Machine learning for the prediction of prostate cancer biopsy based on 3D dynamic contrast-enhanced ultrasound quantification:2018 IEEE International Ultrasonics Symposium (IUS)

Non-targeted transrectal-ultrasound-guided 12-core systematic biopsy (SBx) is the current guideline-recommended clinical pathway for prostate cancer (PCa) diagnosis, despite being associated with a risk of complications as well as un-derdiagnosis or overtreatment. Quantification algorithms for dynamic contrast-enhanced ultrasound (DCE-US) have shown good potential for PCa localisation in two dimensions (2D), and a few have recently been expanded to 3D. In this work, we present a 3D implementation of all estimators in the contrast ultrasound dispersion imaging (CUDI) family and exploit combinations of the extracted parameters to predict individual SBx-core outcomes. We show that machine-learning approaches can improve the classification performance compared to individual CUDI parameters and foresee potential for further development in image-based PCa localisation

3-D quantitative dynamic contrast ultrasound for prostate cancer localization

\u3cp\u3eTo investigate quantitative 3-D dynamic contrast-enhanced ultrasound (DCE-US) and, in particular 3-D contrast-ultrasound dispersion imaging (CUDI), for prostate cancer detection and localization, 43 patients referred for 10-12-core systematic biopsy underwent 3-D DCE-US. For each 3-D DCE-US recording, parametric maps of CUDI-based and perfusion-based parameters were computed. The parametric maps were divided in regions, each corresponding to a biopsy core. The obtained parameters were validated per biopsy location and after combining two or more adjacent regions. For CUDI by correlation (r) and for the wash-in time (WIT), a significant difference in parameter values between benign and malignant biopsy cores was found (p < 0.001). In a per-prostate analysis, sensitivity and specificity were 94% and 50% for r, and 53% and 81% for WIT. Based on these results, it can be concluded that quantitative 3-D DCE-US could aid in localizing prostate cancer. Therefore, we recommend follow-up studies to investigate its value for targeting biopsies.\u3c/p\u3

Probabilistic 3D contrast-ultrasound tractography based on a a convective-dispersion finite-element scheme

Three-dimensional dynamic contrast-enhanced ultrasound imaging opens the door for the characterisation of vascular networks. This work focusses on a stable finite-element algorithm to retrieve local properties of the vascularity based on the convection-dispersion equation. We show that, even at low spatial and temporal resolution, local convective-dispersion behaviour can be captured. Moreover, we present a probabilistic tractography strategy to visualise convective pathways and provide an in-vivo example

Three-dimensional estimation of ultrasound-contrast-agent dispersion and convection in the prostate

Prostate cancer (PCa) diagnosis still relies on systematic biopsy due to the insufficient reliability of imaging techniques. In this work, we introduce a new method to quantify contrast agent convective dispersion (D) and velocity (v) in a three-dimensional dynamic contrast-enhanced ultrasound (3D DCE-US) recording. First, the concentration gradients of the data are established using Gaussian derivatives in space and time. Assuming D and v to be locally constant, the convective-dispersion equation can then be solved by minimizing a leastsquares problem within a moving 3D kernel. Six PCa patients underwent two-minute 3D DCE-US examination prior to radical prostatectomy. Subsequently, the 3D parametric maps of D and v were compared with the 3D histopathologic data. Preliminary results suggest a good correlation between malignancy according to histopathology and both D and v maps

Contrast-enhanced ultrasound tractography for 3D vascular imaging of the prostate

\u3cp\u3eDiffusion tensor tractography (DTT) enables visualization of fiber trajectories in soft tissue using magnetic resonance imaging. DTT exploits the anisotropic nature of water diffusion in fibrous structures to identify diffusion pathways by generating streamlines based on the principal diffusion vector. Anomalies in these pathways can be linked to neural deficits. In a different field, contrast-enhanced ultrasound is used to assess anomalies in blood flow with the aim of locating cancer-induced angiogenesis. Like water diffusion, blood flow and transport of contrast agents also shows a principal direction; however, this is now determined by the local vasculature. Here we show how the tractographic techniques developed for magnetic resonance imaging DTT can be translated to contrast-enhanced ultrasound, by first estimating contrast flow velocity fields from contrast-enhanced ultrasound acquisitions, and then applying tractography. We performed 4D in-vivo contrast-enhanced ultrasound of three human prostates, proving the feasibility of the proposed approach with clinically acquired datasets. By comparing the results to histopathology after prostate resection, we observed qualitative agreement between the contrast flow tracts and typical markers of cancer angiogenic microvasculature: higher densities and tortuous geometries in tumor areas. The method can be used in-vivo using a standard contrast-enhanced ultrasound protocol, opening up new possibilities in the area of vascular characterization for cancer diagnostics.\u3c/p\u3

Contrast-enhanced ultrasound tractography for 3D vascular imaging of the prostate

Diffusion tensor tractography (DTT) enables visualization of fiber trajectories in soft tissue using magnetic resonance imaging. DTT exploits the anisotropic nature of water diffusion in fibrous structures to identify diffusion pathways by generating streamlines based on the principal diffusion vector. Anomalies in these pathways can be linked to neural deficits. In a different field, contrast-enhanced ultrasound is used to assess anomalies in blood flow with the aim of locating cancer-induced angiogenesis. Like water diffusion, blood flow and transport of contrast agents also shows a principal direction; however, this is now determined by the local vasculature. Here we show how the tractographic techniques developed for magnetic resonance imaging DTT can be translated to contrast-enhanced ultrasound, by first estimating contrast flow velocity fields from contrast-enhanced ultrasound acquisitions, and then applying tractography. We performed 4D in-vivo contrast-enhanced ultrasound of three human prostates, proving the feasibility of the proposed approach with clinically acquired datasets. By comparing the results to histopathology after prostate resection, we observed qualitative agreement between the contrast flow tracts and typical markers of cancer angiogenic microvasculature: higher densities and tortuous geometries in tumor areas. The method can be used in-vivo using a standard contrast-enhanced ultrasound protocol, opening up new possibilities in the area of vascular characterization for cancer diagnostics

ULTRA-SR Challenge: Assessment of Ultrasound Localization and TRacking Algorithms for Super-Resolution Imaging

With the widespread interest and uptake of super-resolution ultrasound (SRUS) through localization and tracking of microbubbles, also known as ultrasound localization microscopy (ULM), many localization and tracking algorithms have been developed. ULM can image many centimeters into tissue in-vivo and track microvascular flow non-invasively with sub-diffraction resolution. In a significant community effort, we organized a challenge, Ultrasound Localization and TRacking Algorithms for Super-Resolution (ULTRA-SR). The aims of this paper are threefold: to describe the challenge organization, data generation, and winning algorithms; to present the metrics and methods for evaluating challenge entrants; and to report results and findings of the evaluation. Realistic ultrasound datasets containing microvascular flow for different clinical ultrasound frequencies were simulated, using vascular flow physics, acoustic field simulation and nonlinear bubble dynamics simulation. Based on these datasets, 38 submissions from 24 research groups were evaluated against ground truth using an evaluation framework with six metrics, three for localization and three for tracking. In-vivo mouse brain and human lymph node data were also provided, and performance assessed by an expert panel. Winning algorithms are described and discussed. The publicly available data with ground truth and the defined metrics for both localization and tracking present a valuable resource for researchers to benchmark algorithms and software, identify optimized methods/software for their data, and provide insight into the current limits of the field. In conclusion, Ultra-SR challenge has provided benchmarking data and tools as well as direct comparison and insights for a number of the state-of-the art localization and tracking algorithms