Shanghai United Imaging Intelligence Co., Ltd.

Disclosed herein are systems, methods, and instrumentalities associated with reconstructing a 3D representation of an anatomical structure based on a screen recording of a display while medical images of the anatomical structure are shown on the display. A plurality of medical images of the anatomical structure may be extracted from the screen recording, and one or more parameters for reconstructing the 3D representation of the anatomical structure based on the extracted medical images may be determined. The 3D representation of the anatomical structure may then be reconstructed based on the extracted medical images and the one or more determined parameters. From the 3D representation of the anatomical structure, abnormalities may be detected, and medical reporting may be performed using pre-trained machine-learning models.

A method for coronary road mapping may include receiving a current image associated with a medical scan, determining an image that matches the current image from a sequence of images for which respective blood vessel maps have been determined, and overlaying the current image with the blood vessel map associated with the matching image. The sequence of images may be captured when a contrast agent was present, and the matching image may be identified in response to determining that the physiological phase (e.g., cardiac cycle) and/or view depicted by the image match those depicted by the current image. Motion compensation may be performed as part of the blood vessel overlay, which may enable better visualization of blood vessels and medical device(s) (e.g., a catheter) placed in the blood vessels even when the contrast agent is worn out.

Multiple predictions about the position of an object during a time period may each indicate the position of the object at a respective time during the time period. Respective validity indications corresponding to the multiple predictions may each indicate an accuracy of the corresponding prediction. Whether a change has occurred in a distribution of the predictions from a first subset of predictions to a second subset of predictions during the time period may be determined. If the change has occurred, a prediction from the first subset of predictions or the second subset of predictions may be selected, based on the validity of the predictions and/or the detection of a motion, as a best indication of the position of the object.

Described herein are method, system, and apparatus for correlating medical images with a medical report. An apparatus may obtain a plurality of medical images of a patient and a medical report for the patient, wherein the plurality of medical images may be associated with respective sequence labels and respective view labels, and wherein the medical report may include multiple diagnoses. The apparatus may determine and fuse respective image-type embeddings and image-content embeddings for the plurality of medical images into a combined image feature representation. The apparatus may further encode the features of the medical report into a text feature representation and calculate a similarity score based on the image feature representation and the text feature representation to indicate a correlation between the medical images and the medical report.

An object or person in a medical environment may be identified based on images of the medical environment. The identification may include determining an identifier associated with the object or the person, a position of the object or the person in the medical environment, and a three-dimensional (3D) shape/pose of the object or the person. Representation information that indicates at least the determined identifier, position in the medical environment, and 3D shape/pose of the object or the person may be generated and then used (e.g., by a visualization device) together with one or more predetermined 3D models to determine a 3D model for the object or the person identified in the medical environment and generate a visual depiction of at least the object or the person in the medical environment based on the determined 3D model and the position of the object or the person in the medical environment.

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.

The motion estimation of an anatomical structure may be performed using a machine-learned (ML) model trained based on medical training images of the anatomical structure and corresponding segmentation masks for the anatomical structure. During the training of the ML model, the model may be used to predict a motion field that may indicate a change between a first training image and a second training image, and to transform the first training image and a corresponding first segmentation mask based on the motion field. The parameters of the ML model may then be adjusted to maintain a correspondence between the transformed first training image and the second training image and between the transformed first segmentation mask or a second segmentation mask associated with the second training image. The correspondence may be assessed based on at least a boundary region shared by the anatomical structure and one or more other anatomical structures.

Disclosed is a method and a system for automatic positioning of a medical equipment with respect to a patient. The method includes obtaining sensor data related to the patient, from a plurality of sensors fixed relative to the medical equipment. The method further includes processing the sensor data to determine at least one pose characteristic of the patient and at least one shape characteristic of the patient. The method further includes determining at least one adjustment parameter for the medical equipment based on the at least one pose characteristic of the patient and the at least one shape characteristic of the patient. The method further includes adjusting the medical equipment based on the at least one adjustment parameter.

Disclosed herein are systems, methods, and instrumentalities associated with MRI image reconstruction. According to embodiments of the disclosure, an apparatus configured to perform the MRI image reconstruction task may be configured to obtain an under-sampled MRI image and generate a reconstructed MRI image based on the under-sampled MRI image and a machine-learned (ML) model. The ML model may be trained via contrastive learning, during which randomly selected locations of the reconstructed MRI data generated by the ML model may be replaced with values that are different than the under-sampled MRI data, and the MRI data thus derived may be used as a negative example for the training.

The present disclosure provides a system for image reconstruction. The system may obtain an initial image of a subject. The initial image may be generated based on scan data of the subject that is collected by an imaging device. The system may also generate a gradient image associated with the initial image. The system may further generate a target image of the subject by applying an image reconstruction model based on the initial image and the gradient image. The target image may have a higher image quality than the initial image.