57 results about "2d ultrasound" patented technology

A LUS robotic surgical system is trainable by a surgeon to automatically move a LUS probe in a desired fashion upon command so that the surgeon does not have to do so manually during a minimally invasive surgical procedure. A sequence of 2D ultrasound image slices captured by the LUS probe according to stored instructions are processable into a 3D ultrasound computer model of an anatomic structure, which may be displayed as a 3D or 2D overlay to a camera view or in a PIP as selected by the surgeon or programmed to assist the surgeon in inspecting an anatomic structure for abnormalities. Virtual fixtures are definable so as to assist the surgeon in accurately guiding a tool to a target on the displayed ultrasound image.

A LUS robotic surgical system is trainable by a surgeon to automatically move a LUS probe in a desired fashion upon command so that the surgeon does not have to do so manually during a minimally invasive surgical procedure. A sequence of 2D ultrasound image slices captured by the LUS probe according to stored instructions are processable into a 3D ultrasound computer model of an anatomic structure, which may be displayed as a 3D or 2D overlay to a camera view or in a PIP as selected by the surgeon or programmed to assist the surgeon in inspecting an anatomic structure for abnormalities. Virtual fixtures are definable so as to assist the surgeon in accurately guiding a tool to a target on the displayed ultrasound image.

An ultrasound based technique for detecting and imaging vibrations in tissue caused by eddies produced during bleeding through punctured arteries or from organs. A clutter signal, normally suppressed in conventional color flow imaging, is employed to detect and characterize local tissue vibrations, to detect internal bleeding in an image, or as an audible or palpable signal, or a readout. Using a tissue vibration image, the origin and extent of vibrations relative to the underlying anatomy and blood flow can be visualized in real time, enabling measurements of vibration amplitude, frequency, and spatial distribution. Bleeding rate can be determined from the frequency and amplitude of the vibrations. Signal processing algorithms usable to identify tissue vibrations from an ensemble of 2D ultrasound data include those based on phase decomposition, spectral estimation using eigendecomposition, and spectral estimation using autoregressive modeling for isolating vibrations from clutter, blood flow, and noise.

A LUS robotic surgical system is trainable by a surgeon to automatically move a LUS probe in a desired fashion upon command so that the surgeon does not have to do so manually during a minimally invasive surgical procedure. A sequence of 2D ultrasound image slices captured by the LUS probe according to stored instructions are processable into a 3D ultrasound computer model of an anatomic structure, which may be displayed as a 3D or 2D overlay to a camera view or in a PIP as selected by the surgeon or programmed to assist the surgeon in inspecting an anatomic structure for abnormalities. Virtual fixtures are definable so as to assist the surgeon in accurately guiding a tool to a target on the displayed ultrasound image.

The present invention relates to a 3D ultrasound diagnostic forming a 3D ultrasound image only with volume data exiting within contour by automatically detecting the contour of a target object, comprising: a first unit for generating a region of interest (ROI) box on a 2D ultrasound image; a second unit for detecting a contour of a target object in the ROI box; and a third unit for forming a 3D ultrasound image by rendering volume data existing in the detected contour.

Characterization of carotid atherosclerosis and classification of plaque into symptomatic or asymptomatic and risk score estimation are of clinical value. A statistical system is described for symptomatic versus asymptomatic plaque automated classification of carotid ultrasound images and cardiovascular risk score computation. The technique is applicable for the following types of modalities for carotids: 2D Ultrasound, 3D Ultrasound, CT, MR. Wall region is segmented and features are extracted consisting of type 1 combination consisting of: (a) Higher Order Spectra; (b) Discrete Wavelet Transform (DWT); (c) Texture and (d) Wall Variability; type 2 combination consisting of: (a) Local Binary Pattern; (b) Law's Mask Energy and (c) Wall Variability and type 3 combination: (a) Trace Transform; (b) Fuzzy Grayscale Level Co-occurrence Matrix and (c) Wall Variability. These features are trained using a training classifier on training images and the coefficients are applied to on-line test patient images. The system yields the cardiovascular risk score value using the feature combinations.

A method and system for real-time ultrasound guided prostate needle biopsy based on a biomechanical model of the prostate from 3D planning image data, such as magnetic resonance imaging (MRI) data, is disclosed. The prostate is segmented in the 3D ultrasound image. A reference patient-specific biomechanical model of the prostate extracted from planning image data is fused to a boundary of the segmented prostate in the 3D ultrasound image, resulting in a fused 3D biomechanical prostate model. In response to movement of an ultrasound probe to a new location, a current 2D ultrasound image is received. The fused 3D biomechanical prostate model is deformed based on the current 2D ultrasound image to match a current deformation of the prostate due to the movement of the ultrasound probe to the new location.

The present invention relates a method and an apparatus for enhancing ultrasound image quality through a post-processing. A method for enhancing an image quality of a two-dimensional (2D) ultrasound image, comprises the steps of: a) decomposing the 2D ultrasound image into a plurality of images having a multi-resolution by N levels, wherein N is a positive integer; b) determining characteristics of each pixel in the decomposed image; c) performing an enhancement process for the decomposed image based on the pixel characteristics; d) performing 1st-level composition for the decomposed image; and e) repeatedly performing steps b) to d) until a size of the composed image is identical to that of the 2D ultrasound image.