Abstract: The invention relates to a magnetic resonance imaging data processing system (126) for processing motion artifacts in magnetic resonance imaging data sets using a deep learning network (146, 502, 702) trained for the processing of motion artifacts in magnetic resonance imaging data sets. The magnetic resonance imaging data processing system (126) comprises a memory (134, 136) storing machine executable instructions (161, 164) and the trained deep learning network (146, 502, 702). Furthermore, the magnetic resonance imaging data processing system (126) comprises a processor (130) for controlling the magnetic resonance imaging data processing system.

Abstract: An X-ray CT apparatus according to the embodiment executes an imaging according to an imaging protocol including one or more image elements corresponding to an imaging type. The X-ray CT apparatus includes an X-ray source, an X-ray detector and processing circuitry. The X-ray source radiates an X-ray. The X-ray detector detects the X-ray. The processing circuitry merges, when first and second imaging protocols are set, first and second imaging elements, respectively included in the first and second imaging protocols, corresponding to same imaging type into a single third imaging element, thereby generating a third imaging protocol including the third imaging element.

Abstract: A contrast parameter learning system is operable to generate contrast significance data for a computer vision model, where the computer vision model was generated by performing a training step on a training set of medical scans. Significant contrast parameters are identified based on the contrast significance data. A re-contrasted training set is generated by performing an intensity transformation function that utilizes the significant contrast parameters on the training set of medical scans. A re-trained model is generated by performing the training step on the first re-contrasted training set. Re-contrasted image data of a new medical scan is generated by performing the intensity transformation function. Inference data is generated by performing an inference function that utilizes the first re-trained model on the re-contrasted image data. The inference data is transmitted via the transmitter to a client device for display via a display device.

Abstract: Techniques for fracture detection are provided. A first image is received to be processed to identify rib fractures. A first set of regions of interest (ROIs) is identified by processing the first image using a first machine learning model, where each ROI in the first set of ROIs corresponds to a first potential fracture. Further, a first ROI of the first set of ROIs is upsampled, and the system attempts to verify the first potential fracture in the first ROI by processing the upsampled first ROI using a second machine learning model.

Abstract: A method for processing images of lungs, the method comprising defining an inhale region of interest of the lungs at an inhale position and an exhale region of interest of the lungs at an exhale position, determining a spatial transformation of each voxel within the lungs between the lungs at the inhale position and the lungs at the exhale position to provide displacement vector estimates for each voxel within the lungs, and performing volume change inference operations to determine a volume change between the lungs at the inhale position and the lungs at the exhale position based on the inhale region of interest, the exhale region of interest, and the displacement vector estimates for each voxel within the lungs.

Abstract: Training a generator includes processing a dental image using the generator to obtain a synthetic pathology label, such has a pixel mask indicating portions of the dental image representing caries. The synthetic pathology label is compared to a target pathology label for the dental image and the generator is updated according to the comparison. The synthetic pathology may be evaluated by a discriminator along with a real pathology label to obtain a realism estimate. The discriminator and generator may be updated according to accuracy of the realism estimate. Inputs to the generator may further include tooth labels and/or labels of restorations. Machine learning models may be trained to label restorations and defects in restorations. A machine learning model may be trained to identify the surface of a tooth having a pathology thereon.

Abstract: Various computational methods and techniques are presented to increase the lateral and axial resolution of an ultrasound imager in order to allow a medical practitioner to use an ultrasound imager in real time to obtain a 3D map of a portion of a body.

Abstract: In order to increase reproducibility of a reconstructed tomographic image without increasing the computational load, detection is performed by oversampling in (N+n) directions during imaging for detection by N detection elements. A vector having N×(N+n) elements is obtained, and vector decimation step is performed in which a total of n×N elements corresponding to a sequence {k} 30 denoting a decimation order are removed. In a discrete Radon transform step, a corresponding discrete Radon inverse matrix WSQ?140 is operated, and in an image data generation step, de-vectoring is performed, thereby tomographic image data are acquired. When oversampling is used, a discrete Radon inverse matrix WSQ?1 is obtained. Therefore, a tomographic image is obtained by matrix computation without resorting to iterative approximation.

Abstract: The present disclosure relates to a system and method for segmenting a normal organ and/or tumor structure based on artificial intelligence for radiation treatment planning. The system may include a data collection unit configured to collect a radiotherapy structure (RT-structure) file including a computerized tomography (CT) image of a patient and contour information of an area of interest for radiation treatment, a pre-processing unit configured to extract the contour information from the RT-structure file and generate a binary image based on the extracted contour information, and a model training unit configured to learn parameters for generating a segmentation map indicative of the area of interest using a deep learning algorithm, based on the binary image, and generate a trained model based on the learnt parameter.

Abstract: A computer-implemented method of training a neural network for organ segmentation may be provided. The method may include: collecting a set of digital sample images from a database; inputting the collected set of digital images into a neural network recognition model; and training the neural network recognition model to recognize a first object in a first digital image as a specific object based on the first object being similar to a second object in a second digital image. The method may comprise predicting a signed distance map (SDM) in conjunction with a segmentation map.

Abstract: Provided are an image processing device, an endoscope system, an image processing method, and a program capable of automatically differentiating a medical image including a scene of interest and supporting saving of the medical image according to a differentiation result.

Abstract: Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Abstract: A computer-implemented method for classifying and presenting a contrast phase (CP) of a contrast enhanced computerized tomography (CECT) scan is provided. The method includes training an artificial intelligence (AI) algorithm utilizing a set of CPs labeled CECT data to associate a set of characteristics of the data with a probability associated with the CP. The method includes receiving a new set of unlabeled CECT data, and applying the AI algorithm to the new unlabeled CECT data to associate a first probability of a first CP and a second probability of a second CP. The method also includes providing a graphical representation including the first probability of the first CP and the second probability of the second CP.

Abstract: The present disclosure includes systems and methods for creating positron emission tomography (PET) images. The method includes receiving at least one PET image of a subject created from PET data acquired from the subject, creating an attenuation correction map using the at least one PET image, and reconstructing PET data using the attenuation correction map and the at least one PET image to generate an attenuation corrected PET image.

Abstract: An apparatus for vascular modeling is disclosed. The apparatus receives medical images from an imaging device that include representations of a coronary vessel tree of a subject recorded at a different viewing angles. The apparatus determines, from a first of the medical images, a first centerline set and first vessel diameters for sample points along the first centerline set, and determines, from a second of the medical images, a second centerline set and second vessel diameters for sample points along the second centerline set. The apparatus determines a correspondence between the first centerline set and the second centerline set, and determines diameters for a combined centerline set based on the correspondence of sample points along the first and second centerline sets. The apparatus provides the combined centerline set for estimating blood flow resistance values of the coronary vessel tree of the subject to determine at least one potential stenosis.

Abstract: Navigation systems and methods for tracking physical objects near a target site during a surgical procedure are provided, the navigation system comprises a robotic device and an instrument attached to the robotic device; a process of optical registration and calibration can improve operation results by increasing accuracy and speed and eliminate the need for additional reference trackers, such as a pointer.

Abstract: A method is for determining orthogonal slice image data sets of a tomosynthesis recording of an examination region. In an embodiment, the method includes recording a tomosynthesis recording and reconstructing a plurality of first slice image data sets in a first plane based upon the tomosynthesis recording; selecting a first slice image data set from the plurality of first slice image data sets; marking a microcalcification or a region of interest with a punctiform marking in the first slice image data set selected; and determining, from a second slice image data set in a second plane orthogonal to the first plane, and from a third slice image data set in a third plane orthogonal to the first plane and the second plane, wherein a point of intersection of the first plane, the second plane and the third plane includes the punctiform marking.

Abstract: A brain tumor image segmentation method and device are disclosed. The disclosed method includes acquiring a basic white matter template generated based on brain magnetic resonance images of a plurality of healthy samples, collecting corresponding low, mid and high b-value diffusion weighted images of the brain of a patient, segmenting out a tumor region including the tumor body and the edema on each image based on the signal distribution of each image in a first set image group of the patient, removing the normal white matter region from the tumor region according to the basic white matter template and the high b-value diffusion weighted image, and classifying the value of the voxel in each image in a second set image group and a second apparent diffusion coefficient image obtained through calculations to obtain a tumor body region and an edema region.

Abstract: Systems and methods are provided for preparation of orthodontics and prosthodontics. A method may include scanning a patient's teeth to form first 3D data of the patient's teeth including a removable element that obscures part of the dental surfaces of the patient's teeth and non-obscured tooth surfaces, removing the removable element form the patient's teeth so that the removable element no longer obscures the part of the dental surfaces of the patient's teeth, and scanning the previously obscured part of the dental surfaces of the patient's teeth and the non-obscured tooth surfaces.

Abstract: A method, user device, and system for displaying augmented anatomical features is disclosed. The method includes detecting a target individual, displaying a visual representation of the body, and determining an anatomical profile of the target individual based on a plurality of reference markers. The method further includes displaying, on the display, a graphical representation of the inner anatomical features onto the visual representation of the body so as to assist in the identification of the inner anatomical features. In another aspect, an initial three-dimensional representation of the body is mapped and a preferred anatomical profile is determined based upon the reference markers. The initial three-dimensional representation of the body is modified to be the shape of the preferred anatomical profile and displayed.

Abstract: Certain exemplary method and/or apparatus embodiments herein can include—acquiring a set of data relative to a patient's maxillofacial region through an x-ray CT imaging apparatus, —reconstructing two or more types of maxillofacial data volumes from the same acquired set of data, —displaying at least one slice of the patient's maxillofacial region with a first type of data volume among the volumes (e.g., FDK volume and MAR volume), —selecting a region of interest (ROI) in the displayed at least one slice, a first portion of said first type of data volume being displayed within the ROI, —displaying within the selected ROI a second portion of a second type of data volume, the at least one slice of the patient's maxillofacial region being displayed with the first type of data volume while the ROI is displayed with the second portion of the second type of data volume.

Abstract: Techniques are disclosed for recording magnetic resonance data of an examination object with a magnetic resonance device. A magnetic resonance sequence is used to record the magnetic resonance data from at least two slices of a slice stack, and at least two temporally separate radio frequency pulses are output within an excitation time frame. A slice thickness of the slices, which is increased by an enlargement factor that is greater than one compared with a nominal slice thickness, is used for at least one, but not all, of the radio frequency pulses. The enlargement factor is selected as a function of a distance value describing the distance between two adjacent slices of the slice stack, such that the increased slice thickness does not result in the resulting excitation region of a slice extending into the adjacent slice.

Abstract: Methods and apparatus for grading coronary calcium, including an example apparatus that includes a set of logics and a memory that stores a set of radiographic images. The set of logics includes an image acquisition logic that acquires a set of radiographic images of a region of tissue, the set of radiographic images including a low-energy reference x-ray image and a higher-energy floating x-ray image, a registration logic that produces a set of registered images from a plurality of members of the set of radiographic images, an enhancement logic that produces a set of calcium-enhanced images from the set of registered images, a post-enhancement logic that produces a bone image from the set of calcium-enhanced images, and a grading logic that computes a coronary heart disease prediction score based, at least in part, on the bone image and a set of features extracted from the bone image.

Abstract: Embodiments access a set of radiological images acquired from a population of subjects, where a member of the set of radiological images includes a left atrium (LA) region; construct a statistical shape differential atlas from the images; generate a template LA model from the statistical shape differential atlas, where the template LA model includes a site of interest (SOI); acquire a pre-ablation radiological image of a region of tissue in a patient demonstrating atrial fibrillation (AF) pathology; generate a patient LA model from the pre-ablation image; compute a deformation field that registers the SOI to the patient LA model using deformable registration; compute a patient feature vector based on the deformation field; generate an AF probability score for the patient based on the feature vector; generate a classification of the patient based, at least in part, on the AF probability score; and display the classification or the AF probability score.

Abstract: A method for processing CT imaging data includes providing CT imaging data obtained at two x-ray energy levels in a first respiratory phase, preferably in an inhalation phase, of the subject and providing second CT imaging data obtained at two x-ray energy levels in a second respiratory phase, preferably in an exhalation phase, of the subject. The method may include reconstructing first regional perfusion blood volume (PBV) imaging data from the provided first CT imaging data, reconstructing second regional PBV imaging data from the provided second CT imaging data, reconstructing first virtual non-contrast (VNC) imaging data from the provided first CT imaging data, reconstructing second VNC imaging data from the provided second CT imaging data, determining a transformation function for registering the first and second reconstructed VNC imaging data, and registering the first and second reconstructed VNC imaging data by applying the transformation function.

Abstract: Embodiments of this application disclose a virtual reality content display method performed at a computing device, the method including the following processing steps: receiving a video stream including a sequence of video frames and obtaining a video frame to be displayed from the video stream; identifying a preset key information area in the video frame; rendering the video frame on an imaging panel, to form a virtual reality content image for display; determining that the preset key information area is being viewed by a user along a line of sight direction on the imaging panel; and in accordance with the determination, displaying a render mesh at a predetermined position between the imaging panel and a virtual camera collocated with the user and displaying the preset key information area at a position closer to the user along the line of sight direction.

Abstract: A method for generating motion-corrected medical images includes obtaining, via a processor, k-space data of a region of interest acquired by a magnetic resonance imaging system utilizing a 3D radial pulse sequence with ZTE acquisition including optional magnetization preparation pulses. The method also includes sampling, via the processor, the k-space data to obtain a plurality of interleaved k-space segments. The method further includes reconstructing, via the processor, one or more interleaved k-space segments of the plurality of interleaved k-space segments to generate a respective motion navigator volume. The method even further includes co-registering, via the processor, each respective motion navigator volume to estimate motion and performing motion correction on the one or more interleaved k-space segments and their corresponding k-space trajectories.

Abstract: A data compression system in a storage system compresses data with a first compression method to generate compressed data, determines whether a compression rate of the compressed data is better than a predetermined reference, outputs data obtained by compressing the data by the compression method having a better compression rate than that of the other compression method of the first compression method and a second compression method when it is determined that the compression rate is better than the reference, and outputs data obtained by compressing the data by the compression method having a worse compression rate than that of the other compression method of the first compression method and the second compression method when it is determined that the compression rate is equal to or worse than the reference.

Abstract: An exemplary system, method, and computer-accessible medium for generating computed tomography (“CT”) image(s) of a subject(s) can be provided which can include receiving low dose CT imaging information for the subject(s), where the low dose CT imaging information can be based on a radiation dose of less than about 50 mAs, receiving a priori CT image data, and generating the CT image(s) based on the low dose CT imaging information and the a priori CT image data. A further CT images(s) can be generated based on the CT image(s) and the a priori CT image data. The a priori CT image data can include a set of Markov Random Field (MRF) coefficients derived from high dose or full dose CT image data.

Abstract: One embodiment of the present invention sets forth a technique for generating simulated training data for a physical process. The technique includes receiving, as input to at least one machine learning model, a first simulated image of a first object, wherein the at least one machine learning model includes mappings between simulated images generated from models of physical objects and real-world images of the physical objects. The technique also includes performing, by the at least one machine learning model, one or more operations on the first simulated image to generate a first augmented image of the first object. The technique further includes transmitting the first augmented image to a training pipeline for an additional machine learning model that controls a behavior of the physical process.

Abstract: In an emission imaging method, emission imaging data are acquired for a subject using an emission imaging scanner (10) including radiation detectors (12). The emission imaging data are reconstructed to generate a reconstructed image by executing a constrained optimization program including a measure of data fidelity between the acquired emission imaging data an a reconstruct-image transformed by a data model of the imaging scanner to emission imaging data. During the reconstructing, each iteration of the constrained optimization program is constrained by an image variability constraint. The reconstructed image is displayed the reconstructed image on a display device. The emission imaging may be positron emission tomography (PET) imaging data, optionally acquired using a sparse detector array. The image variability constraint may be a constraint that an image total variation (image TV) of a latent image defined using a Gaussian blurring matrix be less than a maximum value.

Abstract: Systems and methods are presented for triggering machine learning (ML) annotation model retraining. The method includes an annotation model accepting raw data images with undefined variations of a first shape with a coarse boundary surrounding a first shape. The annotation model creates an annotated image with annotation marks forming a refined boundary surrounding the first shape. An agent user interface (UI) modifies the refined boundaries to supply corrected images. A mediation software application compares the annotated images to corresponding corrected images, and supplies an annotation model retraining resource analysis for the first shape in response to comparing the annotated images to the corrected images. The step of comparing the annotated images to the corrected images may include the mediation application measuring the number of points changed in the refined boundaries to supply the corrected images, and the time required by the human agent to change the points.

Abstract: A content-based image retrieval (CBIR) system and method is presented herein. The CBIR system generates a relatively short vector or array of data, referred to as a barcode, from an input image. The short vector or array data can be used to represent the content of the image for image retrieval purposes. The system obtains the image and applies a transform to the image to generate a plurality of image transform values. The system thresholds the plurality of image transform values to obtain compact image transform values. The system generates a barcode in accordance with the compact image transform values and representative of the image. The system may then transmit the barcode to a database for storage or draw the barcode on a display. The system may also compare barcodes to find and retrieve similar images associated with similar barcodes.

Abstract: An image acquisition unit acquires a plurality of projection images corresponding to each of a plurality of radiation source positions which have been generated by directing an imaging apparatus to perform tomosynthesis imaging for an object compressed by a compression plate. An edge image detection unit detects an edge image caused by the edge of the compression plate in the plurality of projection images. A cutout unit cuts out the projection image according to a positional relationship between the edge image and an image of the object to generate a cut-out projection image.

Abstract: An embodiment is for determining a region of interest to be rendered in an ultrasound volume data set of an interior of an object under examination. In an embodiment, the method includes: determining a projection position in a two dimensional X-ray projection image of the object under examination, wherein a projection ray correlated to the projection position passes through the region of interest; and extracting a partial data set encompassed by the ultrasound volume data set using the projection position determined and based upon geometrical information relating to the X-ray 3D ultrasound unit.

Abstract: A method for computer assisted identification of appropriate anatomical structure for placement of a medical device, comprising: receiving a 3D scan volume comprising set of medical scan images of a region of an anatomical structure where the medical device is to be placed; automatically processing the set of medical scan images to perform automatic segmentation of the anatomical structure; automatically determining a subsection of the 3D scan volume as a 3D ROI by combining the raw medical scan images and the obtained segmentation data; automatically processing the ROI to determine the preferred 3D position and orientation of the medical device to be placed with respect to the anatomical structure by identifying landmarks within the anatomical structure with a pre-trained prediction neural network; automatically determining the preferred 3D position and orientation of the medical device to be placed with respect to the 3D scan volume of the anatomical structure.

Abstract: A computed tomography (CT) image display system (10) includes a mapping unit (32), which receives reconstructed volumetric image data (28) of a subject with values in Hounsfield Units (HU), and a set of reference settings (34), adjusts the set of reference settings (34) to an adjusted set of reference settings (35) according to a pixel-value distribution analysis of the HU values selected according to the reference settings (34), and maps the values in HU to gray scale values according to the adjusted reference settings (35).

Abstract: Image processing using a machine learning model is enabled, thereby accurately reducing noise to improve image quality. A medical image is acquired; and it is evaluated whether noise in the medical image exceeds a predetermined reference value. A noise reducer reduces the noise of the medical image that has been determined to include noise that exceeds the reference value. The noise of the medical image is reduced using a machine learning model constructed by collecting a plurality of learning data sets that include an image with noise as input data and an image without noise as output data. The machine learning model includes a plurality of layers that perform convolution on an image that is input, one layer of which includes a filter layer in which a plurality of linear or nonlinear filters are incorporated, and convolution coefficients of the plurality of linear or nonlinear filters are predetermined.

Abstract: A system and method includes detecting motion in a space; capturing images of a scene in a direction of the motion within the space by peripheral image capturing devices and detecting a body of a person within the images of the scene; extracting an image of the body from the images of the scene; identifying a head of the body based on the extracted at least one image of the body and extracting an image of a head from the extracted the one image of the body; transmitting the one image of the body to a body tracking module; transmitting the extracted image of the head to a face detection module; in parallel, tracking the body within the space and performing by a facial recognition module facial recognition of the extracted image of the head of the body; and determining an identity of a user associated with the body.

Abstract: The present disclosure relates to a patient motion tracking system for automatic generation of a region of interest on a 3D surface of a patient positioned in a radiotherapy treatment room. More particularly, the disclosure relates to an assistive approach of a motion tracking system, by which a region of interest (ROI) is automatically generated on a generated 3D surface of the patient. Furthermore, a method for automatically generating a ROI on the 3D surface of the patient is described. In particular, all the embodiments refer to systems integrating methods for automatic ROI generation in a radiotherapy treatment setup.

Abstract: The invention provides for a medical instrument (100, 400) comprising a medical imaging system (102, 402) configured for acquiring medical imaging data (432) from a subject (108); a subject support (110) configured for supporting the subject during acquisition of the medical imaging data; and an optical imaging system (114, 114?) configured for acquiring optical imaging data (134) of the subject on the subject support. The execution of the machine executable instructions causes a processor (122) controlling the medical instrument to: control (200) the optical imaging system to acquire the optical imaging data; generate (202) the initial vector (136) using the optical imaging data; generate (204) the synthetic image by inputting the initial vector into a generator neural network; calculate (206) a difference (140) between the synthetic image and the optical imaging data; and provide (208) a warning signal (142) if the difference differs by a predetermined threshold.

Abstract: In one embodiment, a computer-implemented method is for providing lymph node information. The method includes receiving medical imaging data; receiving atlas data spatially relating lymph node stations to at least one non-lymphatic anatomical structure; determining a lymph node position in the medical imaging data; generating the lymph node information, the lymph node information being indicative of a lymph node station, to which the lymph node position is anatomically allocated, by applying an algorithm onto the medical imaging data, the atlas data and the lymph node position; and providing the lymph node information.

Abstract: Disclosed is a computer-implemented method of determining one or more position and/or orientation parameters of an anatomical structure of a body portion. The anatomical structure has a longitudinal shape defining a longitudinal axis. The method includes generating and/or reading, by a data processing system, volumetric data of at least a portion of a subject. The method further includes generating and/or reading, by the data processing system, a deformable template which provides an estimate for a location of the longitudinal axis in the portion of the subject. The method further includes matching, by the data processing system, the deformable template to the volumetric data, thereby obtaining a matched template. The matching comprises using one or more internal energy functions and one or more external energy functions for optimizing an objective function.

Abstract: A method is for the reconstruction of an image data set from projection images of an examination object recorded at different projection directions with a computed tomography apparatus. In an embodiment, the method includes establishing an item of contour information describing a contour of at least one of the examination object and the additional object; enhancing projection data of the projection images via forward projection in regions in which at least one of the examination object and the additional object was not acquired; and reconstructing the image data set based upon the projection data enhanced. The examination object and the additional object are acquired from sensor data of at least one camera. Finally, an item of sensor information at least partially describing at least one of the is of the examination object and of the additional object being established and used for establishing the contour information.

Abstract: The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

Abstract: A method and apparatus uses multi-material decomposition of three or more material components to generate material-component images from spectral images reconstructed from spectral computed tomography data. In three-component material decomposition e.g., the Mendonça method is used for multi-material decomposition when the attenuation values satisfy an assumed volume fraction condition (i.e., for a given voxel, the attenuation values are within a triangle having vertices given by unit volume fractions of three respective material components). However, when the volume fraction condition fails (e.g., the attenuation values are outside the triangle), either a shortest-Hausdorff-distance method or a closest-edge method is used for multi-material decomposition. For example, the attenuation values of the voxel are projected onto a lower-dimensional space (e.g., the space of a closest edge) and decomposed into a pair/single material component(s) of the lower-dimensional space.

Abstract: A system and method for creating and using a medical imaging bioinformatics annotated (“MIBA”) master file is disclosed. Creating the MIBA master file includes receiving image data, performing a first registration on the image data for obtaining in-slice registered data, and performing a second registration for registering the in-slice registered data to a three-dimensional (3D) model for obtaining source data. Creating also includes extracting voxel data from the source data and storing the voxel data in a MIBA database, receiving selection of a volume of interest, and extracting a portion of the voxel data corresponding to the volume of interest. The MIBA master file is created from the portion of the voxel data, which is stored in the MIBA database. The MIBA system receives a query, extracts data from the MIBA master file in response to the query, and presents the extracted data on an output interface.

Abstract: Methods and systems are provided for reducing anomalies in ultrasound images. One example method includes combining a plurality of sub-band components to attenuate anomalies in a first ultrasound image formed by the combination of the plurality of sub-band components, wherein the plurality of sub-band components are combined based on a plurality of adaptive weights output from a machine learning model and wherein the plurality of adaptive weights are associated with the plurality of sub-band components. The method further includes outputting for a display device, the first ultrasound image.

Abstract: Disclosed are systems and methods for mapping a patient's lymphatic system. An exemplary method includes generating a three-dimensional (3D) model of a bronchial network in the patient's chest, generating a lymphatic tree map by fitting a model lymph node map to the 3D model, receiving locations of a plurality of identified lymph nodes, updating the positions of lymph nodes on the lymphatic tree map, and displaying the updated lymphatic tree map.

Abstract: Methods, apparatus and systems for deep learning based image reconstruction are disclosed herein. An example at least one computer-readable storage medium includes instructions that, when executed, cause at least one processor to at least: obtain a plurality of two-dimensional (2D) tomosynthesis projection images of an organ by rotating an x-ray emitter to a plurality of orientations relative to the organ and emitting a first level of x-ray energization from the emitter for each projection image of the plurality of 2D tomosynthesis projection images; reconstruct a three-dimensional (3D) volume of the organ from the plurality of 2D tomosynthesis projection images; obtain an x-ray image of the organ with a second level of x-ray energization; generate a synthetic 2D image generation algorithm from the reconstructed 3D volume based on a similarity metric between the synthetic 2D image and the x-ray image; and deploy a model instantiating the synthetic 2D image generation algorithm.