEUS in one image, CUDI showed a similar performance to 2D CEUS. With a sensitivity of 83% at cutoff point 3, it could become a useful imaging procedure, especially with 4D acquisition, improved quantification and combination with other US imaging techniques such as elastography.

Shear-wave elastography (SWE) permits local estimation of tissue elasticity, an important imaging marker in biomedicine. This recently-developed, advanced technique assesses the speed of a laterally-travelling shear wave after an acoustic radiation force "push" to estimate local Young's moduli in an operator-independent fashion. In this work, we show how synthetic SWE (sSWE) images can be generated based on conventional B-mode imaging through deep learning. Using sideby-side-view B-mode/SWE images collected in 50 patients with prostate cancer, we show that sSWE images with a pixel-wise mean absolute error of 4.5±0.96 kPa with regard to the original SWE can be generated. Visualization of high-level feature levels through t-Distributed Stochastic Neighbor Embedding reveals substantial overlap between data from two different scanners. Qualitatively, we examined the use of the sSWE methodology for B-mode images obtained with a scanner without SWE functionality. We also examined the use of this type of network in elasticity imaging in the thyroid. Limitations of the technique reside in the fact that networks have to be retrained for different organs, and that the method requires standardization of the imaging settings and procedure. Future research will be aimed at development of sSWE as an elasticity-related tissue typing strategy that is solely based on B-mode ultrasound acquisition, and the examination of its clinical utility.

Blind Source Separation (BSS) refers to a number of signal processing techniques that decompose a signal into several "source" signals. In recent years, BSS is increasingly employed for the suppression of clutter and noise in ultrasonic imaging. In particular, its ability to separate sources based on measures of independence rather than their temporal or spatial frequency content, makes BSS a powerful filtering tool for data in which the desired and undesired signals overlap in the spectral domain. The purpose of this work was to review existing BSS methods and their potential in ultrasound imaging. Furthermore, we tested and compared the effectiveness of these techniques in the field of contrast-ultrasound superresolution, contrast quantification, and speckle tracking. For all applications, this was done in silico, in vitro, and in vivo. We found that the critical step in BSS filtering is the identification of components containing the desired signal and we highlighted the value of a priori domain knowledge to define effective criteria for signal component selection.

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Item does not contain fulltext, Fertility problems are nowadays being paralleled by important advances in assisted reproductive technologies. Yet the success rate of these technologies remains low. There is evidence that fertilization outcome is affected by uterine motion, but solutions for quantitative analysis of uterine motion are lacking. This work proposes a dedicated method for uterine-motion quantification by B-mode transvaginal ultrasound. Motion analysis is implemented by speckle tracking based on block matching after speckle-size regularization. Sum of absolute differences is the adopted matching metrics. Prior to the analysis, dedicated singular value decomposition (SVD) filtering is implemented to enhance the uterine motion over noise, clutter, and uncorrelated motion induced by neighboring organs and probe movements. SVD and block matching are first optimized by a dedicated ex vivo setup. Robustness to noise and speckle decorrelation is improved by median filtering of the tracking coordinates from surrounding blocks. Speckle tracking is further accelerated by a diamond search. The method feasibility was tested in vivo with a longitudinal study on nine women, aimed at discriminating between four selected phases of the menstrual cycle known to show different uterine behavior. Each woman was scanned in each phase for 4 min; four sites on the uterine fundus were tracked over time to extract strain and distance signals along the longitudinal and transversal directions of the uterus. Several features were extracted from these signals. Among these features, median frequency and contraction frequency showed significant differences between active and quiet phases. These promising results motivate toward an extended validation in the context of fertilization procedures.

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a promising tool for prostate cancer (PCa) localization. Due to the complex acquisition procedure and analysis, along with a questioned diagnostic contribution, the inclusion of DCE-MRI in the updated Prostate Imaging and Reporting Data System (PI-RADS2) has been limited to qualitative assessment only, with a marginal weight on the final score. However, lower diagnostic performance has been reported for PI-RADS2 compared to original PI-RADS, evidencing the need to reconsider the role of DCE-MRI. Recently, magnetic resonance dispersion imaging (MRDI) has shown promise for quantitative DCE-MRI of the prostate. In MRDI, the adopted pharmacokinetic (PK) model includes description of the contrast agent intravascular transport, enabling additional assessment of the microvascular architecture. Compared to standard PK analysis in MRI, separate estimation of an arterial input function is no longer necessary, facilitating the clinical routine. Here we evaluate the diagnostic performance of MRDI for PCa localization compared to standard PK analysis by the Tofts model (TM) in a retrospective study including 80 patients from three different Dutch institutions.