Abstract: A computing system including a processing circuit in communication with a camera having a field of view. The processing circuit is configured to perform operations related to detecting, identifying, and retrieving objects disposed amongst a plurality of objects. The processing circuit may be configured to perform operations related to object recognition template generation, feature generation, hypothesis generation, hypothesis refinement, and hypothesis validation.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilizes artificial intelligence to learn to recommend foreground object images for use in generating composite images based on geometry and/or lighting features. For instance, in one or more embodiments, the disclosed systems transform a foreground object image corresponding to a background image using at least one of a geometry transformation or a lighting transformation. The disclosed systems further generating predicted embeddings for the background image, the foreground object image, and the transformed foreground object image within a geometry-lighting-sensitive embedding space utilizing a geometry-lighting-aware neural network. Using a loss determined from the predicted embeddings, the disclosed systems update parameters of the geometry-lighting-aware neural network. The disclosed systems further provide a variety of efficient user interfaces for generating composite digital images.

Abstract: One or more example embodiments of the present invention relates to a computer-implemented method for providing an outline of a lesion in digital breast tomosynthesis includes receiving input data, wherein the input data comprises a reconstructed tomosynthesis volume dataset based on projection recordings, a virtual target marker within a lesion being in the tomosynthesis volume dataset; applying a trained function to at least a part of the tomosynthesis volume dataset to establish an outline enclosing the lesion, the part of the tomosynthesis volume dataset corresponding to a region surrounding the virtual target marker in the tomosynthesis volume dataset; and providing output data, wherein the output data is an outline of a two-dimensional area or a three-dimensional volume surrounding the target marker.

Abstract: The present invention relates to a method of metal artefact reduction in x-ray dental volume tomography, the method comprising: a step (S1) of obtaining two-dimensional x-ray images (1) or a sinogram (2) of at least part (v) of a patient jaw (3a), acquired through relatively rotating an x-ray source (4) and a detector (5) around the patient jaw (3a); the method being characterized by further comprising: a step (S2) of detecting metal objects (6) in the two-dimensional x-ray images (1) or the sinogram (2) by using at least a trained artificial intelligence algorithm to generate 2D masks (7) which represent the metal objects (6) in the two-dimensional x-ray images (1) or 3D masks which represent the metal objects (6) in the sinogram (2), respectively; and a step (S4; S5) of reconstructing a three dimensional tomographic image (8) respectively based on two-dimensional x-ray images (1) or the sinogram (2) and the 2D masks (7) or the 3D masks as generated.

Abstract: A system for 3D medical image segmentation includes a medical imaging device for obtaining a plurality of 2D images forming a volumetric image, processing circuitry, and a display. The processing circuitry is configured with a first stage to divide the volumetric image into 3D image patches, a hierarchical encoder-decoder structure in which resolution of features of the 3D image patches is decreased by a factor of two in each of a plurality of stages of the encoder, an encoder output connected to the decoder via skip connections, and a convolutional block to produce a voxel-wise final segmentation mask. The encoder includes a plurality of efficient paired attention blocks each with a spatial attention branch and a channel attention branch that learn respective spatial and channel attention feature maps. The display displays the final segmentation mask.

Abstract: A human-model collaborative annotation system for training human annotators includes a database that stores images previously annotated by an expert human annotator and/or a machine learning annotator, a display that displays images selected from the database, an annotation system that enables human annotators to annotate images presented on the display, and an annotation training system. The annotation training system selects an image sample from the database for annotation by a human annotator, receives one or more proposed annotations from the annotation system, compares the human annotator's one or more proposed annotations to previous annotations of the image sample by the expert human annotator or machine learning annotator, presents attention maps on the display to draw the human annotator's attention to any annotation errors identified by the comparing, and selects a next training image sample from the database based on any errors identified in the comparing step.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for automatically detecting and reconstructing patterns in digital images. The disclosed system determines structurally similar pixels of a digital image by comparing neighborhood descriptors that include the structural context for neighborhoods of the pixels. In response to identify structurally similar pixels of a digital image, the disclosed system utilizes non-maximum suppression to reduce the set of structurally similar pixels to collinear pixels within the digital image. Additionally, the disclosed system determines whether a group of structurally similar pixels define the boundaries of a pattern cell that forms a rectangular grid pattern within the digital image. The disclosed system also modifies a boundary of a detected pattern cell to include a human-perceived pattern object via a sliding window corresponding to the pattern cell.

Abstract: A method of detecting a protrusion in an intestinal tract in a computer according to an embodiment of the present disclosure includes acquiring a three-dimensional model of the intestinal tract scanned by a scanning device, the three-dimensional model comprising three-dimensional data of the intestinal tract; mapping, in the computer, the three-dimensional model to a two-dimensional plane in an area-preserving manner; and detecting an area of the protrusion in the two-dimensional plane. The method can replace traditional modes such as enteroscopy, and the protrusion in the intestinal tract is detected in a painless and low-cost mode.

Abstract: A motion correction method may include: calculating a current motion-corrected MR image based on a current motion parameter of an imaging target and K-space measurement data of the imaging target; calculating current motion-corrected K-space data based on the current motion parameter of the imaging target and the current motion-corrected MR image; calculating a current K-space measurement data error based on the K-space measurement data of the imaging target and the current motion-corrected K-space data; and determining, based on the current K-space measurement data error, whether an iteration end condition is met. If so, using the current motion-corrected MR image as a final motion-corrected MR image to be used. Otherwise, updating the current motion parameter of the imaging target based on the current K-space measurement data error and the current motion-corrected MR image. The method advantageously provides an increased motion correction speed of an MR image.

Abstract: An image processing apparatus includes an acquisition unit configured to acquire first medical image data and second medical image data obtained by imaging a subject, an intermediate deformation information acquisition unit configured to acquire intermediate deformation information obtained by applying registration processing up to a predetermined stage in first registration processing including a plurality of stages to the acquired first medical image data and second medical image data, a determination unit configured to perform determination of a deformation abnormality with respect to the acquired intermediate deformation information, and a deformation unit configured to perform, in a case where the determination unit determines that there is the deformation abnormality, second registration processing different from the first registration processing with respect to the first medical image data and the second medical image data and calculate deformation information.

Abstract: The present disclosure provides a computerized method for a likelihood of malignancy in breast tissue of a patient. The method includes receiving, with a computer processor, an image of the breast tissue, providing the image of the breast tissue to a model including a trained neural network; the trained neural network being previously trained by training a first neural network, initializing a second neural network based on the first neural network, training the second neural network, and outputting the second neural network as the trained neural network, receiving an indicator from the model, and outputting a report including the indicator to at least one of a memory or a display.

Abstract: Disclosed herein are systems, media, and methods for processing and aligning breast images and uses thereof. The processes disclosed herein may be used to diagnose, predict, and monitor the status or outcome of breast density masking, and breast cancer in a subject, and methods of treating the same.

Abstract: Systems and methods for monitoring one or more anatomical elements are provided. An image depicting one or more anatomical elements and a plurality of annotations may be received. The image may comprise a plurality of image elements. Each annotation may correspond to one of the one or more anatomical elements and may be associated with one of the plurality of image elements. Movement information about movement of at least a portion of a particular anatomical element may be received. Image elements corresponding to the particular anatomical element may be identified to yield a set of image elements. The set of image elements may be adjusted based on the movement information to yield an updated image reflecting changes to affected anatomical elements.

Abstract: A method of measuring a retinal layer includes obtaining an OCT layer image of a retina, detecting a reference boundary line indicating a retinal layer in the obtained OCT image, obtaining an aligned OCT image by aligning a vertical position of each column of the OCT image so that the detected reference boundary line becomes a baseline, predicting retinal layer regions from the aligned OCT image, calculating boundary lines between the predicted retinal layer regions, and restoring the calculated boundary lines to positions of the boundary lines of the retinal layer of the original OCT image by aligning the vertical positions of the calculated boundary lines of the retinal layer for each column so that the baseline becomes the reference boundary line again.

Abstract: Alignment of a 2D image to a corresponding 3D image is provided by writing a pattern into a 3D sample. The pattern is at known positions in the 3D image, and provides visible reference features in the 2D image. This permits accurate determination of the plane in the 3D image that corresponds to the 2D image.

Abstract: Techniques are disclosed for acquiring data of an examination object in at least two slices by means of a pulse sequence. Time intervals between excitations of neighboring slices and associated minimum intervals are determined. From these, time intervals to be adapted between excitations of neighboring slices are determined and adapted before a measurement protocol is executed, with the adapted time intervals. Through the determination of a minimum time interval between excitations of neighboring slices and the adaptation of the time intervals between excitations of neighboring slices, a falsification of measurement results can be avoided, measurement time of the chosen measurement protocol is not increased, and the user is not restricted in their choice of the slices to be excited.

Abstract: Methods and systems are provided for inferring thickness and volume of one or more object classes of interest in two-dimensional (2D) medical images, using deep neural networks. In an exemplary embodiment, a thickness of an object class of interest may be inferred by acquiring a 2D medical image, extracting features from the 2D medical image, mapping the features to a segmentation mask for an object class of interest using a first convolutional neural network (CNN), mapping the features to a thickness mask for the object class of interest using a second CNN, wherein the thickness mask indicates a thickness of the object class of interest at each pixel of a plurality of pixels of the 2D medical image; and determining a volume of the object class of interest based on the thickness mask and the segmentation mask.

Abstract: Systems and methods for touchless registration using a reference frame adapter is provided. A first image of a region of a patient and a second image of the region of the patient and a reference frame adapter may be received. The reference frame adapter may be configured to selectively attach to a patient reference frame positioned near the region of the patient. The second image may be registered to the first image based on the reference frame adapter.

Abstract: A computer-implemented method includes obtaining a three-dimensional (3D) image of a region of a body of a patient, the 3D image having feature values. The 3D image is segmented to define one or more regions of interest (ROIs). At least one region of interest (ROI) feature threshold is determined. A background feature threshold is determined. A 3D model is generated from the 3D image based on the determined at least one ROI feature threshold, the determined background feature threshold, and the segmentation. The 3D model is output for display to a user.

Abstract: The present disclosure relates to techniques for segmenting objects within medical images using a deep learning network that is localized with object detection based on a derived contrast mechanism. Particularly, aspects are directed to localizing an object of interest within a first medical image having a first characteristic, projecting a bounding box or segmentation mask of the object of interest onto a second medical image having a second characteristic to define a portion of the second medical image, and inputting the portion of the second medical image into a deep learning model that is constructed as a detector using a weighted loss function capable of segmenting the portion of the second medical image and generating a segmentation boundary around the object of interest. The segmentation boundary may be used to calculate a volume of the object of interest for determining a diagnosis and/or a prognosis of a subject.

Abstract: An X-ray imaging device and a control method thereof are provided. The X-ray imaging device according to an embodiment of the present invention comprises: an X-ray generator including at least one X-ray source which radiates X-rays; an X-ray detector which detects X-rays radiated from the X-ray generator to generate multiple pieces of projection data; and a processor which controls the X-ray generator to be in one mode among a first mode in which the X-ray generator radiates X-rays for a first radiation time and a second mode in which the X-ray generator radiates X-rays for a second radiation time corresponding to half of the first radiation time, and generates a tomography image on the basis of the multiple pieces of projection data generated through the X-ray detector.

Abstract: Techniques of determining a quantification of at least one characteristic of a muscle structure comprising at least one muscle and at least one tendon are disclosed. The quantification of the at least one characteristic of the rotator cuff may be determined by using at least one artificial neural network and based on one or more medical images depicting the muscle structure of a patient.

Abstract: One or more example embodiments of the present invention relates in one aspect to a computer-implemented method includes receiving 2D topogram data of a patient; generating 2D topogram annotation data by applying a machine learning algorithm for topogram analysis onto the 2D topogram data; generating the medical imaging decision support data based on the 2D topogram annotation data; and providing the medical imaging decision support data.

Abstract: A system can perform image capture during a medical procedure. The system can record video from one or more video sources. The system can store the recorded video in a cached memory as stored video. The system can receive a triggering event, such as receiving a signal, operation of an actuator, or issuance of a voice command. In response to the triggering event, the system can capture a video segment from the stored video. Because the video segment is captured from stored video, the video segment can optionally include video and/or frames that occurred before the triggering event is received.

Abstract: A method includes obtaining emission-tomography functional image data and a corresponding reconstructed anatomical image volume including at least one organ having natural motion; pre-determining a dedicated model for spatial mismatch correction of the at least one organ; performing initial image reconstruction of the emission-tomography functional image data to generate a reconstructed emission-tomography functional image volume utilizing attenuation correction based on the corresponding reconstructed anatomical image volume; and identifying relevant anatomical regions, within both image volumes, where functional image quality may be affected by the natural motion of the at least one organ.

Abstract: An apparatus for producing constrained medical image data, the apparatus including processing circuitry configured to: receive medical image data that includes or is obtained from scan data representing an anatomical region in which a sub-region is enhanced; predict, using a trained model, mask data from the medical imaging data, wherein the mask data is representative of the anatomical region without enhancement of the sub-region; and predict, using the trained model or a further trained model, subtraction data from the same medical image data, the subtraction data being representative of the same anatomical region, and the processing circuitry being further configured to apply at least one constraint to obtain constrained subtraction data.

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor (181, 182, 20) to perform an imaging method (100). The method includes: reconstructing emission imaging data to generate an emission image of a lesion; converting intensity values of the emission image to at least one standardized uptake value (SUV value) for the lesion; processing input data using a regression neural network (NN) (28) to output an SUV correction factor for the lesion, wherein the input data includes at least two of (i) image data comprising the emission image or a feature vector representing the emission image, (ii) the at least one SUV value, (iii) a size of the lesion, and (iv) reconstruction parameters used in the reconstructing; and controlling a display device (24) to display at least one of (I) the SUV correction factor and (II) a corrected SUV value generated by applying the SUV correction factor to the at least one SUV value.

Abstract: A method of creating an image representative of a measured dataset by iteratively updating a base image includes: generating a principal dataset from the measured dataset, the principal dataset having noise at substantially the same level as the measured dataset; generating an additional dataset from the measured dataset such that the additional dataset has noise at substantially the same level as the measured dataset and is not identical to the principal dataset; processing the base image without noise compensation using the principal dataset and additional dataset to obtain a principal interim image and an additional interim image; comparing the principal and the additional interim image to determine an indication of a noise level present; and using the determined indication of noise present to select noise compensation to apply when processing the base image using the measured dataset to create a new base image representative of the measured dataset.

Abstract: An image segmentation apparatus for magnetic resonance imaging according to an embodiment includes processing circuitry. The processing circuitry is configured to obtain a localizer image of an organ, the localizer image being three-dimensional or being in a plurality of layers and two-dimensional. The processing circuitry is configured to temporarily localize, on a basis of the localizer image, a segment in which the organ is present in terms of the layer direction of a plurality of slices included in the localizer image. The processing circuitry is configured to obtain a segmentation result of the organ, by performing an image segmentation process on the localizer image positioned inside the segment in which the organ is present.

Abstract: A controller (522) for live annotation of interventional imagery includes a memory (52220) that stores software instructions and a processor (52210) that executes the software instructions. When executed by the processor (52210), the software instructions cause the controller (522) to implement a process that includes receiving (S210) interventional imagery during an intraoperative intervention and automatically analyzing (S220) the interventional imagery for detectable features. The process executed when the processor (52210) executes the software instructions also includes detecting (S230) a detectable feature and determining (S240) at add an annotation to the interventional imagery for the detectable feature. The processor further includes identifying (S250) a location for the annotation as an identified location in the interventional imagery and adding (S260) the annotation to the interventional imagery at the identified location to correspond to the detectable feature.

Abstract: Techniques are provided for determining magnetic resonance images showing different contrasts in an examination. Magnetic resonance data for all magnetic resonance images are acquired using the same acquisition technique and the magnetic resonance images are reconstructed from their magnetic resonance data sets using at least one reconstruction algorithm. The reconstruction comprises at least one de-noising step. After acquisition of the magnetic resonance data, at least one noise strength measure is determined for the magnetic resonance data sets for each contrast, and de-noising strengths for the de-noising step are chosen individually for each contrast depending on the respective at least one noise strength measure.

Abstract: The present disclosure provides a zero-shot low-dose Computed Tomography (CT) image denoising method and apparatus based on strip diffusion model. This method includes building a strip diffusion model which uses high fidelity of the diffusion model to complete end-to-end denoising of low-dose CT images with different doses, thicknesses and devices. Particularly, in the training process, only the normal-dose CT images are required, greatly reducing the data reliance of the model, and model training across dose and thickness condition can be carried out with the data of only one scenario. In a sampling process, strip scanning strategy is used in combination with overlapped strip information and the input low-dose CT images to solve the maximum a posteriori problem, thereby sequentially generating denoising results. The present disclosure only uses simple convolution and attention architecture and carries out extensive experiments on the datasets involving different doses and thicknesses.

Abstract: The invention relates to a computer-implemented method for segmenting measurement data from a measurement of an object, the object having at least one Material transition region, the measurement data generating a digital representation of the object having the at least one at least one material transition region, the digital object representation having a plurality of pieces of spatially-resolved image information of the object, the method comprising the following steps: determining the measurement data; segmenting at least two homogenous regions of the digital object representation; and determining the position of at least one material transition region between the at least two homogeneous regions. The invention thus provides an improved computer-implemented method for segmenting measurement data from a measurement of an object, which correctly detects material transitions from the measurement data of the object.

Abstract: Disclosed in the present invention are a method and an apparatus for identification of imaging quality of fetal ultrasound images, the method including: acquiring parameters of fetal ultrasound images, used for identification of imaging quality of fetal ultrasound images; identifying an imaging score of fetal ultrasound images based on the parameters thereof; and identifying the imaging quality thereof based on the imaging score thereof. Obviously, it may lead to a quick and accurate identification of the imaging quality of fetal ultrasound images by automatically identifying the imaging quality thereof based on the imaging score thereof, thereby realizing quick and accurate management of the imaging quality thereof so as to facilitate to acquire fetal ultrasound images with high quality, which facilitates the acquisition of accurate fetal growth and development and may have an acknowledgment of the operational standardization of the personnel during the detection of fetal ultrasound images.

Abstract: Systems and methods for detecting anatomical structures include, for each training subject of a plurality of training subjects, a corresponding MR image, and generating an initial anatomical template based on a first training subject of the plurality of training subjects. A computing device can map MR images of the other training subjects onto a template space by applying a global transformation followed by a local transformation. The computing device can average the mapped MR images with the initial anatomical template to generate a final anatomical template and boundaries of an anatomical structure of interest can be drawn in the final anatomical template. The computing device can fine tune the boundaries using an edge detection algorithm. The final anatomical template can be used to identify boundaries of the anatomical structure(s) of interest automatically (e.g., without human intervention) in non-training subjects.

Abstract: A processor is configured to generate a structure-highlighted synthesized two-dimensional image from a plurality of tomographic images, to detect a structure of interest from the plurality of tomographic images or the structure-highlighted synthesized two-dimensional image, and to set at least some of the plurality of tomographic images as either storage-required images or non-storage-required images based on a detection result of the structure of interest.

Abstract: A method and a system for generating a dynamic addictive neural circuit based on weakly supervised contrastive learning are disclosed. The method includes: based on a convolutional neural network, reducing a dimensionality of voxels of multiple groups of fMRI to attributes of brain region nodes, and generating multiple groups of dynamic brain connection maps containing time series based on the attributes of the brain region nodes; extracting spatio-temporal features of brain connections in the dynamic brain connection maps; inputting the spatio-temporal features into an abnormal connection detection network, calculating an abnormal probability of brain connections based on contrastive learning, and obtaining the brain connection with a highest abnormal probability at each time point; and generating the dynamic addictive neural circuit based on neuroscientific prior knowledge and the brain connection with the greatest probability of abnormality.

Abstract: A CADx system for analysing medical images to monitor at least one feature on the image to predict at least one of a change of the state or maintenance of the current state of a monitored feature within a time frame is described. The system comprising: an input circuit for receiving at least one medical input image; a feature state change circuit for analysing the received input image and predicting a state change comprising: a state change predictor to predict a state change of the monitored feature within the time frame; and an output circuit to output an indication of the change of state or maintenance of the current state of the monitored feature within the time frame based on the prediction of the feature state change predictor. A method of training a feature state change prediction circuit using Machine Learning is also described.

Abstract: Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.

Abstract: A computer-implemented method is for creating a corrected volume data set representing an examination object. In an embodiment, the method includes providing an initial volume data set based on unknown projection data sets which were acquired via a transmission X-ray imaging system in unknown real mapping geometry; specifying virtual mapping geometry for the initial volume data set; generating virtual projection data sets based on the initial volume data set and the specified virtual mapping geometry; generating corrected mapping geometry based on the plurality of virtual projection data sets; and generating the corrected volume data set based on the virtual projection data sets and the corrected mapping geometry.

Abstract: A computer-implemented method of classifying objects in an image, the method comprising: receiving image data associated with the image; receiving incidence angle data, wherein the incidence angle data is indicative of an incidence angle from which the image data is collected by a detector; and using a machine learning model to classify one or more objects within the image as belonging to one of one or more categories, wherein classifying the one or more objects within the image is based on: the incidence angle data, and respective values of one or more parameters of the image data.

Abstract: System and method of more efficiently identifying and segmenting anatomical structures from 2-D cone beam CT images, rather than from reconstructed 3-D volume data, is disclosed. An image processing system receives, from a cone beam CT device, at least one 2-D x-ray image, which is part of a set of x-ray images taken from a 360 degree scan of a patient with a cone beam CT imaging device. The x-ray image contains at least one anatomical structure such as vertebral bodies to be segmented. The received x-ray is then analyzed in order to identify and segment the anatomical structure contained in the x-ray image based on a stored model of anatomical structures. Once the 360 degree spin is completed, a 3-D image volume from the x-ray image set is created. The identification and segmentation information derived from the x-ray image is then added to the created 3-D image volume.

Abstract: The present disclosure relates to: a method for evaluating a state of a motor function of a patient who has or is suspected of having brain damage by using parameters obtained from a diffusion weighted image of the patient's brain; a program for executing the method in a computer; and an image processing device and an MRI device which can be used in practicing the method. According to the present disclosure, the motor function state of a patient who has or is suspected of having brain damage can be evaluated, and it is also possible to predict the recovery of the motor function after regeneration treatment. Accordingly, whether the patient is suitable for the regeneration treatment can be determined before the start of the treatment.

Abstract: Techniques for representing a scene or map based on statistical data of captured environmental data are discussed herein. In some cases, the data (such as covariance data, mean data, or the like) may be stored as a multi-resolution voxel space that includes a plurality of semantic layers. In some instances, individual semantic layers may include multiple voxel grids having differing resolutions. Multiple multi-resolution voxel spaces may be merged to generate combined scenes based on detected voxel covariances at one or more resolutions.

Abstract: This invention provides a histologic system and method for rapidly and accurately assessing tumor margins for the presence or absence of tumor using machine learning algorithms. This affords a rapid and accurate histologic tumor readout and increase process efficiency and decreases the chance for human error. Advantageously and uniquely, the system and method allows for analyzing the tissue section as complete or incomplete as the first criteria to determine whether a tissue section is clear of tumor. A machine learning process receives whole slide images (WSI) of tissue and determines (a) if each image of the WSI contains complete/incomplete tissue samples and (b) if each image of the WSI contains tumorous tissue or an absence thereof. A reconstruction process generates a model of the tissue that maps types of tissue therein, and a display process provides results of the model or report for use and manipulation by a user.

Abstract: The present application provides an image reconstruction method, a device, equipment, a system, and a computer-readable storage medium. Said method comprises: obtaining a target reconstruction model (S1); invoking a first convolutional layer in the obtained target reconstruction model to extract shallow layer features from the obtained image to be reconstructed (S2); invoking a residual network module in the target reconstruction model to obtain middle layer features from the shallow layer features (S3); invoking a densely connected network module in the target reconstruction model to obtain deep layer features from the middle layer features (S4); and invoking a second convolutional layer in the target reconstruction model to perform image reconstruction on the deep layer features so as to obtain a reconstructed image of the image to be reconstructed (S5). Said method improves the quality and resolution of a reconstructed image.

Abstract: Embodiments described herein are generally related to a method and a system for constructing and delivering a pulse to perform an entangling gate operation between two trapped ions during a quantum computation, and more specifically, to a method of demodulating and spline interpolating a pulse that can be practically implemented in the system while increasing the fidelity of the entangling gate operation, or the probability that at least two ions are in the intended qubit state(s) after performing the entangling gate operation between the two ions.

Abstract: The present disclosure relates to a method for modeling a personalized breast implant including the operations of: obtaining medical image data and body scan data; obtaining first image data by 3D modeling the medical image data; obtaining second image data by 3D modeling the body scan data; creating third image data by merging the first image data and the second image data; and creating breast implant appearance information on the basis of the third image data.

Abstract: A cabinet X-ray image system for obtaining colorized or greyscale density X-ray images of a specimen has a cabinet defining an interior chamber. The cabinet includes a walled enclosure surrounding the interior chamber, a door configured to cover the interior chamber and a sampling chamber for containing the specimen. The system further includes a display and an X-ray unit. The X-ray unit includes an X-ray source, an X-ray detector and a specimen platform configured to receive the specimen. Moreover, the system includes a controller configured to selectively energize the X-ray source, control the X-ray detector to collect the X-rays, determine the density of different areas of the specimen and create a density X-ray image of the specimen. The density X-ray image is colorized or greyscale. The controller is also configured to selectively display the density X-ray image of the specimen on the display.

Abstract: In some embodiments, the systems and methods of the disclosure can efficiently and accurately classify neurodegenerative disorder(s) and/or movement disorder(s) of a subject (e.g., a patient) using at least quantitative features associated with one or more regions of interest determined from one or more sets of image data of the subject's brain. The method may include processing one or more sets of MRI image data of the subject's brain to extract one or more quantitative features for one or more regions. The one or more quantitative features may include a first quantitative and a second quantitative feature. The method may further include classifying at least the one or more quantitative features into one or more classes associated with neurodegenerative dementia disorder, neurodegenerative movement disorder, non-neurodegenerative movement disorder and/or heathy control. The method may include generating a report including a classification of at least the one or more quantitative features.