Abstract: A system and method for characterising tissues are provided. The system comprises a point-of-care ultrasound device for obtaining ultrasound images of tissues within a system of interest, a processor, and a memory comprising instructions which when executed by the processor configure the processor to perform the method. The method comprises obtaining an ultrasound image of a tissue types within a system of interest; identifying features of the tissue on the ultrasound image, feeding said identified features to a trained model, and identifying a tissue pathology based on the identified features.

Abstract: A system and method for characterising tissues are provided. The system comprises a processor, and a memory comprising instructions which when executed by the processor, configure the processor to perform the method. The method comprises receiving raw data corresponding to a dense two-dimensional (2D) image or signals arising from a scan of tissues within a system of interest, generating a three-dimensional (3D) data set from the dense 2D image, and inputting the 3D data set into a convolutional network having a plurality of filters. The convolutional network converting the 3D data set into a 1D array corresponding to the frequency domain of the 3D data set, and extracting features form the 1D array and classify the 1D array into a tissue pathology classification.

Abstract: The present disclosure provides for ultrasound systems and methods to pre-process ultrasound data to distinguish abnormal tissue from normal tissue. An exemplary method can include receiving a set of ultrasound data and partitioning the set into a set of windows. The method can then provide for processing the set of windows to determine a power spectrum for each window. The power spectrum for each window can be processed to determine a normalized power spectrum for each window. This normalized power spectrum can be processed for each window with a machine learning model. The method can then provide for displaying an image where each window of the set of windows is displayed using a unique identifier based on the output of the machine learning model.

Abstract: The present disclosure provides for ultrasound systems and methods to pre-process ultrasound data to distinguish abnormal tissue from normal tissue. An exemplary method can include receiving a set of ultrasound data and partitioning the set into a set of windows. The method can then provide for processing the set of windows to determine a power spectrum for each window. The power spectrum for each window can be processed to determine a normalized power spectrum for each window. This normalized power spectrum can be processed for each window with a machine learning model. The method can then provide for displaying an image where each window of the set of windows is displayed using a unique identifier based on the output of the machine learning model.

Abstract: The present disclosure provides for ultrasound systems and methods to pre-process ultrasound data to distinguish abnormal tissue from normal tissue. An exemplary method can include receiving a set of ultrasound data and partitioning the set into a set of windows. The method can then provide for processing the set of windows to determine a power spectrum for each window. The power spectrum for each window can be processed to determine a normalized power spectrum for each window. This normalized power spectrum can be processed for each window with a machine learning model. The method can then provide for displaying an image where each window of the set of windows is displayed using a unique identifier based on the output of the machine learning model.

Abstract: Noninvasive imaging biomarkers to predict cancer treatment response based on early measurements, which would spare non-responding patients from unnecessary side effects and costs of ineffective treatment. Tissue characterization, classification and/or discrimination method is provided to capture different patterns of tissue perfusions. Two or three-dimensional dynamic contrast enhanced ultrasound (DCE US) data of a contrast bolus perfused tissue are acquired or available. Parametric perfusion maps of contrast bolus tissue perfusion parameters representing the DCE US data are generated. For each of the generated parametric perfusion maps statistical parameters are extracted. These statistical parameters, which are based on underlying perfusion characteristics, are first order statistical parameters, second order statistical parameters, or a combination thereof. The method then further classifies and/or discriminates the perfusion maps of the tissue using the extracted statistical parameters.

Abstract: A method for characterizing tissue, contrast agent behavior or microbubble behavior in dynamic contrast enhanced (DCE) medical image time-series data is provided. Time-series sequence of contrast enhanced medical imaging data is acquired during a contrast wash-in or a wash-out. Regions or volumes of interest (ROI/VOI) are selected and from those second order statistics is extracted at each frame of the time-series data. Each extracted second order statistic is assembled over time into a time-statistics curve (TSC). The TSC is normalized to emphasize a shape of the contrast behavior through the ROI or VOI instead of an intensity of the contrast behavior. The tissue, the contrast agent behavior, or the microbubble behavior is then characterized from the time-statistics curve.

Abstract: The present disclosure provides for ultrasound systems and methods to pre-process ultrasound data to distinguish abnormal tissue from normal tissue. An exemplary method can include receiving a set of ultrasound data and partitioning the set into a set of windows. The method can then provide for processing the set of windows to determine a power spectrum for each window. The power spectrum for each window can be processed to determine a normalized power spectrum for each window. This normalized power spectrum can be processed for each window with a machine learning model. The method can then provide for displaying an image where each window of the set of windows is displayed using a unique identifier based on the output of the machine learning model.

Abstract: Guidance and visualization for three-dimensional dynamic contrast-enhanced ultrasound imaging is provided. Anatomical B-mode images and live three-dimensional dynamic contrast-enhanced ultrasound images (3D-DCE-US) of an anatomical region of interest are acquired. An ultrasound imaging probe tracker tracks six degrees of freedom position and orientation data of the ultrasound imaging probe. With reference to the common three-dimensional coordinate frame, and for each of the acquired images, the anatomical B-mode images, the live 3D-DCE-US images, and a three-dimensional computer-generated model are visualized and overlaid with each other. The visualization provides guidance and feedback to a user of the ultrasound imaging probe during three-dimensional dynamic contrast-enhanced ultrasound imaging.

Abstract: The present invention provides a validated method for motion correction with great potential for mitigating motion artifacts in 3D DCE-US. The method is described as a method for motion correction of three-dimensional contrast enhanced ultrasound without the availability of Bmode data. Four-dimensional cine data including three-dimensional contrast enhanced ultrasound image frames are acquired. The acquired three-dimensional contrast enhanced ultrasound image frames are subdivided into groups of similar images referred to as windows. A first pass registration is performed for each of the images in the window to a window representative image. A second pass is performed for each of the registered images from the first pass to a master reference image.