FULLY AUTOMATED CARDIAC FUNCTION AND MYOCARDIUM STRAIN ANALYSES USING DEEP LEARNING

    公开(公告)号:US20220338816A1

    公开(公告)日:2022-10-27

    申请号:US17236173

    申请日:2021-04-21

    Abstract: A system and method for cardiac function and myocardial strain analysis include techniques and structure for classifying a set of cardiac images according to their views, detecting a heart range and valid short-axis slices in the set of cardiac images, determining heart segment locations, segmenting heart anatomies for each time frame and each slice, calculating volume related parameters, determining key physiological time points, calculating myocardium transmural thickness and deriving a cardiac function measure from the myocardium transmural thickness at the key physiological time points, estimating a dense motion field from the key physiological time points as applied to the set of cardiac images, calculating myocardial strain along different myocardium directions from the dense motion field, and providing the cardiac function measure and myocardial strain calculation to a user through a user interface.

    MRI RECONSTRUCTION WITH IMAGE DOMAIN OPTIMIZATION

    公开(公告)号:US20220026514A1

    公开(公告)日:2022-01-27

    申请号:US16936571

    申请日:2020-07-23

    Abstract: An apparatus for magnetic resonance imaging (MRI) image reconstruction is provided. The apparatus accesses a training set of MRI data for training. The training set can include paired fully sampled data or unpaired fully sampled data. Undersampled MRI data is optimized in an MRI data optimization module to generate reconstructed MRI data. The apparatus builds a discriminative model using the training set and the reconstructed MRI data. During inference, the parameters of the discriminator model are fixed and the discriminator model is used to classify an output of the MRI data optimization model as the reconstructed MRI image.

    ESTIMATING OBJECT THICKNESS WITH NEURAL NETWORKS

    公开(公告)号:US20210158510A1

    公开(公告)日:2021-05-27

    申请号:US17014573

    申请日:2020-09-08

    Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with estimating a thickness of an anatomical structure based on a visual representation of the anatomical structure and a machine-learned thickness prediction model. The visual representation may include an image or a segmentation mask of the anatomical structure. The thickness prediction model may be learned based on ground truth information derived by applying a partial differential equation such as Laplace's equation to the visual representation and solving the partial differential equation. When the visual representation includes an image of the anatomical structure, the systems, methods and instrumentalities described herein may also be capable of generating a segmentation mask of the anatomical structure based on the image.

    Interactive contour refinements for data annotation

    公开(公告)号:US12232900B2

    公开(公告)日:2025-02-25

    申请号:US17560492

    申请日:2021-12-23

    Abstract: An automated process for data annotation of medical images includes obtaining image data from an imaging sensor, partitioning the image data, identifying an object of interest in the partitioned image data, generating an initial contour with one or more control points with respect to the object of interest, identifying a manual adjustment of one of the control points, automatically adjust a position of at least one other control point within a predetermined range of the manually adjusted control point to a new position, the new position of the at least one other control point and manually adjusted control point defining a new contour, and generating an updated image with the new contour and corresponding control points.

    Systems and methods for enhancing medical images

    公开(公告)号:US12190508B2

    公开(公告)日:2025-01-07

    申请号:US17726383

    申请日:2022-04-21

    Abstract: Described herein are systems, methods, and instrumentalities associated with medical image enhancement. The medical image may include an object of interest and the techniques disclosed herein may be used to identify the object and enhance a contrast between the object and its surrounding area by adjusting at least the pixels associated with the object. The object identification may be performed using an image filter, a segmentation mask, and/or a deep neural network trained to separate the medical image into multiple layers that respectively include the object of interest and the surrounding area. Once identified, the pixels of the object may be manipulated in various ways to increase the visibility of the object. These may include, for example, adding a constant value to the pixels of the object, applying a sharpening filter to those pixels, increasing the weight of those pixels, and/or smoothing the edge areas surrounding the object of interest.

    MOTION DETECTION ASSOCIATED WITH A BODY PART

    公开(公告)号:US20240378731A1

    公开(公告)日:2024-11-14

    申请号:US18195009

    申请日:2023-05-09

    Abstract: Detecting motions associated with a body part of a patient may include using an image sensor installed inside a medical scanner to capture first and second images of the patient inside the medical scanner, wherein the first image may depict the patient in a first state and the second image may depict the patient in a second state. A first area, in the first image, that corresponds to the body part of the patient may be identified and a second area, in the second image, that corresponds to the body part may also be identified so that a first plurality of features may be extracted from the first area of the first image and a second plurality of features may be extracted from the second area of the second image. A motion associated with the body part of the patient may be determined based on the first and second pluralities of features.

Patent Agency Ranking