An ultrasound imaging device (10) with an ultrasound probe (12) acquires a live ultrasound image which is displayed with a contour (62) or reference image (60) registered with the live ultrasound image using a composite transform (42). To update the composite transform, the ultrasound imaging device acquires a baseline three-dimensional ultrasound (3D-US) image (66) tagged with a corresponding baseline orientation of the ultrasound probe measured by a probe tracker, and one or more reference 3D-US images (70) each tagged with a corresponding reference orientation. Transforms (54) are computed to spatially register each reference 3D-US image with the baseline 3D-US image. A closest reference 3D-US image is determined whose corresponding orientation is closest to a current orientation of the ultrasound probe as measured by the probe tracker. The composite transform is updated to include the transform to spatially register the closest reference 3D-US image to the baseline 3D-US image.

An image processing apparatus includes an acquisition unit configured to acquire first medical image data and second medical image data obtained by imaging a subject, an intermediate deformation information acquisition unit configured to acquire intermediate deformation information obtained by applying registration processing up to a predetermined stage in first registration processing including a plurality of stages to the acquired first medical image data and second medical image data, a determination unit configured to perform determination of a deformation abnormality with respect to the acquired intermediate deformation information, and a deformation unit configured to perform, in a case where the determination unit determines that there is the deformation abnormality, second registration processing different from the first registration processing with respect to the first medical image data and the second medical image data and calculate deformation information.

A method for characterizing tissue of a patient, including receiving acoustic data derived from the interaction between the tissue and the acoustic waves irradiating the tissue; generating a morphology rendering of the tissue from the acoustic data, in which the rendering represents at least one biomechanical property of the tissue; determining a prognostic parameter for a region of interest in the rendering, in which the prognostic parameter incorporates the biomechanical property; and analyzing the prognostic parameter to characterize the region of interest. In some embodiment, the method further includes introducing a contrast agent into the tissue; generating a set of enhanced morphology renderings of the tissue after introducing the contrast agent; determining an enhanced prognostic parameter from the enhanced morphology renderings; and analyzing the enhanced prognostic parameter.

There is provided a program causing a computer to execute processing including: acquiring an endoscopic image of a subject from an endoscope; acquiring a three-dimensional medical image obtained by capturing an image of an internal body portion of the subject by means of X-ray CT, X-ray cone beam CT, MRI-CT, or an ultrasonic diagnosis apparatus configured to capture a three-dimensional image of an internal body portion of the subject; generating a virtual endoscopic image reconfigured from the three-dimensional medical image, based on the acquired endoscopic image; calculating distance image information in the endoscopic image based on the virtual endoscopic image and the endoscopic image; and outputting operation assistance information on an operation of the endoscope based on the distance image information and the three-dimensional medical image.

The disclosure relates to a method for determining a boundary about an area of interest in an image set. The includes obtaining the image set from an imaging modality and processing the image set in a convolutional neural network. The convolutional neural network is trained to perform the acts of predicting an inverse distance map for the actual boundary in the image set; and deriving the boundary from the inverse distance map. The disclosure also relates to a method of training a convolutional neural network for use in such a method, and a medical imaging arrangement.

An apparatus and method for processing an ultrasound image in various sensor conditions are provided. The method includes receiving a data cube via sensors, transforming the received data cube into focus data through beam focusing, and outputting inphase data and quadrature phase data for the focus data using a neural network corresponding to signal adder and Hilbert transform functions. The method further includes detecting an envelope of the inphase data and the quadrature phase data and reconstructing an ultrasound image for the data cube using log compression.

An ultrasonic image is obtained from a bias-switchable row-column array transducer. A row channel data set is obtained by applying a bias voltage pattern to groups of row electrodes, the bias voltage pattern being chosen such that row electrodes within each group have the same bias voltage; transmitting a waveform along each of the plurality of row electrodes; and recording received column signals from each of the plurality of column electrodes. A column channel data set is obtained by applying a bias voltage pattern to groups of column electrodes, the bias voltage pattern being chosen such that column electrodes within each group have the same bias voltage; transmitting a waveform along each of the plurality of column electrodes; and recording received row signals from each of the plurality of row electrodes.

A medical image diagnosis apparatus according to an embodiment includes a processing circuit. The processing circuit is configured to obtain three-dimensional data related to a target site. The processing circuit is configured to generate a three-dimensional model of the target site by using the obtained three-dimensional data. The processing circuit is configured to calculate positions of one or more recommended cross-sections to be set for the target site, on the basis of information about the size of the target site obtained by using the three-dimensional model. The processing circuit is configured to cause a display device to display the positions of the one or more recommended cross-sections.

2019PF00643 32 ABSTRACT Disclosed is an ultrasound roadmap image generation system. The system includes a processor configured for communication with an ultrasound imaging device that is movable relative to a patient. The processor receives a first bi-plane or 3D image representative of a first volume within the patient and a second bi-plane or 3D image representative of a second volume within the patient. The processor then registers the first bi-plane or 3D image and the second bi-plane or 3D image to determine the motion between the first bi-plane or 3D image and the second bi-plane or 3D image. The processor then generates a 2D roadmap image of a region of interest by combining the first bi-plane or 3D image and the second bi-plane or 3D image, based on the determined motion; and outputs a screen display comprising the 2D roadmap image.

An ultrasonic imaging system has a bias-switchable, ultrasonic transducer array and a bipolar voltage source. The array has a dielectric layer having a top surface and a bottom surface; top and bottom electrode strips in electrical contact with the top and bottom surface of the dielectric layer, the bottom electrode strips being oriented at a non-zero angle relative to the top electrode strips. There is an acoustic matching layer or multiplicity of matching layers on the front-side of the array and a leakage-current mitigation layer. The bipolar voltage source is connected to each of the top and bottom electrode strips to induce a polarization in the dielectric layer, the bipolar voltage source being capable of switching between a high voltage state and a low voltage state. A controller controls the bipolar voltage source, and pulsing to and receiving signals from the top and bottom electrode strips.