Abstract: The present disclosure provides a system and method for fetus monitoring. The method may include obtaining ultrasound data relating to a fetus collected by an ultrasound imaging device; generating a 4D image of the fetus based on the ultrasound data; directing a display component of a virtual reality (VR) device to display the 4D image to an operator; detecting motion of the fetus based on the ultrasound data; and directing a haptic component of the VR device to provide haptic feedback with respect to the motion to the operator.

Abstract: Described herein are systems, methods, and instrumentalities associated with generating a multi-dimensional representation of a medical environment based on images of the medical environments. Various pre-processing and/or post-processing operations may be performed to supplement and/or improve the multi-dimensional representation. These operations may include determining semantic information associated with the medical environment based on the images and adding the semantic information to the multi-dimensional representation in addition to space and time information. The operations may also include anonymizing a person presented in the multi-dimensional representation, adding synthetic views to the multi-dimensional representation, improving the quality of the multi-dimensional representation, etc. The multi-dimensional representation of the medical environment generated using these techniques may allow a user to experience and explore the medical environment, for example, via a virtual reality device.

Abstract: A system for Magnetic Resonance Imaging (MRI) is provided. The system may obtain at least one training sample each of which includes full MRI data. The system may also obtain a preliminary subsampling model and a preliminary MRI reconstruction model. The system may further generate a subsampling model corresponding to an MRI reconstruction model by jointly training the preliminary subsampling model and the preliminary MRI reconstruction model using the at least one training sample. The subsampling model may be the trained preliminary subsampling model, and the MRI reconstruction model may be at least a portion of the trained preliminary MRI reconstruction model.

Abstract: Described herein are systems, methods, and instrumentalities associated with tracking groups of small objects in medical images. The tracking may be accomplished by, for each one of a sequence of medical images, determining a plurality of candidate objects captured in the medical image, grouping the plurality of candidate objects into a plurality of groups of candidate objects and dividing the medical image into a plurality of regions that each surrounds a corresponding group of candidate objects. Each of the plurality of regions may be examined to extract respective features associated with each corresponding group of candidate objects. A match between a first group of candidate objects in a first medical image and a second group of candidate objects in a second medical image may be determined based on first features associated with the first group and second features associated with the second group.

Abstract: Described herein are systems, methods, and instrumentalities associated with estimating the motions of multiple 3D points in a scene and predicting a view of scene based on the estimated motions. The tasks may be accomplished using one or more machine-learning (ML) models. A first ML model may be used to predict motion-embedding features for a temporal state of a scene, based on motion-embedding features for previous states. A second ML model may be used to predict a motion field representing displacement or deformation of the multiple 3D points from a source time to a target time. Then, a third ML model may be used to predict respective image properties of the 3D points based on their updated locations at the target time and/or a viewing direction. An image of the scene at the target time may then be generated based on the predicted image properties of the 3D points.

Abstract: A non-invasive biometric system includes a processor that is configured to control a scanner, which is configured to scan and capture one or more anatomical images of a body of a target person. The processor is further configured to identify one or more anatomical structures in the captured one or more anatomical images and extract anatomical features for the identified one or more anatomical structures. The processor is further configured to register the extracted anatomical features for the identified one or more identified anatomical structures to a posture and an external appearance of the target person. The processor is further configured to encode and utilize the extracted anatomical features as biometric data, which is unique for the target person, and may be used for authentication of the target person.

Abstract: A system for providing rehabilitation in a virtual environment includes an extended reality (XR) headset to present a first rehabilitation therapy to a patient in a virtual environment. A sensing device is configured to track physical movements of the patient and a processor is configured to receive the sensing data to determine pose information. The processor is configured to determine a performance metric associated with the physical movements and compare the performance metric with a reference metric to determine whether the patient has successfully performed the defined physical movements. The processor is configured to change the first rehabilitation therapy to a second rehabilitation therapy based on a difference between the performance metric and the reference metric upon determining that the patient has unsuccessfully performed the defined physical movements. The system aids the patient by changing the rehabilitation therapies according to the performance of the patient.

Abstract: A system for patient positioning is provided. The system may acquire image data relating to a patient holding a posture and a plurality of patient models. Each patient model may represent a reference patient holding a reference posture, and include at least one reference interest point of the referent patient and a reference representation of the reference posture. The system may also identify at least one interest point of the patient from the image data using an interest point detection model. The system may further determine a representation of the posture of the patient based on a comparison between the at least one interest point of the patient and the at least one reference interest point in each of the plurality of patient models.

Abstract: Methods and systems for guiding a patient for a medical examination using a medical apparatus. For example, a computer-implemented method for guiding a patient for a medical examination using a medical apparatus includes: receiving an examination protocol for the medical apparatus; determining a reference position based at least in part on the examination protocol; acquiring a patient position; determining a deviation metric based at least in part on comparing the patient position and the reference position; determining whether the deviation metric is greater than a pre-determined deviation threshold; and if the deviation metric is greater than a pre-determined deviation threshold: generating a positioning guidance based at least in part on the determined deviation metric, the positioning guidance including guidance for positioning the patient relative to the medical apparatus.

Abstract: A system for image segmentation is provided. The system may obtain a target image including an ROI, and segment a preliminary region representative of the ROI from the target image using a first ROI segmentation model corresponding to a first image resolution. The system may segment a target region representative of the ROI from the preliminary region using a second ROI segmentation model corresponding to a second image resolution. At least one model of the first and second ROI segmentation models may at least include a first convolutional layer and a second convolutional layer downstream to the first convolutional layer. A count of input channels of the first convolutional layer may be greater than a count of output channels of the first convolutional layer, and a count of input channels of the second convolutional layer may be smaller than a count of output channels of the second convolutional layer.

Abstract: Described herein are systems, methods, and instrumentalities associated with reconstruction of multi-contrast magnetic resonance imaging (MRI) images. The reconstruction may be performed based on under-sampled MRI data collected for the multiple contrasts using corresponding sampling patterns. The sampling patterns and the reconstruction operations for the multiple contrasts may be jointly optimized using deep learning techniques implemented through one or more neural networks. An end-to-end reconstruction optimizing framework is provided with which information collected while processing one contrast may be stored and used for another contrast. A differentiable sampler is described for obtaining the under-sampled MRI data from a k-space and a novel holistic recurrent neural network is used to reconstruct MRI images based on the under-sampled MRI data.

Abstract: Described herein are systems, methods, and instrumentalities associated with processing complex-valued MRI data using a machine learning (ML) model. The ML model may be learned based on synthetically generated MRI training data and by applying one or more meta-learning techniques. The MRI training data may be generated by adding phase information to real-valued MRI data and/or by converting single-coil MRI data into multi-coil MRI data based on coil sensitivity maps. The meta-learning process may include using portions of the training data to conduct a first round of learning to determine updated model parameters and using remaining portions of the training data to test the updated model parameters. Losses associated with the testing may then be determined and used to refine the model parameters. The ML model learned using these techniques may be adopted for a variety of tasks including, for example, MRI image reconstruction and/or de-noising.

Abstract: Disclosed is a system and a method for configuring an IP device to be discoverable to a client device over a local network having a DHCP server for assigning dynamic IP addresses. The method includes obtaining a dynamic IP address assigned to the IP device upon completion of boot process for the IP device; checking if a static IP address has been set for the IP device; determining if the dynamic IP address and the static IP address are in a same subnet of the local network; implementing the static IP address set for the IP device, if the dynamic IP address and the static IP address are in the same subnet of the local network; and implementing the dynamic IP address assigned to the IP device, if the dynamic IP address and the static IP address are not in the same subnet of the local network.

Abstract: An unsupervised machine learning method with self-supervision losses improves a slice-wise spatial resolution of 3D medical images with thick slices, and does not require high resolution images as the ground truth for training. The method utilizes information from high-resolution dimensions to increase a resolution of another desired dimension.

Abstract: Described herein are systems, methods, and instrumentalities associated with automatic image annotation. The annotation may be performed based on one or more manually annotated first images of an object and a machine-learned (ML) model trained to extract first features from the one or more first images. To automatically annotate a second, un-annotated image of the object, the ML model may be used to extract second features from the second image, determine information that may be indicative of the characteristics of the object in the second image based on the first and second features, and generate an annotation of the object for the second image using the determined information. The images may be obtained from various sources including, for example, sensors and/or medical scanners, and the object of interest may include anatomical structures such as organs, tumors, etc. The annotated images may be used for multiple purposes including machine learning.

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.

Abstract: Human mesh model recovery may utilize prior knowledge of the hierarchical structural correlation between different parts of a human body. Such structural correlation may be between a root kinematic chain of the human body and a head or limb kinematic chain of the human body. Shape and/or pose parameters relating to the human mesh model may be determined by first determining the parameters associated with the root kinematic chain and then using those parameters to predict the parameters associated with the head or limb kinematic chain. Such a task can be accomplished using a system comprising one or more processors and one or more storage devices storing instructions that, when executed by the one or more processors, cause the one or more processors to implement one or more neural networks trained to perform functions related to the task.

Abstract: A system for physiological motion measurement is provided. The system may acquire a reference image corresponding to a reference motion phase of an ROI and a target image of the ROI corresponding to a target motion phase, wherein the reference motion phase may be different from the target motion phase. The system may identify one or more feature points relating to the ROI from the reference image, and determine a motion field of the feature points from the reference motion phase to the target motion phase using a motion prediction model. An input of the motion prediction model may include at least the reference image and the target image. The system may further determine a physiological condition of the ROI based on the motion field.

Abstract: The disclosure relates to systems and methods for magnetic resonance imaging (MRI). A method may include obtaining k-space data associated with MR signals acquired by an MR scanner. The k-space data may corresponding to a first sampling rate. The method may also include generating one or more estimated images based on the k-space data and a target neural network model. The one or more estimated images may correspond to a second sampling rate that exceeds the first sampling rate. The method may further include determining one or more target images based on the one or more estimated images and the k-space data using a compressed sensing model. The compressed sensing model may be constructed based on the one or more estimated images.

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.