Abstract: The present disclosure relates to a system. The system may obtain a k-space dataset according to magnetic resonance (MR) signals acquired by a magnetic resonance imaging (MRI) scanner. The system may also generate, based on the k-space dataset using an image reconstruction model that includes a sequence sub-model and a domain translation sub-model, a reconstructed image by: inputting at least a part of the k-space dataset into the sequence sub-model; outputting, from the sequence sub-model, a feature representation of the k-space dataset; inputting the feature representation of the k-space dataset into the domain translation sub-model; and outputting, from the domain translation sub-model, the reconstructed image.

Abstract: A standalone image reconstruction device is configured to reconstruct the raw signals received from a radiology scanner device into a reconstructed output signal. The image reconstruction device is a vendor neutral interface between the radiology scanner device and the post processing imaging device. The reconstructed output signal is a user readable domain that can be used to generate a medical image or a three-dimensional (3D) volume. The apparatus is configured to reconstruct signals from different types of radiology scanner devices using any suitable image reconstruction protocol.

Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with cardiac assessment. An apparatus as described herein may obtain electrocardiographic imaging (ECGI) information associated with a human heart and magnetic resonance imaging (MRI) information associated with the human heart, and integrate the ECGI and MRI information using a machine-learned model. Using the integrated ECGI and MRI information, the apparatus may predict target ablation sites, estimate electrophysiology (EP) measurements, and/or simulate the electrical system of the human heart.

Abstract: Data samples are transmitted from a central server to at least one local server apparatus. The central server receives a set of predictions from the at least one local server apparatus that are based on the transmitted set of data samples. The central server trains a central model based on the received set of predictions. The central model, or a portion of the central model corresponding to a task of interest, can then be sent to the at least one local server apparatus. Neither local data from local sites nor trained models from the local sites are transmitted to the central server. This ensures protection and security of data at the local sites.

Abstract: Method and system for displaying one or more regions of interest of an original image. For example, a computer-implemented method for displaying one or more regions of interest of an original image includes: obtaining one or more detection results of one or more first regions of interest, each detection result of the one or more detection results corresponding to one first region of interest of the one or more first regions of interest, each detection result including image information and one or more attribute parameters for their corresponding first region of interest; and obtaining one or more attribute parameter thresholds provided by a user in real time, each attribute parameter threshold of the one or more attribute parameter thresholds corresponding to one attribute parameter of the one or more attribute parameters.

Abstract: Healthcare services can be automated utilizing a system that recognizes at least one characteristic of a patient based on images of the patient acquired by an image capturing device. Relying on information extracted from these images, the system may automate multiple aspects of a medical procedure such as patient identification and verification, positioning, diagnosis and/or treatment planning using artificial intelligence or machine learning techniques. By automating these operations, healthcare services can be provided remotely and/or with minimum physical contact between the patient and a medical professional.

Abstract: Methods and systems for providing guidance for adjusting a target. For example, a computer-implemented method for providing guidance for adjusting a target includes: receiving, by a neural network, a reference image; receiving, by the neural network, the target image, the target image being related to a position of a target; determining a similarity metric based at least in part on information associated with the reference image and information associated with the target image by the neural network; generating a target attention map corresponding to the target image based at least in part on the similarity metric; outputting the target image and the target attention map; and providing a guidance for adjusting the position of the target based at least in part on the target image and the target attention map.

Abstract: A system for image reconstruction in magnetic resonance imaging (MRI) is provided. The system may obtain undersampled k-space data associated with an object, wherein the undersampled K-space data may be generated based on magnetic resonance (MR) signals collected by an MR scanner that scans the object. The system may construct an ordinary differential equation (ODE) that formulates a reconstruction of an MR image based on the undersampled k-space data. The system may further generate the MR image of the object by solving the ODE based on the undersampled k-space data using an ODE solver.

Abstract: Methods and systems for determining a motion amplitude of an object being scanned. For example, a computer-implemented method for determining a motion amplitude of an object being scanned includes determining an internal parameter set based at least in part on calibrating the scanning apparatus according to a first predetermined algorithm; determining an external parameter set based at least in part on a testing surface and the internal parameter set; determining a mapping relationship between a pixel coordinate system and a world coordinate system based at least in part on the determined internal parameter set and the determined external parameter set; determining a motion amplitude of the object in the pixel coordinate system; and determining a motion amplitude of the object in the world coordinate system based at least in part on the determined mapping relationship and the motion amplitude of the object in the pixel coordinate system.

Abstract: A method for MRI reconstruction is provided. The method may include obtaining a plurality of sub-sampled images of a subject. The plurality of sub-sampled images may include a first sub-sampled image of the subject and one or more second sub-sampled images of the subject. The first sub-sampled image may be generated using a first MRI sequence and a first sub-sampling rate. Each of the one or more second sub-sampled images may be generated using a second MRI sequence and a second sub-sampling rate. The second sub-sampling rate may be smaller than the first sub-sampling rate. The method may include obtaining an image reconstruction model having been trained according to a machine learning technique. The method may further include generating a first full image of the subject corresponding to the first MRI sequence based on the first sub-sampled image, the one or more second sub-sampled images, and the image reconstruction model.

Abstract: A method for image processing may be provided. The method may include obtaining a 3D image of a subject and an ROI within the subject. The method may also include generating a 3D segmentation image relating to the ROI of the subject based on the 3D image. The method may also include selecting an MPR plane from the 3D image. The method may further include determining a target 2D image of the MPR plane based on the 3D image and the 3D segmentation image. The target 2D image of the MPR plane may include a bounding box annotating the ROI on the MPR plane.

Abstract: A system for medical diagnosis training is provided. The system may receive, from a user terminal, a training request inputted by a user. In response to the training request, the system may obtain a medical image for training the user and a reference diagnostic result with respect to the medical image, and transmit the medical image to the user terminal. The system may receive, from the user terminal, a diagnostic result with respect to the medical image inputted by the user. The system may further generate an evaluation result of the diagnostic result based on the reference diagnostic result.

Abstract: A system for generating a bullseye plot of a heart of a subject is provided. The system may obtain multiple slice images in a plurality of groups, wherein each group corresponds to one of a plurality of sections of the heart and includes at least one slice image of the corresponding section, and each slice image includes part of the right ventricle, part of the left ventricle, and part of the myocardium. The system may also identify at least one landmark associated with the left ventricle by applying a landmark detection network in each of the slice images. The system may further generate the bullseye plot of the heart based on the at least one landmark identified in each of the multiple slice images, wherein the bullseye plot includes a plurality of sectors, each of which represents an anatomical region of the myocardium in one of the plurality of sections.

Abstract: The present disclosure generally provides systems and methods for situation awareness. When executing a set of instructions stored in at least one non-transitory storage medium, at least one processor may be configured to cause the system to perform operations including obtaining, from at least one of one or more sensors, environmental data associated with an environment corresponding to a first time point, generating a first static global representation of an environment corresponding to the first time point based at least in part on the environmental data, generating a first dynamic global representation of the environment corresponding to the first time point based at least in part on the environmental data, and estimating, based on the first static global representation and the first dynamic global representation, a target state of the environment at a target time point using a target estimation model.

Abstract: A method includes using fully sampled retro cine data to train an algorithm, and applying the trained algorithm to real time MR cine data to yield reconstructed MR images.

Abstract: A method for automated calibration is provided. The method may include obtaining a plurality of interest points based on prior information regarding a device and image data of the device captured by a visual sensor. The method may include identifying at least a portion of the plurality of interest points from the image data of the device. The method may also include determining a transformation relationship between a first coordinate system and a second coordinate system based on information of at least a portion of the identified interest points in the first coordinate system and in the second coordinate system that is applied to the visual sensor or the image data of the device.

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.

Abstract: A medical system may utilize a modular and extensible sensing device to derive a two-dimensional (2D) or three-dimensional (3D) human model for a patient in real-time based on images of the patient captured by a sensor such as a digital camera. The 2D or 3D human model may be visually presented on one or more devices of the medical system and used to facilitate a healthcare service provided to the patient. In examples, the 2D or 3D human model may be used to improve the speed, accuracy and consistency of patient positioning for a medical procedure. In examples, the 2D or 3D human model may be used to enable unified analysis of the patient's medical conditions by linking different scan images of the patient through the 2D or 3D human model. In examples, the 2D or 3D human model may be used to facilitate surgical navigation, patient monitoring, process automation, and/or the like.

Abstract: A patient's healthcare experience may be enhanced utilizing a system that automatically recognizes the patient based on one or more images of the patient and generates personalized medical assistance information for the patient based on electronic medical records stored for the patient. Such electronic medical records may comprise imagery data and/or non-imagery associated with a medical procedure performed or to be performed for the patient. As such, the imagery and/or non-imagery data may be incorporated into the personalized medical assistance information to provide positioning and/or other types of diagnostic or treatment guidance to the patient or a service provider.

Abstract: Described herein are systems, methods and instrumentalities associated with image segmentation. The systems, methods and instrumentalities have a hierarchical structure for producing a coarse segmentation of an anatomical structure and then refining the coarse segmentation based on a shape prior of the anatomical structure. The coarse segmentation may be generated using a multi-task neural network and based on both a segmentation loss and a regression loss. The refined segmentation may be obtained by deforming the shape prior using one or more of a shape-based model or a learning-based model.