Abstract: A video of medical scan images associated with an anatomical structure may be arranged into multiple image pairs. The multiple image pairs may be provided to a machine learning (ML) model successively and the ML model may determine respective first sets of image features associated with the multiple image pairs and, for each of the multiple image pairs, refine the first set of image features associated with the image pair based on the respective first sets of image features associated with one or more other image pairs. A motion field associated with the image pair may be determined based at least on the refined first set of image features associated with the image pair and a task may be performed based on the respective motion fields.

Abstract: A storage assembly includes a heat dissipation device and a storage. The heat dissipation device includes a thermally conductive block, a heat sink and a plurality of heat pipes. An end of each of the heat pipes is thermally coupled with the thermally conductive block, and another end of each of the heat pipes is thermally coupled with the heat sink. The storage is thermally coupled with the thermally conductive block.

Abstract: Disclosed herein are systems, methods, and instrumentalities associated with medical image denoising. An apparatus configured to perform the medical image denoising task may be configured to obtain a medical image of an object and separate the medical image into a background layer and a foreground layer. The apparatus may then denoise the background layer using a first neural network pre-trained to suit the characteristics of the background layer, denoise the foreground layer using a second neural network pre-trained to suit the characteristics of the foreground layer, and merge the denoised background layer and the denoised foreground layer back into a clean medical image that depicts the object with improved image quality.

Abstract: Deblurring and denoising a medical image such as X-ray fluoroscopy images may be challenging, and deep-learning based techniques may be employed to meet the challenge. An artificial neural network (ANN) may be trained using training images with synthetic noise and as well as training images with real noise. The parameters of the ANN may be adjusted during the training based on at least a first loss designed to maintain continuity between consecutive medical images generated by the ANN and a second loss designed to maintain similarity of patches inside a medical image generated by the ANN. The parameters of the ANN may be further adjusted based on a third loss that may be calculated from ground truth associated with the synthetic training images. Transfer learning between the synthetic images and the real images may be accomplished using these techniques.

Abstract: 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.

Abstract: A slurry blending tool may include a blending tank to receive and blend one or more materials into a slurry, and at least one inlet pipe connected to the blending tank and to provide the one or more materials to the blending tank. The at least one inlet pipe may vertically enter the blending tank and may not contact the blending tank. The slurry blending tool may include a blending pump partially provided within the blending tank and to blend the one or more materials into the slurry. The slurry blending tool may include an outlet pipe connected to the blending pump and to remove the slurry from the blending tank.

Abstract: 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.

Abstract: Described herein are systems, methods, and instrumentalities associated with medical image classification and/or sorting. An apparatus may obtain a medical image from a medical image repository and further obtain multiple textual descriptions associated with a set of image classification labels. The apparatus may pair the medical image with one or more of the multiple text descriptions to obtain one or more corresponding image-text pairs, and classify the medical image based on a machine learning (ML) model and the one or more image-text pairs. The ML model may be configured to predict respective similarities between the medical image and the corresponding text descriptions in the one or more image-text pairs, and the apparatus may determine the class of the medical image by comparing the similarities predicted by the ML model. The apparatus may then further process the medical image based on the class of the medical image.

Abstract: In some embodiments, a method for enhancing objects in an X-ray video may include receiving an image frame in an X-ray video and detecting, using a first machine learning model, one or more objects in the image frame, wherein the detection is performed based on the image frame, a sequence of image frames preceding the image frame in the X-ray video, and data indicating one or more objects in the sequence of image frames. The method may further include determining, using a second machine learning model, a background image layer, based on the image frame and one or more image frames from the sequence of image frames that precedes the image frame in the X-ray video. The method may further generate an output image containing an enhanced view of the one or more objects in the image frame based in part on the background image layer.

Abstract: Described herein are systems, methods, and instrumentalities associated with segmenting and/or determining the shape of an anatomical structure. An artificial neural network (ANN) is used to perform these tasks based on a statistical shape model of the anatomical structure. The ANN is trained by evaluating and backpropagating multiple losses associated with shape estimation and segmentation mask generation. The model obtained using these techniques may be used for different clinical purposes including, for example, motion estimation and motion tracking.

Abstract: Described herein are systems, methods, and instrumentalities associated with automatically detecting and enhancing multiple objects in medical scan images. The detection and/or enhancement may be accomplished utilizing artificial neural networks such as one or more classification neural networks and/or one or more graph neural networks. The neural networks may be used to detect areas in the medical scan images that may correspond to the objects of interest and cluster the areas belonging to a same object into a respective cluster. These tasks may be accomplished, for example, by representing the areas corresponding to the objects of interest and their interrelationships with a graph and processing the graph through the one or more graph neural networks so that the areas belonging to each object may be properly labeled and clustered. The clusters may then be used to enhance the objects of interests in one or more output scan images.

Abstract: A 3D anatomical model of one or more blood vessels of a patient may be obtained using CT angiography, while a 2D image of the blood vessels may be obtained based on fluoroscopy. The 3D model may be registered with the 2D image based on a contrast injection site identified on the 3D model and/or in the 2D image. A fused image may then be created to depict the overlaid 3D model and 2D image, for example, on a monitor or through a virtual reality headset. The injection site may be determined automatically or based on a user input that may include a bounding box drawn around the injection site on the 3D model, a selection of an automatically segmented area in the 3D model, etc.

Abstract: Described herein are systems, methods, and instrumentalities associated with processing mammogram images using machine learning based techniques. An apparatus as described herein may obtain a mammographic image of a person, extract features from the mammographic image using a feature encoder, and predict a health condition of the person based on the extracted features. The feature encoder may be trained using a self-supervised technique and based on multi-view mammogram images that may belong to a same person or to different people.

Abstract: The present invention provides a method for measuring a content of a polycyclic aromatic hydrocarbon in a carbon black, comprising the following steps: extracting a carbon black to be measured using an organic solvent to obtain a sample to be tested; testing the sample to be tested by ultraviolet-visible spectrometer to obtain an absorbance; and using the absorbance and a calibration curve to obtain a content of a polycyclic aromatic hydrocarbon in the sample to be tested, wherein the calibration curve shows relationship between the absorbance of the polycyclic aromatic hydrocarbon and the content of the polycyclic aromatic hydrocarbon. The measurement method of the present invention benefits reduction of detection time.

Abstract: Described herein are systems, methods, and instrumentalities associated with automatic determination of hemodynamic characteristics. An apparatus as described may implement a first artificial neural network (ANN) and a second ANN. The first ANN may model a mapping from a set of 3D points associated with one or more blood vessels to a set of hemodynamic characteristics of the one or more blood vessels, while the second ANN may generate, based on a geometric relationship of the set of points in a 3D space, parameters for controlling the mapping. The apparatus may obtain a 3D anatomical model representing at least one blood vessel of a patient based on one or more medical images of the patient, and determine, based on the first ANN and the second ANN, a hemodynamic characteristic of the at least one blood vessel of the patient at a target location of the 3D anatomical model.

Abstract: Disclosed herein are deep-learning based systems, methods, and instrumentalities for medical decision-making. A system as described herein may implement an artificial neural network (ANN) that may include multiple encoder neural networks and a decoder neural network. The multiple encoder neural networks may be configured to receive multiple types of patient data (e.g., text and image based patient data) and generate respective encoded representations of the patient data. The decoder neural network (e.g., a transformer decoder) may be configured to receive the encoded representations and generate a medical decision, a medical summary, or a medical questionnaire based on the encoded representations. In examples, the decoder neural network may be configured to implement a large language model (LLM) that may be pre-trained for performing the aforementioned tasks.

Abstract: Described herein are systems, methods, and instrumentalities associated with image segmentation such as tubular structure segmentation. An artificial neural network is trained to segment tubular structures of interest in a medical scan image based on annotated images of a different type of tubular structures that may have a different contrast and/or appearance from the tubular structures of interest. The training may be conducted in multiple stages during which a segmentation model learned from the annotated images during a first stage may be modified to fit the tubular structures of interest in a second stage. In examples, the tubular structures of interest may include coronary arteries, catheters, guide wires, etc., and the annotated images used for training the artificial neural network may include blood vessels such as retina blood vessels.

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: 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: An apparatus may obtain a sequence of medical images of a target structure and determine, using a first ANN, a first segmentation and a second segmentation of the target structure based on a first medical image and a second medical image, respectively. The first segmentation may indicate a first plurality of pixels that may belong to the target structure. The second segmentation may indicate a second plurality of pixels that may belong to the target structure. The apparatus may identify, using a second ANN, a first subset of true positive pixels among the first plurality of pixels that may belong to the target structure, and a second subset of true positive pixels among the second plurality of pixels that may belong to the target structure. The apparatus may determine a first refined segmentation and a second refined segmentation of the target structure based on the true positive pixels.