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.

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: Methods and systems for reconstructing an image. For example, a method includes: receiving k-space data; receiving a transform operator corresponding to the k-space data; determining a distribution representing information associated with one or more previous iteration images; generating a next iteration image by an image reconstruction model to reduce an objective function, the objective function corresponding to a data consistency metric and a regularization metric; evaluating whether the next iteration image is satisfactory; and if the next iteration image is satisfactory, outputting the next iteration image as an output image. In certain examples, the data consistency metric corresponds to a first previous iteration image, the k-space data, and the transform operator. In certain examples, the regularization metric corresponds to the distribution. In certain examples, the computer-implemented method is performed by one or more processors.

Abstract: A method includes acquiring magnetic resonance imaging (MRI) data with multi-coil dimensions, compressing the coil dimensions to a fixed and predetermined number of virtual coils, and utilizing the fixed and predetermined number of virtual coils by an artificial intelligence engine for artificial intelligence applications.

Abstract: Described herein are systems, methods and instrumentalities associated with motion tracking and strain determination. A motion tracking apparatus as described herein may track the motion of an anatomical structure from a source image to a target image and determine corresponding points on one or more surfaces of the anatomical structure in both the source image and the target image. Using these surface points, the motion tracking apparatus may calculate one or more strain parameters associated with the anatomical structure and provide the strain parameters for medical diagnosis and/or treatment.

Abstract: Methods and systems for artificial intelligence based medical image segmentation are disclosed. In a method for autonomous artificial intelligence based medical image segmentation, a medical image of a patient is received. A current segmentation context is automatically determined based on the medical image and at least one segmentation algorithm is automatically selected from a plurality of segmentation algorithms based on the current segmentation context. A target anatomical structure is segmented in the medical image using the selected at least one segmentation algorithm.

Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with imagery data processing. The neural networks may be pre-trained to learn parameters or models for processing the imagery data and upon deployment the neural networks may automatically perform further optimization of the learned parameters or models based on a small set of online data samples. The online optimization may be facilitated via offline meta-learning so that the optimization may be accomplished quickly in a few optimization steps.

Abstract: Methods and systems for enhancing a distributed medical network. For example, a computer-implemented method includes inputting training data corresponding to each local computer into their corresponding machine learning model; generating a plurality of local losses including generating a local loss for each machine learning model based at least in part on the corresponding training data; generating a plurality of local parameter gradients including generating a local parameter gradient for each machine learning model based at least in part on the corresponding local loss; generating a global parameter update based at least in part on the plurality of local parameter gradients; and updating each machine learning model hosted at each local computer of the plurality of local computers by at least updating their corresponding active parameter set based at least in part on the global parameter update.

Abstract: Systems and methods for image classification include receiving imaging data of in-vivo or excised tissue of a patient during a surgical procedure. Local image features are extracted from the imaging data. A vocabulary histogram for the imaging data is computed based on the extracted local image features. A classification of the in-vivo or excised tissue of the patient in the imaging data is determined based on the vocabulary histogram using a trained classifier, which is trained based on a set of sample images with confirmed tissue types.

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: Systems and methods for generating and tracking shapes of a target may be provided. The method may include obtaining at least one first resolution image corresponding to at least one of a sequence of time frames of a medical scan. The method may include determining, according to a predictive model, one or more shape parameters regarding a shape of a target from the at least one first resolution image. The method may include determining, based on the one or more shape parameters and a shape model, at least one shape of the target from the at least one first resolution image. The method may further include generating a second resolution visual representation of the target by rendering the determined shape of the target.

Abstract: A system for reconstructing magnetic resonance images includes a processor that is configured to obtain, from a magnetic resonance scanner, sub-sampled k-space data; apply an inverse fast fourier transform to the sub-sampled k-space data to generate a preliminary image; and process the preliminary image via a trained cascaded recurrent neural network to reconstruct a magnetic resonance image.

Abstract: The present disclosure is directed to a method and system for automatically detecting a physiological condition from a medical image of a patient. The method may include receiving the medical image acquired by an imaging device. The method may further include detecting, by a processor, target objects and obtaining the corresponding target object patches from the received medical image. And the method may further include determining, by the processor, a first parameter using a first learning network for each target object patch. The first parameter represents the physiological condition level of the corresponding target object, and the first learning network is trained by adding one or more auxiliary classification layers. This method can quickly, accurately, and automatically predict target object level and/or image (patient) level physiological condition from a medical image of a patient by means of a learning network, such as 3D learning network.

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.

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: Cardiac features captured via an MRI scan may be tracked and analyzed using a system described herein. The system may receive a plurality of MR slices derived via the MRI scan and present the MR slices in a manner that allows a user to navigate through the MR slices. Responsive to the user selecting one of the MR slices, contextual and global cardiac information associated with the selected slice may be determined and displayed. The contextual information may correspond to the selected slice and the global information may encompass information gathered across the plurality of MR slices. A user may have the ability to navigate between the different display areas and evaluate the health of the heart with both local and global perspectives.

Abstract: A method of characterizing a serum and plasma portion of a specimen in regions occluded by one or more labels. The characterization may be used for Hemolysis, Icterus, and/or Lipemia, or Normal detection. The method captures one or more images of a labeled specimen container including a serum or plasma portion, processes the one or more images to provide segmentation data and identification of a label-containing region, and classifying the label-containing region with a convolutional neural network (CNN) to provide a pixel-by-pixel (or patch-by-patch) characterization of the label thickness count, which may be used to adjust intensities of regions of a serum or plasma portion having label occlusion. Optionally, the CNN can characterize the label-containing region as one of multiple pre-defined label configurations. Quality check modules and specimen testing apparatus adapted to carry out the method are described, as are other aspects.

Abstract: A bullseyes plot may be generated based on cardiac magnetic resonance imaging (CMRI) to facilitate the diagnosis and treatment of heart diseases. Described herein are systems, methods, and instrumentalities associated with bullseyes plot generation. A plurality of myocardial segments may be obtained for constructing the bullseye plot based on landmark points detected in short-axis and long-axis magnetic resonance (MR) slices of the heart and by arranging the short-axis MR slices sequentially in accordance with the order in which the slices are generated during the CMRI. The sequential order of the short-axis MR slices may be determined utilizing projected locations of the short-axis MR slices on a long-axis MR slice and respective distances of the projected locations to a landmark point of the long-axis MR slice. The myocardium and/or landmark points may be identified in the short-axis and/or long-axis MR slices using artificial neural networks.

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: Described herein are neural network-based systems, methods and instrumentalities associated with estimating the motion of an anatomical structure. The motion estimation may be performed using a feature pyramid and/or a motion pyramid that correspond to multiple image scales. The motion estimation may be performed using neural networks and parameters that are learned via a training process involving a student network and a teacher network pre-pretrained with abilities to apply progressive motion compensation.