Relative Elastic Modulus Imaging Using Sector Ultrasound Data for Abdominal Applications: An Evaluation of Strategies and Feasibility

Characterizing Mechanical Properties of Soft Tissues Using Non-contact Displacement Measurements: How Should We Assess the Uncertainty?

Techniques aimed at the non-invasive characterization of soft tissues according to elastic properties are rapidly evolving. Virtual touch-based elastographic methods including acoustic radiation force imaging (ARFI) and optical elastography measure the peak axial displacement (PD) and time-to-peak-displacement (TTP) of tissue in response to a localized force. These measurements have been used clinically to differentiate tissues, albeit with mixed results. However, to date, the reason has not been fully understood. In this study, we apply a novel modeling approach to explore the mechanistic link between simplistic displacement measurements and tissue viscoelasticity in the application of virtual touch-based elastographic methods to staging chronic liver disease (CLD). To our knowledge, such a study has not been reported in the literature. Specifically, a numerical screening study was first conducted to identify factors that most strongly determine PD and TTP. Response surface experimental designs were then applied to these factors to produce meta-models of expected PD and TTP probability density functions (PDFs) as functions of identified factors. Results from the screening study suggest that both PD and TTP measurements are primarily influenced by three factors: the initial Young's modulus of the tissue, the first viscoelastic Prony series time constant, and pre-compression applied during acquisition. To investigate the implications of these results, stochastic inputs for these three factors associated were used to determine a robust response surface. The identified response surface methodology can be used to determine optimal cutoff values for PD and TTP that could be used in order to stage chronic liver disease.

Digital image technology based on PCA and SVM for detection and recognition of foreign bodies in lyophilized powder

BACKGROUND:

Digital image technology has made great progress in the field of foreign body detection and classification, which is of great help to drug purity extraction and impurity analysis and classification.

OBJECTIVE:

The detection and classification of foreign bodies in lyophilized powder are important. The method which can obtain a higher accuracy of recognition needs to be proposed.

METHODS:

We used digital image technology to detect and classify foreign bodies in lyophilized powder, and studied the process of image preprocessing, median filtering, Wiener filtering and average filtering balance to better detect and classify foreign bodies in lyophilized powder.

RESULTS:

Through industrial small sample data simulation, test results show that in the process of image preprocessing, 3 × 3 median filtering is best. In the aspect of foreign body recognition, the recognition based on principal component analysis (PCA) and support vector machine (SVM) algorithm and the recognition based on PCA and Third-Nearest Neighbor classification algorithm are compared and results show that the PCA+SVM algorithm is better.

CONCLUSION:

We demonstrated that integrating PCA and SVM to classify foreign bodies in lyophilized powder. Our proposed method is effective for the prediction of essential proteins.

Physiological Motion Reduction Using Lagrangian Tracking for Electrode Displacement Elastography

Minimally invasive treatments such as microwave ablation (MWA) have been growing in popularity for extending liver cancer survival rates in patients, when surgery is not an option. As a non-ionizing, real-time alternative to contrast-enhanced computed tomography, electrode displacement elastography (EDE) has shown promise as an imaging modality for MWA. Despite imaging efficacy, motion artifacts caused by physiological motion result in unintended speckle pattern variance, thereby inhibiting consistent and accurate ablated region visualization. To combat these unavoidable motion artifacts, a Lagrangian deformation tracking (LDT) approach based on freehand EDE was developed to track tissue movement and better define tissue properties. For validating LDT efficacy, a spherical inclusion phantom as well as seven in vivo data sets were processed, and strain tensor images were compared with identical time sampled images estimated using a traditional Eulerian approach. In vivo results revealed greater consistency among visualized LDT strain tensor images, with segmented ablated regions exhibiting standard deviation reductions of up to 98% when compared with Eulerian strain tensor images. Additionally, Lagrangian strain tensor images provided Dice coefficient improvements up to 25%, and success rates improved from approximately 50% to nearly 100% for ablated region visualization.

Comparison of Displacement Tracking Algorithms for in Vivo Electrode Displacement Elastography

Hepatocellular carcinoma and liver metastases are common hepatic malignancies presenting with high mortality rates. Minimally invasive microwave ablation (MWA) yields high success rates similar to surgical resection. However, MWA procedures require accurate image guidance during the procedure and for post-procedure assessments. Ultrasound electrode displacement elastography (EDE) has demonstrated utility for non-ionizing imaging of regions of thermal necrosis created with MWA in the ablation suite. Three strategies for displacement vector tracking and strain tensor estimation, namely coupled subsample displacement estimation (CSDE), a multilevel 2-D normalized cross-correlation method, and quality-guided displacement tracking (QGDT) have previously shown accurate estimations for EDE. This paper reports on a qualitative and quantitative comparison of these three algorithms over 79 patients after an MWA procedure. Qualitatively, CSDE presents sharply delineated, clean ablated regions with low noise except for the distal boundary of the ablated region. Multilevel and QGDT contain more visible noise artifacts, but delineation is seen over the entire ablated region. Quantitative comparison indicates CSDE with more consistent mean and standard deviations of region of interest within the mass of strain tensor magnitudes and higher contrast, while Multilevel and QGDT provide higher CNR. This fact along with highest success rates of 89% and 79% on axial and lateral strain tensor images for visualization of thermal necrosis using the Multilevel approach leads to it being the best choice in a clinical setting. All methods, however, provide consistent and reproducible delineation for EDE in the ablation suite.

Building an open-source simulation platform of acoustic radiation force-based breast elastography

Ultrasound-based elastography including strain elastography, acoustic radiation force impulse (ARFI) imaging, point shear wave elastography and supersonic shear imaging (SSI) have been used to differentiate breast tumors among other clinical applications. The objective of this study is to extend a previously published virtual simulation platform built for ultrasound quasi-static breast elastography toward acoustic radiation force-based breast elastography. Consequently, the extended virtual breast elastography simulation platform can be used to validate image pixels with known underlying soft tissue properties (i.e. 'ground truth') in complex, heterogeneous media, enhancing confidence in elastographic image interpretations. The proposed virtual breast elastography system inherited four key components from the previously published virtual simulation platform: an ultrasound simulator (Field II), a mesh generator (Tetgen), a finite element solver (FEBio) and a visualization and data processing package (VTK). Using a simple message passing mechanism, functionalities have now been extended to acoustic radiation force-based elastography simulations. Examples involving three different numerical breast models with increasing complexity-one uniform model, one simple inclusion model and one virtual complex breast model derived from magnetic resonance imaging data, were used to demonstrate capabilities of this extended virtual platform. Overall, simulation results were compared with the published results. In the uniform model, the estimated shear wave speed (SWS) values were within 4% compared to the predetermined SWS values. In the simple inclusion and the complex breast models, SWS values of all hard inclusions in soft backgrounds were slightly underestimated, similar to what has been reported. The elastic contrast values and visual observation show that ARFI images have higher spatial resolution, while SSI images can provide higher inclusion-to-background contrast. In summary, our initial results were consistent with our expectations and what have been reported in the literature. The proposed (open-source) simulation platform can serve as a single gateway to perform many elastographic simulations in a transparent manner, thereby promoting collaborative developments.