Abstract: An ultrasound image acquisition method, comprising: according to a first collection instruction, determining a working mode of an ultrasound image acquisition system to be a first mode; in the first mode, transmitting a first ultrasound wave to interior of a to-be-detected object; obtaining a first ultrasound echo signal based on the first ultrasound wave that is returned from the interior of the to-be-detected object, and combining beams thereof to obtain first basic ultrasound wave images; according to the first basic ultrasound images, acquiring a first reference direction and the contour of a reference to-be-detected sub-object; according to the first reference direction and the contour of the reference to-be-detected sub-object, acquiring first spatial position information of the interior of the to-be-detected object; according to the first spatial position information, generating and displaying a first stereoscopic ultrasound image and/or a first planar ultrasound image based on the interior of the to-b

Abstract: The generation device 100 according to the present application includes an acquisition unit 131 and a generation unit 134. The acquisition unit 131 acquires a first pathological image captured and an annotation that is information added to the first pathological image and is meta information related to the first pathological image. The generation unit 134 generates learning data for evaluating pathological-related information based on a second pathological image according to the second pathological image different from the first pathological image, the learning data being learning data obtained by transcribing the annotation in a manner corresponding to the second pathological image.

Abstract: Image series transformation for optimal compressibility is performed with neural upsampling and error resilience. It incorporates a novel correlation network composed of convolutional layers for feature extraction and a channel-wise transformer with attention to capture complex inter-channel dependencies. The system includes an angle optimizer to further enhance the compressibility of an image and an error resilience subsystem to improve robustness against transmission errors and data loss. The convolutional layers extract multi-dimensional features from the image, while the channel-wise transformer learns global inter-channel relationships. The error resilience subsystem applies forward error correction coding, data partitioning based on importance, and embeds error concealment hints. This hybrid approach addresses both local and global features, mitigates compression artifacts, improves image quality, and enhances data integrity during transmission.

Abstract: A method and system for identifying an implant in an x-ray image of an anatomy shown on a display screen. The method includes: displaying the image including an artifact of the implant; prompting a user to enter parameters of the implant; displaying a rendering of the implant over the image; prompting the user to position the rendering on the artifact at an accurate position corresponding to an actual position of the implant; alerting the user when the rendering is at an inaccurate position; prompting the user to reposition the rendering from the inaccurate position to the accurate position; alerting the user when the rendering is maneuvered by the user to the accurate position; and after the rendering has been maneuvered to the accurate position by the user, reconstructing the x-ray image to replace the artifact with the rendering affixed in the accurate position.

Abstract: Herein disclosed are robotic dentistry methods comprising tooth extraction and crown preparation. Robotic tooth extraction may comprise determining if the correct tooth is targeted, based on a scanned image; determining a decay state of the tooth based on the scanned image; selecting an extraction technique determined as a function of the decay state; and extracting the tooth using the selected extraction technique. Robotic crown preparation may comprise receiving an initial reduction plan to reduce a tooth; presenting, to a dentist for approval, the initial reduction plan for approval; upon approval, reducing the tooth according to the approved initial reduction plan; upon rejection, modifying the initial reduction plan based on dentist input; receiving the modified reduction plan to reduce the tooth; presenting, to a dentist for approval, the modified reduction plan for approval; and upon approval of the modified reduction plan, reducing the tooth according to the approved modified reduction plan.

Abstract: A medical image data processing apparatus comprising processing circuitry configured to: receive medical image data; segment a body part included in the medical image data into multiple regions; refine or constrain the segmentation based on at least one plane to obtain a segmentation that includes at least one boundary or other feature having a desired property.

Abstract: A training data modification system (TDM) for machine learning and related methods. The system comprises a data modifier (DM) configured to perform a modification operation to modify medical training X-ray imagery of a patient. The modification operation causes image structures in the modified medical training imagery. The image structure is representative of a property of i) a medical procedure, ii) an image acquisition operation by an X-ray-based medical imaging apparatus (IA), iii) an anatomy of the patient.

Abstract: A method for diagnostic imaging reconstruction uses a prior image xpr from a scan of a subject to initialize parameters of a neural network which maps coordinates in image space to corresponding intensity values in the prior image. The parameters are initialized by minimizing an objective function representing a difference between intensity values of the prior image and predicted intensity values output from the neural network. The neural network is then trained using subsampled (sparse) measurements of the subject to learn a neural representation of a reconstructed image. The training includes minimizing an objective function representing a difference between the subsampled measurements and a forward model applied to predicted image intensity values output from the neural network. Image intensity values output from the trained neural network from coordinates in image space input to the trained neural network are computed to produce predicted image intensity values.

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: Provided is a medical image analysis method including acquiring a target medical image, detecting a target joint spacing region from the target medical image, acquiring a first value related to a width of a joint part from the target medical image, acquiring a second value related to joint spacing from the target joint spacing region, and calculating a target joint condition indicator indicating a joint condition based on the first value and the second value.

Abstract: A computer-implemented method for facilitating opportunistic screening for cardiomegaly includes obtaining a set of computed tomography (CT) images. The set of CT images captures at least a portion of a heart of a patient, and the set of CT images is captured for a purpose independent of assessing cardiomegaly. The method further includes using the set of CT images as an input to an artificial intelligence (AI) module configured to determine a heart measurement based on CT image set input. The method also includes obtaining heart measurement output generated by the AI module and, based on the heart measurement output, classifying the patient into one of a plurality of risk levels for cardiomegaly. The classification is operable to trigger additional action based on the corresponding risk level for the patient.

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 with a resolution that is comparable to an MRI machine.

Abstract: In part, the disclosure relates to computer-based methods, devices, and systems suitable for detecting a delivery catheter using intravascular data. In one embodiment, the delivery catheter is used to position the intravascular data collection probe. The probe can collect data suitable for generating one or more representations of a blood vessel with respect to which the delivery catheter can be detected.

Abstract: An exemplary image registration system identifies a subsurface structure at a surgical site based on subsurface imaging data from a subsurface image scan at the surgical site. The image registration system uses the identified subsurface structure at the surgical site for a registration of endoscopic imaging data from an endoscopic imaging modality with additional imaging data from an additional imaging modality. Corresponding systems and methods are also disclosed.

Abstract: A medical image processing apparatus and a medical image processing method are provided which are capable of clearly presenting a distal end of a medical device in a tomosynthesis image of an object under examination into which the medical device is inserted. The medical image processing apparatus handles a tomosynthesis image generated using a plurality of projection images obtained by imaging an object under examination in an angle range of less than 180 degrees, and includes: a storage section for pre-storing blur data at individual imaging space coordinates; and a correction section for correcting the tomosynthesis image using the blur data.

Abstract: A computer-implemented method of an embodiment is for automatically estimating and/or correcting an error due to artifacts in a multi-energy computed tomography result dataset relating to at least one target material. In an embodiment, the method includes determining at least one first subregion of the imaging region, which is free from the target material and contains at least one, in particular exactly one, second material with known material-specific energy dependence of x-ray attenuation; for each first subregion, comparing the image values of the energy dataset for each voxel, taking into account the known energy dependence, to determine deviation values indicative of artifacts; and for at least a part of the at least one remaining second subregion of the imaging region, calculating estimated deviation values by interpolating and/or extrapolating from the determined deviation values in the first subregion, the estimated deviation values being used as estimated error due to artifacts.

Abstract: A method for denoising an image, an apparatus, and a computer-readable storage medium. The method includes: dividing a to-be-processed image into a plurality of image areas; for each image area, expanding an edge of the image area to obtain an expanded area; wherein the expanded area comprises the image area and an edge area, and the edge area comprises pixels in another image area adjacent to the image area; for each expanded area, performing denoising processing on the expanded area through a preset algorithm to obtain a denoised expanded area; wherein the denoised expanded area comprises a denoised image area and a denoised edge area; and abandoning the denoised edge area in each denoised expanded area, and retaining and combining all the denoised image areas to form a denoised image.

Abstract: A flexible coil includes a flexible substrate with a plurality of holes extending through the substrate. Each hole is configured to receive at least a portion of a fastener extending through the hole. Each fastener engages the flexible substrate and an antenna loop to positively retain the antenna loop to the flexible substrate. The holes are arranged in the flexible substrate to align each antenna loop with respect to the other antenna loops mounted to the flexible substrate. The fasteners are removably mounted to the flexible substrate such that the fastener positively retains the antenna loop to the flexible substrate when mounted to the flexible substrate but allows individual antenna loops to be removed from the flexible substrate when the fastener is removed from the flexible substrate. Multiple fasteners are provided for each antenna loop and are spaced apart from each other and positioned along the length of the loop.

Abstract: A medical image processing method and processing apparatus, and a computer readable storage medium. The method includes: obtaining a to-be-processed image; performing a feature extraction on the to-be-processed image to obtain a corresponding feature image; and re-determining a pixel value of each pixel in the to-be-processed image based on first information and second information of a corresponding pixel in the feature image, and processing the to-be-processed image; wherein the first information is information of a pixel adjacent to the corresponding pixel in the features image, and the second information is information of a pixel that is not adjacent to and is similar to the corresponding pixel in the features image.

Abstract: At least one example embodiment relates to a computer-implemented method for providing stroke information, the method comprising receiving computed tomography imaging data of an examination area of a patient, the examination area of the patient comprising a plurality of brain regions, at least one brain region of the plurality of brain regions being affected by a stroke, receiving brain atlas data, generating registered imaging data based on the computed tomography imaging data and the brain atlas data, the registered imaging data being registered to the brain atlas data, generating the stroke information regarding the stroke based on a set of algorithms and the registered imaging data, and providing the stroke information.

Abstract: A learning image generation device including: a supervised data acquisition unit that acquires supervised data including a learning image and a correct learning image in which a correct region is defined in the learning image as a pair; and a variation learning image generation unit that generates a variation learning image in which a pixel value of a pixel belonging to the correct region is varied within a limitation range of an allowable pixel value of the pixel belonging to the correct region in the learning image.

Abstract: An apparatus and method for generating a three-dimensional (3D) model of cardiac anatomy including an overlay. The apparatus includes at least a processor configured receive a set of images of a cardiac anatomy pertaining to a subject, generate a set of shape parameters based on the set of images, wherein generating the set of shape parameters includes receiving cardiac geometry training data including a plurality of image sets as input correlated to a plurality of shape parameter sets as output, training a shape identification model using the cardiac geometry training data, and generating the set of shape parameters using the shape identification model, generate a 3D model of the cardiac anatomy based on the set of shape parameters, generate a map by determine a level of uncertainty at each location of a plurality of locations on the generated 3D model, and overlay the map onto the 3D model.

Abstract: The present invention relates to the identification of one or more candidate signs indicative of an NTRK oncogenic fusion within patient data. Subject matter of the present invention are a computer-implemented method, a system, and a non-transitory computer-readable storage medium for determining a probability value from patient data associated with a subject patient, the probability value indicating the probability of the subject patient suffering from cancer caused by a mutation of a neurotrophic receptor tyrosine kinase (NTRK) gene.

Abstract: A system and a method for measuring a maturation stage of a biological organ are based on quantitative MR maps for the organ. The method includes acquiring with a first interface and for a subject, a quantitative MR map for the organ. The quantitative MR map includes voxels each characterized by a quantitative value. The quantitative value of each voxel represents a measurement of a physical or physiological property of a tissue of the biological organ for the voxel. The method also includes applying to the quantitative map a trained function to estimate the subject organ maturation stage, and the trained function outputting an age. The method provides with a second interface the maturation stage of the organ of the subject as being the output age.

Abstract: Devices, systems, and methods used to register medical image data with anatomical positions are disclosed. The image data is captured by an imaging system over a period of time. The positions are determined by one or more modalities over the period of time. The positions are collected into position data, and determinations are made as to which position data is best for registering the image data with patient anatomy and for reconstructing 3D images of the patient anatomy.

Abstract: A method for processing breast tissue image data includes processing image data of a patient's breast tissue to generate a high-dimensional grid depicting one or more high-dimensional objects in the patient's breast tissue; determining a probability or confidence of each of the one or more high-dimensional objects depicted in the high-dimensional grid; and modifying one or more aspects of at least one of the one or more high-dimensional objects based at least in part on its respective determined probability or confidence to thereby generate a lower-dimensional format version of the one or more high-dimensional objects. The method may further include displaying the lower-dimensional format version of the one or more high-dimensional objects in a synthesized image of the patient's breast tissue.

Abstract: Systems and methods of guiding a clinician in a medical procedure for a nodule involve receiving three-dimensional (3D) image data, generating a volumetric vector map based on the 3D image data, and displaying the volumetric vector map in a way, e.g., via a heat map, that assists a clinician in performing a medical procedure. The systems and methods involve identifying volumetric parameters of the nodule in the 3D image data, generating a voxel map based on the volumetric parameters, identifying a maximum attenuation value in the 3D space of the voxel map, applying a differential equation, e.g., a gradient, to the 3D space of the voxel map from a voxel with the maximum attenuation value to other voxels within the voxel map, and generating a volumetric vector map based on the result of applying the differential equation.

Abstract: A computer-implemented method and system for predicting orthodontic results based on landmark detection includes: acquiring a crown point cloud, a set of tooth point clouds, and tooth labels; extracting global dentition features and local tooth features, performing feature fusion on the global dentition features, tooth labels of individual teeth, and the local tooth features of the individual teeth to obtain fused features of the individual teeth, and extracting landmarks of the individual teeth and offset vectors from points in the tooth point clouds to the landmarks; fusing the landmarks of the individual teeth and the tooth point clouds, extracting tooth attention features, acquiring dentition attention features, and fusing the dentition attention features, the global dentition features, and the local tooth features to obtain a point cloud with fused landmarks; and acquiring pre- and post-treatment rigid transformation parameters, and obtaining a post-treatment crown model prediction result.

Abstract: Systems and methods for performing an assessment of a lesion are provided. A plurality of input medical images of a lesion is received. The plurality of input medical images comprises an initial input medical image and one or more additional input medical images. The initial input medical image comprises a region of interest around the lesion. A mask of the lesion is curated for the initial input medical image based on the region of interest and a set of candidate masks. The region of interest in the initial input medical image is propagated to the one or more additional input medical images based on prior registration transformations. A mask of the lesion is curated for each of the one or more additional input medical images based on the propagated regions of interest and the set of candidate masks. One or more assessments of the lesion are performed based on the mask for the initial input medical image, the masks for the one or more additional input medical images, and prior assessments of lesions.

Abstract: A system and method for reviewing a tomosynthesis image data set comprising volumetric image data of a breast, the method comprising, in one embodiment, causing an image or a series of images from the data set to be displayed on a display monitor and selecting or indicating through a user interface an object or region of interest in a presently displayed image of the data set, thereby causing an image from the data set having a best focus measure of the user selected or indicated object or region of interest to be automatically displayed on the display monitor.

Abstract: The following relates generally to motion prediction in magnetic resonance (MR) imaging. In some embodiments, a “modular” approach is taken to motion correction. That is, individual motion sources (e.g., a patient's breathing, heartbeat, stomach contractions, peristalsis, and so forth) are accounted for individually in the motion correction. In some embodiments, to correct for a particular motion source, a reference state is created from a volume of interest (VOI), and other states are created and deformably aligned to the reference state.

Abstract: A system includes a machine-learning network. The network includes an input interface configured to receive input data from a sensor. The processor is programmed to receive the input data, generate a perturbed input data set utilize the input data, wherein the perturbed input data set includes perturbations of the input data, denoise the perturbed input data set utilizing a denoiser, wherein the denoiser is configured to generate a denoised data set, send the denoised data set to both a pre-trained classifier and a rejector, wherein the pre-trained classifier is configured to classify the denoised data set and the rejector is configured to reject a classification of the denoised data set, train, utilizing the denoised input data set, the a rejector to achieve a trained rejector, and in response to obtaining the trained rejector, output an abstain classification associated with the input data, wherein the abstain classification is ignored for classification.

Abstract: A personalized naturally designed mitral valve prosthesis and a method for manufacturing the such to precisely fit a specific patient for which the valve prosthesis is made for, is provided. The method includes measuring size and shape of a mitral valve of the specific patient by using imaging means, building a 3D model of the personalized mitral valve prosthesis, optimizing the 3D model using FEM method and fabricating the personalized mitral valve prosthesis by cutting and connecting the annular ring, leaflets and chords to form a personalized prosthesis mitral valve.

Abstract: Methods and systems are provided for identifying shape-based atypical segmentations. In one example, a method includes receiving a segmentation of a region of interest (ROI) of a medical image, the segmentation output by a segmentation model, calculating a confidence metric of the segmentation that indicates how well a shape of the segmentation can be encoded by an encoding of one or more principal modes of shape variation of a set of previously determined segmentations of the ROI, and responsive to the confidence metric meeting a predetermined condition relative to a threshold, displaying the segmentation, storing the segmentation, and/or using the segmentation in one or more downstream processes; otherwise, prompting a user to perform a manual segmentation.

Abstract: A system for facilitating lesion analysis accesses a first data structure comprising a plurality of entries including anatomic location information and annotation information associated with a plurality of lesions represented in a first set of cross-sectional images. The system displays a respective representation of each of the plurality of entries and presents a second set of cross-sectional images. The system receives user input triggering selection of a particular entry of the plurality of entries of the first data structure.

Abstract: Described herein are a system and methods for efficiently using depth and image information for a space to generate a 3D representation of that space. In some embodiments, an indication of one or more points is received with respect to image information, which is then mapped to corresponding points within depth information. A boundary may then be calculated to be associated with each of the points based on the depth information at, and surrounding, each point. Each of the boundaries are extended outward until junctions are identified as bounding the boundaries in a direction. The system may determine whether the process is complete or not based on whether any of the calculated boundaries are currently unlimited in extent in any direction. Once the system determines that each of the boundaries is limited in extent, a 3D representation of the space may be generated based on the identified junctions and/or boundaries.

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: An image processing method includes: extracting a candidate modality image to be visualized from a data group of a single or a plurality of modality images; first associating separate modality data referring to an extracted modality image, with the extracted modality image; uniquely determining an image to be visualized, based on the modality image extracted at the extracting and an association result of the first associating; second associating different modality data being not associated at the first associating, with the modality image extracted at the extracting; and displaying the different modality data on the modality image extracted at the extracting.

Abstract: Disclosed is a computer-implemented method of segmenting a medical patient image using an atlas and relating the segmentation result to a model of possible geometric changes to the segmentation result (e.g. for correcting the position of the segmentation of anatomical structures) which consider for example anatomical limitations. The thus-related segmentation result may be used as a basis for changing and/or correcting the position, shape and/or orientation of at least parts of the segmentation result, e.g. by user interaction. The invention also relates to an atlas data set comprising information such as values of the variables of the model of possible geometric changes in relation to the positions of anatomical structures in the atlas.

Abstract: Using deep learning, imaging harmonization among images produced with differing PET tracers is achieved. A method may include providing an original PET image of the brain using an original PET tracer, providing a target PET tracer, and converting, using a deep learning neural network, the original PET image into a target PET image simulating the image that would be obtained had the target PET tracer been used.

Abstract: A system and related methods are provided for processing a diagnostic test article, for example, one that is used for medical or substance testing. The system and methods combine a diagnostic test article with a software application that is integrated with each of a mobile communication device and a cloud-based network via an internet connection for achieving certain advantages and improvements over conventional point of use diagnostics.

Abstract: One or more example embodiments provides a method for evaluating an angiographic dual-energy computed tomography dataset recorded using a contrast agent comprising iodine to determine a quantitative calcium score.

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: The present disclosure provides an operation method of a PET (positron emission tomography) quantitative localization system, which includes steps as follows. The PET image and the MRI (magnetic resonance imaging) of the patient are acquired; the nonlinear deformation is performed on the MRI and the T1 template to generate deformation information parameters; the AAL (automated anatomical labeling) atlas is deformed to an individual brain space of the patient, so as to generate an individual brain space AAL atlas, where the AAL atlas and the T1 template are in a same space; lateralization indexes of the ROIs of the individual brain space AAL atlas corresponding to the PET image normalized through the gray-scale intensity are calculated; the lateralization indexes are inputted into one or more machine learning models to analyze the result of determining a target.

Abstract: A system and method for performing optoacoustic classification prediction is provided. The method utilizes one or more processors in connection with receiving OA feature scores in connection with OA images collected from a patient examination for a volume of interest. The volume of interest includes a lesion. The method applies the OA feature scores to a classification model to obtain a predictive result indicative of a trait of the lesion.

Abstract: A system and method for assisting colonoscopic image diagnosis based on artificial intelligence include a processor, which is configured to analyze each video frame of a colonoscopic image using at least one medical image analysis algorithm and detects a finding suspected of being a lesion in the video frame. The processor calculates the coordinates of the location of the finding suspected of being a lesion. The processor generates display information, including whether the finding suspected of being a lesion is present and the coordinates of the location of the finding suspected of being a lesion.

Abstract: A computer-implemented method may identify attributes of electronic images and display the attributes. The method may include receiving one or more electronic medical images associated with a pathology specimen, determining a plurality of salient regions within the one or more electronic medical images, determining a predetermined order of the plurality of salient regions, and automatically panning, using a display, across the one or more salient regions according to the predetermined order.

Abstract: A method for determining a probability of developing lung cancer by using an artificial intelligence model, includes: receiving, by an analysis device, a chest fluorodeoxyglucose (FDG) PET/CT image for a sample; inputting, by the analysis device, the F-18 FDG PET/CT image to a first classification neural network, and outputting prediction information related to development of lung cancer for the sample; and predicting, by the analysis device, a probability of developing lung cancer for the sample on the basis of the prediction information. The first classification neural network is trained by using chest F-18 FDG PET/CT images for healthy people and training images excluding lung cancer regions from chest F-18 FDG PET/CT images for lung cancer patients.

Abstract: For reconstruction in medical imaging, self-consistency using data augmentation is improved by including data consistency. Artificial intelligence is trained based on self-consistency and data consistency, allowing training without supervision. Fully sampled data and/or ground truth is not needed but may be used. The machine-trained model is less likely to reconstruct images with motion artifacts, and/or the training data may be more easily gathered by not requiring full sampling.

Abstract: Systems and methods for calculating external bone loss for alignment of pre-diseased joints comprising: generating a three-dimensional (“3D”) computer model of an operative area from at least two two-dimensional (“2D”) radiographic images, wherein at least a first radiographic image is captured at a first position, and wherein at least a second radiographic image is captured at a second position, and wherein the first position is different than the second position; identifying an area of bone loss on the 3D computer model; and applying a surface adjustment algorithm to calculate an external missing bone surface fitting the area of bone loss.