Abstract: A system according to the present disclosure may include a display unit, a processor communicatively coupled to the display unit and to an ultrasound imaging apparatus for generating an image from ultrasound data representative of a bodily structure and fluid flowing within the bodily structure. The processor may be configured to generate vector field data including axial and lateral (or transverse) velocity components of the fluid flowing within the bodily structure, calculate velocity profiles for a plurality of locations along a wall of the bodily structure based on the axial and lateral velocity components, generate wall shear stress (WSS) visualization data based, at least in part, on the velocity profiles, and cause the display unit to concurrently display the image including the bodily structure overlaid with the WSS visualization data.

Abstract: Systems and methods are provided for generating and using statistical data which is indicative of a difference in shape of a type of anatomical structure between images acquired by a first imaging modality and images acquired by a second imaging modality. This statistical data may then be used to modify a first segmentation of the anatomical structure which is obtained from an image acquired by the first imaging modality so as to predict the shape of the anatomical structure in the second imaging modality, or in general, to generate a second segmentation of the anatomical structure as it may appear in the second imaging modality based on the statistical data and the first segmentation.

Abstract: A method for implementing a dynamic three-dimensional lung map view for navigating a probe inside a patient's lungs includes loading a navigation plan into a navigation system, the navigation plan including a planned pathway shown in a 3D model generated from a plurality of CT images, inserting the probe into a patient's airways, registering a sensed location of the probe with the planned pathway, selecting a target in the navigation plan, presenting a view of the 3D model showing the planned pathway and indicating the sensed location of the probe, navigating the probe through the airways of the patient's lungs toward the target, iteratively adjusting the presented view of the 3D model showing the planned pathway based on the sensed location of the probe, and updating the presented view by removing at least a part of an object forming part of the 3D model.

Abstract: The present invention describes a new functional biomarker of vascular inflammation and its use in predicting all-cause or cardiac mortality. The invention also provides a method for stratifying patients according to their risk of all-cause or cardiac mortality using data gathered from a computer tomography scans of a blood vessel to determine a specific combination of structural and functional biomarkers of vascular inflammation and disease.

Abstract: Methods and systems are provided for determining a stiffness information of a medical instrument used during a minimally invasive interventional procedure in a vascular system of a patient by recording a three-dimensional volume image of the vascular system at least in the intervention region.

Abstract: The present disclosure pertains to the analysis of the cardiac region in CT images. Provided herein are a method, a computer system and a computer program product for the segmentation of the cardiac region in CT images.

Abstract: Example methods and systems for tomographic data analysis are provided. One example method may comprise: obtaining first three-dimensional (3D) feature volume data and processing the first 3D feature volume data using an AI engine that includes multiple first processing layers, an interposing forward-projection module and multiple second processing layers. Example processing using the AI engine may involve: generating second 3D feature volume data by processing the first 3D feature volume data using the multiple first processing layers, transforming the second 3D volume data into 2D feature data using the forward-projection module and generating analysis output data by processing the 2D feature data using the multiple second processing layers.

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

Abstract: This application discloses a map element extraction method and apparatus, and a server. The map element extraction method includes obtaining a laser point cloud and an image of a target scene, the target scene including a map element; performing registration between the laser point cloud and the image to obtain a depth map of the image; performing image segmentation on the depth map of the image to obtain a segmented image of the map element in the depth map; and converting a two-dimensional location of the segmented image in the depth map to a three-dimensional location of the map element in the target scene according to a registration relationship between the laser point cloud and the image.

Abstract: The present invention relates to scatter correction method and apparatus for dental cone-beam CT. An object of the present invention is improving quality of reconstructed images by processing the scatter correction by learning which uses Monte Carlo simulation and artificial neural network.

Abstract: A method for artifact correction in computed tomography, the method comprising: (1) acquiring a plurality of data sets associated with different X-ray energies (i.e., D1, D2, D3 . . . Dn); (2) generating a plurality of preliminary images from the different energy data sets acquired in Step (1) (i.e., I1, I2, I3 . . . In); (3) using a mathematical function to operate on the preliminary images generated in Step (2) to identify the sources of the image artifact (i.e., the artifact source image, or ASI, where ASI=f(I1, I2, I3 . . . In)); (4) forward projecting the ASI to produce ASD=fp(ASI); (5) selecting and combining the original data sets D1, D2, D3 . . . Dn in order to produce a new subset of the data associated with the artifact, whereby to produce the artifact reduced data, or ARD, where ARD=f(ASD, D1, D2, D3 . . . Dn); (6) generating a repaired data set (RpD) to keep low-energy data in artifact-free data and introduce high-energy data in regions impacted by the artifact, where RpD=f(ARD, D1, D2, D3 . . .

Abstract: An ultrasound diagnostic apparatus includes processing circuitry. The processing circuitry generates a first image based on an echo signal obtained by transmission and reception of ultrasound waves. The processing circuitry acquires a second image that is an image generated by a medical image diagnostic apparatus. The processing circuitry performs a registration of the first image and the second image, by discretely setting a plurality of relative positions of the first image and the second image within a specified range, calculating the similarity between the first image and the second image corresponding to the plurality of relative positions respectively, updating the plurality of the relative positions based on the calculation result of the similarity, and recalculating the similarity corresponding to the plurality of the updated relative positions respectively. The processing circuitry causes a display to display the images obtained after the registration.

Abstract: Systems and methods for quantitative susceptibility mapping (“QSM”) using magnetic resonance imaging (“MRI”) are described. Localized magnetic field information is used when performing the inversion to compute quantitative susceptibility maps. The localized magnetic field information can include multi-resolution subvolumes obtained by segmenting, or dividing, a field shift map. In some instances, a trained machine learning algorithm, such as a trained neural network, can be implemented to convert the localized magnetic field information into quantitative susceptibility data. These local susceptibility maps can be combined to form a composite quantitative susceptibility map of the imaging volume.

Abstract: A method may include obtaining a plurality of groups of imaging data. Each group of the plurality of groups of imaging data may be generated based on MR signals acquired by an MR scanner via scanning a subject using a diffusion sequence. The method may also include determining one or more correction coefficients associated with an error caused by the diffusion sequence for each group of the plurality of groups of imaging data. The method may also include determining, based on the one or more correction coefficients corresponding to the each group of the plurality of groups of imaging data, a plurality of groups of corrected imaging data. The method may further include determining averaged imaging data by averaging the plurality of groups of corrected imaging data in a complex domain and generating, based on the averaged imaging data, an MR image.

Abstract: An automatic field-of-view (FOV) prescription system is provided. The system includes an FOV prescription computing device that includes at least one processor electrically coupled to at least one memory device. The at least one processor is programmed to receive localizer images that depict an anatomy, and generate masks associated with the localizer images, wherein the masks represent part of the localizer images that depict the anatomy. The at least one processor is also programmed to calculate bounding boxes surrounding the anatomy based on the masks, generate an FOV based on the bounding boxes, and output the FOV.

Abstract: In medicine and dentistry, image quality affects computer vision accuracy. However, some problems are more tolerant of noise depending on disease severity and radiographic obviousness. There is a need to have a noise estimation model that adapts to each specific domain. A noise estimation model is trained to output a set of domain noise estimates for an input image, each estimate indicating an impact of noise present in the input image on a particular domain, e.g. labeling of a dental feature such as a dental anatomy, pathology, or treatment. The noise estimation model is trained by processing image pairs with a set of machine learning models for a plurality of domains, the image pairs including a raw image and a modified image obtained by adding noise to the raw image. Outputs of the set of machine learning models for the raw and modified images are compared to obtain measured noise metrics. The noise estimation model processes the modified image and is trained to estimate noise metrics.

Abstract: A GAN is trained to process input images and produce a synthetic dental image. The GAN further takes masks as inputs with each image, the masks labeling pixels of the image corresponding to dental features (anatomy and/or treatments). The GAN includes an encoder-decoder with normalization between stages of the decoder according to the masks. A synthetic image and an unpaired dental image is evaluated by a first discriminator of the GAN to obtain a realism estimate. The synthetic image and an unpaired dental image may be processed using a pretrained dental encoder to obtain a perceptual loss. The GAN is trained with the realism estimate and perceptual loss. Utilization may include modifying a mask for an input image to include or exclude a shape of a feature such that the synthetic image includes or excludes a dental feature.

Abstract: Systems and methods of enhanced display and viewing of three dimensional (3D) tomographic data acquired in tomosynthesis or tomography. A set of projection data is acquired with an image acquisition system and used to reconstruct enhanced 3D volume renderings that are viewed with motion, advanced image processing or stereotactically to assist in medical diagnosis. Various enhancements are provided for further processing the images, thereby providing additional features and benefits during image viewing.

Abstract: An aspect of the present invention allows for more accurately identifying a possible lesion in a human lung field. The aspect of the present invention includes an image obtaining section configured to obtain a chest cross-sectional image of a subject, a segmentation section configured to classify, into a plurality of segments, unit elements of the chest cross-sectional image, and an image dividing section configured to divide the chest cross-sectional image into a plurality of regions. A data deriving section is configured to derive data associated with the possible lesion, the data being derived on the basis of a segment of unit elements in the each region among the plurality of segments. An identifying section is configured to output an identification result, which is a result of identification of the possible lesion in the lung field of the subject.

Abstract: The present invention relates to X-ray image data analysis of a part of a cardiovascular system of a patient in order to estimate a level of inflammation in the part of the cardiovascular system. X-ray image data is received, a segmented model of the part of the cardiovascular system is generated and predetermined features related to inflammation are extracted from the segmented model. The extracted features are used as input to an inflammation function for calculating inflammation values of which each represents a level of inflammation in the part of the cardiovascular system. The image data analysis can improve the estimation of inflammation. Furthermore, the inflammation values can be presented to a user together with suggestions for performing actions. This can for example enable a prediction of plaque development as well as future acute coronary syndrome events.

Abstract: In a method for reconstructing magnetic resonance (MR) image data from k-space data, k-space data of an image region of a subject are provided to a computer that is also provided with multiple navigator signals for the image region of the subject. The computer sorts the k-space data into multiple bins, the multiple bins representing different motion states of the subject. For each of the multiple bins, the computer executes a compressed sensing procedure to reconstruct the MR image data from the k-space data in the respective bin. Execution of the compressed sensing procedure includes solving an optimization problem comprising a data consistency component and a transform sparsity component. Motion information is incorporated by the computer into at least one of the data consistency component and the transform sparsity component of the optimization problem.

Abstract: A method includes: generating a refine image from an incorrect image from which an incorrect label is inferred by a neural network; generating a third map by superimposing a first map and a second map, the first map indicating pixels to each of which a change is made in generating the refine image, of plural pixels in the incorrect image, the second map indicating a degree of attention for each local region in the refine image, each local region being a region that has drawn attention at the time of inference by the neural network, and the third map indicating a degree of importance for each pixel for inferring a correct label; and obtaining an added value for respective divided region in the third map by summing pixel values within the respective divided region, the respective divided region being a region divided according to a predetermined index.

Abstract: A landmark detection system can more accurately detect landmarks in images using a detection scheme that penalizes for dispersion parameters, such as variance or scale. The landmark detection system can be trained using both labeled and unlabeled training data in a semi-supervised approach. The landmark detection system can further implement tracking of an object across multiple images using landmark data.

Abstract: The present disclosure a system and method for generating a tomographic image of a subject. In some aspects, the method includes receiving an initial image acquired from a subject using the tomographic imaging system, and performing, using the initial image and a cost function model, a penalty calculation based on a spatially variant hyper-parameter. The method also includes generating an updated image using the penalty calculation, and generating a finalized image by iteratively updating the updated image until a stopping criterion is met.

Abstract: Various methods and systems are provided for multi-material decomposition for computed tomography. In one embodiment, a method comprises acquiring, via an imaging system, projection data for a plurality of x-ray spectra, estimating path lengths for a plurality of materials based on the projection data and calibration data for the imaging system, iteratively refining the estimated path lengths based on a linearized model derived from the calibration data, and reconstructing material-density images for each material of the plurality of materials from the iteratively-refined estimated path lengths. By determining path-length estimates in this way without modeling the physics of the imaging system, accurate material decomposition may be performed more quickly and with less sensitivity to changes in physics of the system, and furthermore may be extended to more than two materials.

Abstract: Dental images are processed according to a first machine learning model to determine teeth labels. The teeth labels and image are concatenated and processed using a second machine learning model to label anatomy including CEJ, JE, GM, and Bone. The anatomy labels, teeth labels, and image are concatenated and processed using a third machine learning model to obtain feature measurements, such as pocket depth and clinical attachment level. The feature measurements, anatomy labels, teeth labels, and image may be concatenated and input to a fourth machine learning model to obtain a diagnosis for a periodontal condition. Feature measurements and/or the diagnosis may be processed according to a diagnosis hierarchy to determine whether a treatment is appropriate. Machine learning models may further be used to reorient, decontaminate, and restore the image prior to processing. Machine learning models may be embodied as CNN, GAN, and cyclic GAN.

Abstract: Generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system by: generating first and second sets of one or more MR images from first and second input MR data; aligning the first and second sets of MR images using a neural network model comprising first and second neural networks, the aligning comprising: estimating, using the first neural network, a first transformation between the first and second sets of MR images; generating a first updated set of MR images from the second set of MR images using the first transformation; estimating, using the second neural network, a second transformation between the first set and the first updated set of MR images; and aligning the first set of MR images and the second set of MR images at least in part by using the first transformation and the second transformation.

Abstract: Systems for differentially detecting light from a sample in a flow stream (e.g., in a flow cytometer) across one or more dimensions are described. Light detection systems according to embodiments include a flow cell configured to propagate a sample in a flow stream, a light source configured to irradiate the sample in the flow cell and a detector system having an optical adjustment component and a detector that is configured to differentially detect light from the flow cell without a scatter bar. Systems according to certain embodiments are configured to differentially detect light by modulating one or more components of the optical adjustment component or the detector. Methods for differentially detecting light from a sample in a flow stream with a detector unit without a scatter bar are also described. Kits having two or more components for use in the subject systems are also provided.

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

Abstract: The present disclosure relates generally to medical imaging and, more particularly to systems, methods, and devices for planning and carrying out minimally invasive procedures using external devices for needle guidance and the display and manipulation of the image set when planning and performing the procedure.

Abstract: Methods and systems for determining a region of interest (ROI) in an image associated with a subject are provided. Slice information of multiple slices of an image are identified and multiple slice ranges are determined based on the slice information. Based on the slice ranges, multiple sub-images of the image are then determined. For each sub-image, a template and a corresponding probability graph are acquired, a registration result is determined by registering the sub-image with the template, and a ROI in the sub-image is identified based on the registration result. A ROI of the image can be determined by combining the ROIs of the sub-images.

Abstract: A magnetic resonance (MR) imaging device repeatedly executes a navigator pulse sequence to generate navigator data in image space as a function of time, and a motion signal of an anatomical feature that moves with a physiological cycle as a function of time is extracted from the navigator data. A concurrent physiological signal as a function of time is generated by a physiological monitor concurrently with the repeated execution of the navigator pulse sequence. A gating time offset is determined by comparing the motion signal of the anatomical feature as a function of time and the concurrent physiological signal as a function of time. The MR imaging device performs a prospective or retrospective gated MR imaging sequence using gating times defined as occurrence times of gating events detected by the physiological monitor modified by the gating time offset.

Abstract: Femoral version impacts the long-term functioning of the femoroacetabular joint. Accurate measurements of version are required for success in total hip arthroplasties and hip reconstructive surgeries. An automated algorithm is provided for identifying the major landmarks of the femur. These landmarks are then used to identify proximal axes and create a statistical shape model of the proximal femur. With six proximal axes selected, and 200 parameters (distances and angles between points) from the shape model measured, the best-fitting linear correlation is found. The difference between true version and version predicted by this model was 0.00°±5.13° with a maximum overestimation and underestimation of 11.80° and 15.35°, respectively. This model and its prediction of femoral version are a substantial improvement over pre-operative 2D or intra-operative visual estimation measures. Acetabular orientation is also determined by an automated algorithm using radii of curvature measurements.

Abstract: A medical image diagnostic system includes processing circuitry configured (to): (a) acquire a trained model generated by using, as learning data, images or signals corresponding to a first group of time-series images acquired by performing a first pre-scan on a first patient injected with a contrast agent in a first examination, as well as timing information about timing of a transition from a first pre-scan to a first main scan in a first examination, and information about appropriateness of the timing; and (b) determine appropriate timing of a transition from a second pre-scan to a second main scan by inputting, to the trained model, images or signals corresponding to a second group of time-series images acquired by performing the second pre-scan on a second patient injected with a contrast agent in the second examination different from the first examination.

Abstract: In accordance with some embodiments, systems, methods, and media for automatically segmenting and diagnosing prostate lesions using multi-parametric magnetic resonance imaging (mp-MRI) data are provided. In some embodiments, the system comprises is programmed to: receive mp-MRI data depicting a prostate, including T2w data and ADC data; provide the T2w data and ADC data as input to first and second input channels of a trained convolutional neural network (CNN); receive, from the trained CNN, output values from output channels indicating which pixels are likely to correspond to a particular class of prostate lesion, the channels corresponding to predicted aggressiveness in order of increasing aggressiveness, identify a prostate lesion in the data based on output values greater than a threshold; predict an aggressiveness based on which channel had values over the threshold; and present an indication that a prostate lesion of the predicted aggressiveness is likely present in the prostate.

Abstract: A method and apparatus is disclosed, which improves the analysis of an object within a scanned bag. Specifically, the techniques disclosed herein overcome the problem of measurement errors due to imaging artifacts, which can occur during imaging examinations like CT scans. This process also discloses a method of using an improved accuracy of data units of an object lead to more accurate classification of the material that makes up the object.

Abstract: A computed tomography (CT) system and method is provided. The CT system is used to carry out an image improvement method in which a prior or previously-acquired patient image can be used to supplement or otherwise improve an acquired CT image, wherein the acquired projection data representative of the acquired CT image might be truncated or otherwise incomplete/insufficient to accurately and stably recover the scanned object/patient.

Abstract: There is provided a method and apparatus for refining a model of an anatomical structure in an image. A model for the anatomical structure in the image is acquired. The model comprises a plurality of control points, each control point corresponding to a feature in the anatomical structure. The model is placed in the image with respect to the anatomical structure. Based on a user input received to adjust the model in the image, a position of at least one of the plurality of control points is adjusted to alter a shape of the model to the anatomical structure in the image, wherein adjustment of the position of one or more of the at least one control points is restricted based on information relating to the at least one control point.

Abstract: A computing device obtains information about a medical slide image, and determines a dataset type of the medical slide image and a panel of the medical slide image. The computing device assigns to an annotator account, an annotation job defined by at least the medical slide image, the determined dataset type, an annotation task, and a patch that is a partial area of the medical slide image. The annotation task includes the determined panel, and the panel is designated as one of a plurality of panels including a cell panel, a tissue panel, and a structure panel. The dataset type indicates a use of the medical slide image and is designated as one of a plurality of uses including a training use of a medical learning model and a validation use of the machine learning model.

Abstract: Systems and techniques for generating and/or employing a computed tomography (CT) medical imaging stroke model are presented. In one example, a system employs a convolutional neural network to generate learned medical imaging stroke data regarding a brain anatomical region based on CT data associated with the brain anatomical region and diffusion-weighted imaging (DWI) data associated with one or more segmentation masks for the brain anatomical region. The system also detects presence or absence of a medical stroke condition in a CT image based on the learned medical imaging stroke data.

Abstract: A system and method include training of an artificial neural network to generate a simulated attenuation-corrected reconstructed volume from an input non-attenuation-corrected reconstructed volume, the training based on a plurality of non-attenuation-corrected volumes generated from respective ones of a plurality of sets of two-dimensional emission data and on a plurality of attenuation-corrected reconstructed volumes generated from respective ones of the plurality of sets of two-dimensional emission data.

Abstract: A system and method for cardiac activation imaging includes non-invasively or minimally invasively acquiring data about an electrical activation of a heart of a subject using at least one sensor. An activation image of the heart of the subject is reconstructed using a weighted sparse constrained reconstruction.

Abstract: Systems, methods, and apparatuses for reducing or eliminating reverberation artifacts in images are disclosed. Systems including one or more ultrasound probes are disclosed. Apparatuses for providing registration information for an ultrasound probe or probes at different positions are disclosed. A method of combining volume images acquired at different positions to reduce or eliminate reverberation artifacts is disclosed.

Abstract: In one embodiment, a method includes accessing a plurality of verified videos depicting one or more subjects, generating, based on the verified videos, a plurality of verified-video feature values corresponding to the subjects, generating, using one or more video transformations based on the verified videos, a plurality of fake videos, generating, based on the fake videos, a plurality of fake-video feature values, training a machine-learning model to determine whether a specified video is a genuine video, wherein the machine-learning model is trained based on the verified-video feature values and the fake-video feature values. The machine-learning model may be trained to classify the specified video in a genuine-video class or a fake-video class, and the machine-learning model maybe trained based on an association between a genuine-video class and the verified-video feature values and an association between a fake-video class and the fake-video feature values.

Abstract: A method and system is disclosed for acquiring image data of a subject. The image data can be collected with an imaging system with a selected filtering characteristic. The image data can be reconstructed using reconstruction techniques.

Abstract: A music analysis method includes estimating a plurality of provisional points that are candidates for a specific point that has musical meaning in a musical piece from an audio signal of the musical piece by using a first process, selecting a part of a plurality of candidate points, which include the plurality of provisional points and a plurality of division points that divide intervals between the plurality of provisional points, as a plurality of selection points, and estimating a plurality of specific points in the musical piece from a result of calculating a probability that each of the plurality of selection points is the specific point by using a second process which is different from the first process.

Abstract: Systems and methods for detecting an aneurysm are disclosed. The method includes forming a virtual skeleton model. The virtual skeleton model has a plurality of edges with each edge having a plurality of skeleton points. Each skeleton point is associated with a subset of the plurality of blood vessel surface points. The method includes virtually fitting elliptically shaped tubules for each edge of the virtual skeleton model and identifying a potential aneurysm based on the fitted elliptically shaped tubules.

Abstract: Systems and methods for magnetic resonance imaging acceleration. The systems may perform the methods to obtain imaging data of a subject, or a portion thereof, captured by the MRI system according to an undersampling pattern; execute a first iterative procedure; determine that the first iteration number meets a first threshold; execute, in response to the determination that the first iteration number meets the first threshold, a second iterative procedure; determine that a sum of the first iteration number and the second iteration number meets a second threshold; and generate a reconstruction image of the subject, or a portion thereof, according to the processed imaging data, wherein the first threshold is lower than the second threshold.

Abstract: Systems and methods for imaging based on multiple cross-sectional images acquired at different angles are disclosed. According to an aspect, multiple cross-sectional images of an object are acquired at different angles. The method also includes registering the acquired cross-sectional images. Further, the method includes reconstructing an enhanced resolution image of the object based on the registered images. As a result of registering the images, a distortion map is generated that is coregistered with the high-resolution image. The method also includes displaying an image of the object based on the enhanced resolution image and the distortion map.

Abstract: In a learning device, method, and program for a discriminator, and a discriminator, it is possible to enable accurate learning of a discriminator that discriminates a state of an object to be observed, such as a cell. An image acquisition unit acquires a first image including an influence of a meniscus and a second image with the influence of the meniscus eliminated for the same object to be observed. Next, a training data generation unit generates training data for learning a discriminator based on the second image. Then, a learning unit learns the discriminator based on the first image and the training data.