Abstract: A lesion detection and classification artificial intelligence (AI) pipeline comprising a plurality of trained machine learning (ML) computer models is provided. First ML model(s) process an input volume of medical images (VOI) to determine whether VOI depicts a predetermined amount of an anatomical structure. The AI pipeline determines whether criteria, such as a predetermined amount of an anatomical structure of interest being depicted in the input volume, are satisfied by output of the first ML model(s). If so, lesion processing operations are performed including: second ML model(s) processing the VOI to detect lesions which correspond to the anatomical structure of interest; third ML model(s) performing lesion segmentation and combining of lesion contours associated with a same lesion; and fourth ML models processing the listing of lesions to classify the lesions. The AI pipeline outputs the listing of lesions and the classifications for downstream computing system processing.

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: Computing devices and methods are provided to automatically characterize a localization system tracker for a procedure. A starting tracker definition is refined until convergence using an iterative process and measured features of the tracker in images provided by a localization system camera. In each iteration, poses are determined from the measured features using a current tracker definition. The current definition is then refined using the poses and an optimization function. Tracker images may be captured passively, when performing other procedure steps. In a robotic context, command trajectories move a robot may be to respective poses where images to characterize the tracker are captured. An updated trajectory may be commanded to optimize convergence, additionally using the measured features at the updated pose in the iterative process. A robot kinematic model may be defined, refining a starting model using tracker poses from the localization system and sensor positions from the robot system.

Abstract: Disclosed is a system and method for segmentation of selected data. In various embodiments, automatic segmentation of fiber tracts in an image data may be performed. The automatic segmentation may allow for identification of specific fiber tracts in an image.

Abstract: Presented herein are methods, systems, devices, and computer-readable media for image annotation for medical procedures. The system operates in a parallel manner. In one flow, the system starts from clinical terms and image and applies image detection module in order to get visual candidates for related radiological finding and provide them with semantic descriptors. In the second (parallel) flow, the system produces a list of prioritized semantic descriptors (with probabilities). The second flow is done by application of a reverse inference algorithm that uses clinical terms and expert clinical knowledge. The results of both flows combined by matching module for detection the best candidate and with limited user input images can be annotated. The clinical terms are extracted from clinical documents by textual analysis.

Abstract: An ultrasound diagnostic apparatus includes an ultrasound probe and an imaging unit that transmits and receives an ultrasound beam to and from a subject using the ultrasound probe and converts a received signal output from the ultrasound probe into an image to generate an ultrasound image of the subject, according to set imaging conditions. The ultrasound diagnostic apparatus further includes a probe state determination unit that determines whether the ultrasound probe is in an aerial emission state or a contact state with the subject; and an apparatus control unit that changes the imaging conditions in a case in which the probe state determination unit determines that the ultrasound probe has been changed from the contact state with the subject to the aerial emission state and controls the imaging unit such that the ultrasound image is generated using the changed imaging conditions.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing EEG source localization. One of the methods includes obtaining brain data comprising: EEG data comprising respective channel data corresponding to each of a plurality of electrodes of an EEG sensor, and fMRI data comprising respective voxel data corresponding to each of a plurality of voxels; identifying, in a three-dimensional coordinate system, a respective location for each electrode; generating, using the respective identified locations of each electrode, data representing a location in the three-dimensional coordinate system of each voxel; determining, for each electrode, a region of interest in the three-dimensional coordinate system; and identifying, for each electrode, one or more corresponding parcellations in the brain of the subject, wherein each parcellation that corresponds to an electrode at least partially overlaps with the region of interest of the electrode.

Abstract: A method for multi-modal tissue classification of an anatomical tissue involves a generation of a tissue classification volume (40) of the anatomical tissue derived from a spatial registration and an image extraction of one or more MRI features of the anatomical tissue and one or more ultrasound image features of the anatomical tissue. The method further involves a classification of each voxel of the tissue classification volume (40) as one of a plurality of tissue types including a healthy tissue voxel and an unhealthy tissue voxel.

Abstract: A computer-implemented method for generating a motion corrected image is provided. The method includes receiving listmode data collected by an imaging system; producing two or more histo-image frames or two or more histo-projection frames based on the listmode data; providing the two or more histo-image frames or two or more histo-projection frames to an Artificial Intelligence (AI) system; receiving two or more AI reconstructed images from the AI system based on the two or more histo-image frames or the two or more histo-projection frames; and generating a motion estimation in reconstructed images by using a motion free AI reconstructed image frame as a reference frame.

Abstract: An image processing system (IPS) and related method. The system (IPS) comprises an input interface (IN) for receiving an image (IM) of an object (OB) acquired by an imaging apparatus (IA). A kernel provider (KP) of the system (IPS) is configured to provide respective scatter kernels for at least two scatter types. A scatter correction module (SCM) of the system (IPS) is configured to perform a correction in the image based on the provided at least two kernels.

Abstract: The present disclosure relates to a computed tomography apparatus, comprising: a first semiconductor device with a first radar transceiver IC, a second semiconductor device with a second radar transceiver IC, and at least one third semiconductor device with a third radar transceiver IC, which are arranged at different positions around a tomographical measurement region; a synchronization circuit, which is designed to provide a synchronization signal in order to operate the first, second and third radar transceiver IC as synchronized transmitters and receivers using the synchronization signal; and an evaluation circuit, which is designed to ascertain at least one characteristic of a medium located in the measurement region based on time-of-flight measurements of radar signals received from at least two receivers.

Abstract: For an ultrasound image acquired by an ultrasound detection system, background data with small noises are first filtered. Next, a binary image is generated by performing image binarization on the noise-reduced ultrasound image based on a first threshold value, wherein the binary image contains information associated with the body of high-echo foreground images in the ultrasound image. An output image is generated by performing foreground expansion on the binary image based on the pixel value of the ultrasound image and a second threshold value smaller than the first threshold value, wherein the output image contains information associated with not only the body but also the outline of high-echo foreground images in the ultrasound image. An improved ultrasound image is generated by performing a post-processing on the ultrasound image according to information of foreground and non-foreground region in the output image.

Abstract: A system and method is disclosed for augmenting image data of an invasive medical device having non-medical structures. An invasive medical device having markers with distinct physical shapes relative to other portions of the invasive medical device can be inserted in a patient. An imaging device can generate images of the invasive medical device within the patient. A trained model for the invasive medical device can be trained on annotated images of the invasive medical device annotated with marker and spatial information. An imaging computer system can apply the trained model to images of the invasive medical device having depictions of the markers to determine current spatial information. The depictions of the markers can be identified and correlated with the spatial information from the annotated images. The images and visual spatial information representing the spatial information of the invasive medical device can be outputted to a display.

Abstract: A system and method is disclosed for guiding invasive medical devices relative to anatomical structures. An imaging device can generate one or more images of the invasive medical device within the patient. A trained model for the invasive medical device can be trained on annotated images of the invasive medical device with at least one of orientation and position information. An imaging computer system can apply the trained model to unannotated images of the invasive medical device within the patient to determine at least one of a current orientation and a current position of the invasive medical device relative to the one or more anatomical structures within the patient. The images of the invasive medical device, visual orientation information representing the current orientation, and visual position information representing the current position of the invasive medical device relative to the anatomical structures within the patient can be outputted to a display.

Abstract: A system and method is disclosed for guiding an invasive medical device with radiation absorbing markers. The invasive medical device can include markers with different radiation absorbing properties relative to other portions of the invasive medical device. An imaging device can generate images of the invasive medical device within a patient. A trained model for the invasive medical device can be trained on annotated images of the invasive medical device annotated with marker information identifying the markers and spatial information for the invasive medical device. An imaging computer system can apply the trained model to images of the invasive medical device within the patient including depictions of the markers to determine current spatial information of the invasive medical device inside the patient. The images of the invasive medical device and visual spatial information representing the spatial information of the invasive medical device can be outputted to a display.

Abstract: This radiographic imaging apparatus (100) is provided with a control unit (5) configured to associate a projection image (70) in which at least a part of a post-removal projection image (70a) in which a contrast agent image (Ba) has been removed and at least a part of a fluoroscopic image (20) are most similar.

Abstract: An embodiment in accordance with the present invention provides a method for 3D-2D registration (for example, registration of a 3D CT image to a 2D radiograph) that permits deformable motion between structures defined in the 3D image based on a series of locally rigid transformations. This invention utilizes predefined annotations in 3D images (e.g., the location of anatomical features of interest) to perform multiple locally rigid registrations that yield improved accuracy in aligning structures that have undergone deformation between the acquisition of the 3D and 2D images (e.g., a preoperative CT compared to an intraoperative radiograph). The 3D image is divided into subregions that are masked according to the annotations, and the registration is computed simultaneously for each divided region by incorporating a volumetric masking method within the 3D-2D registration process.

Abstract: An apparatus for classifying an image according to an embodiment includes a fake image generation module receiving a classification target image and a fake image in a form in which only a background exists and no specific object exists in the classification target image, a difference of images vector generation module generating a difference of images between the classification target image and the fake image and a difference of images vector by converting the generated difference of images into preset one-dimensional matrix data, a difference of feature vectors generation module generating a difference of feature vectors between a feature vector generated based on the classification target image and a feature vector generated based on the fake image, and an image classification module classifying the classification target image based on the difference of images vector, the feature vector generated based on the classification target image, and the difference of feature vectors.

Abstract: In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated (“VTA”) by therapy delivery according to the therapy program. The score may be determined using an efficacy map comprising a plurality of voxels that are each assigned a value. In some examples, the efficacy map is selected from a plurality of stored efficacy maps based on a patient condition, one or more patient symptoms, or both the patient condition and one or more patient symptoms. In addition, in some examples, voxels of the efficacy map are assigned respective values that are associated with a clinical rating scale.

Abstract: Internal bleeding systems, devices, and methods are disclosed that include a sensing module and a processing module. The sensing module has an ultrasound module and a communication module. The ultrasound module emits and receives ultrasound energy to and reflected from tissue in a patient and generates a reflected energy signal. The communication module transmits the reflected energy signal to the processing module. The processing module analyzes the reflected energy signal to determine if it indicates the presence of blood from internal bleeding in the patient.

Abstract: A method for postural independent location of targets in diagnostic images acquired by multimodal acquisitions, compensating for deformation of soft tissues due to changing posture, includes generating a transition of a digital image of the inside of a target region from a first to a second position by correlating the position of markers placed on the external surface of the target region in a digital image of the inside of the target region and in a digital representation of the external surface of the target region acquired by optically scanning the external surface; and at a later time registering the diagnostic image of the inside of the target region, transitioned into the second position, with a diagnostic image of the same target region acquired with the target region in the second position by matching a second representation of the external surface of the target region in the second position without markers with the diagnostic image of the inside of the target region transitioned into the second posit

Abstract: In a method and apparatus for determining parameter values in voxels of an examination object using magnetic resonance fingerprinting (MRF), a first signal comparison is made of signal characteristics of established voxel time series with first comparison signal characteristics. Further synthetic comparison signal characteristics are generated from the first comparison signal characteristics and values determined in the first signal comparison. The generated further comparison signal characteristics are used to perform a further signal comparison, with which values of at least a first and a second further parameter are determined. From the further comparison signal characteristics, a value of at least one further parameter is determined that could not necessarily already be determined in the first signal comparison.

Abstract: A method is disclosed for correcting the mismatch between computed tomography (CT) scan and positron emission tomography (PET) scan modalities caused by patient respiration by selecting PET image slices aligned with the phase and amplitude in which the CT was acquired PET image slices so they are aligned with the phase and amplitude in which the CT scan was acquired.

Abstract: In a method for recording a PET image data set, an overall recording area is moved continuously through the FOV at a constant movement speed, an attenuation map of the overall recording area being used to reconstruct the PET image data record from the PET raw data. The magnetic resonance data of a slice of the patient currently located within the FOV and movement status information relating to a cyclical movement of the patient are recorded simultaneously with recording the PET raw data. A movement status class is assigned to the PET raw data and the magnetic resonance data in each case. Using the magnetic resonance data assigned to the different movement status classes, attenuation maps of the patient are determined for the different movement status classes and applied to the PET raw data assigned to the corresponding movement status class to reconstruct the PET image data set.

Abstract: In an embodiment, a simulation of the evolution of a tumor in a prostate gland of a subject is based at least on a coupled system of reaction-diffusion equations and a patient-specific geometric model of the prostate gland of the subject. The use of reaction-diffusion equations and a patient-specific geometric model provides a tumor model that predicts the expected progression of prostate cancer in the subject. The tumor model may be used to devise a customized treatment for the subject.

Abstract: Methods and systems are provided for improving model robustness and generalizability. The method may comprise: acquiring, using a medical imaging apparatus, a medical image of a subject; reformatting the medical image of the subject in multiple scanning orientations; applying a deep network model to the medical image to improve the quality of the medical image; and outputting an improved quality image of the subject for analysis by a physician.

Abstract: A system and method is disclosed for automatically determining position information for an invasive medical device based on imaging data. An imaging device can generate 2D images of the invasive medical device within the patient from a vantage point relative to the patient. A trained model for the invasive medical device can be trained on annotated 2D images of the invasive medical device with position information. An imaging computer system can apply the trained model to unannotated 2D images of the invasive medical device within the patient to determine a current position of the invasive medical device. The 2D images of the invasive medical device and visual position information representing the current position of the invasive medical device can be outputted to a display.

Abstract: Systems and methods are disclosed for identifying anatomically relevant blood flow characteristics in a patient. One method includes: receiving, in an electronic storage medium, a patient-specific representation of at least a portion of vasculature of the patient having a lesion at one or more points; receiving values for one or more metrics of interest associated with one or more locations in the vasculature of the patient; receiving one or more observed lumen measurements of the vasculature of the patient; determining the location of a diseased region in the vasculature of the patient using the received values for the one or more metrics of interest, wherein the determination of the location includes predicting or receiving one or more healthy lumen measurements of the vasculature of the patient; determining the extent of the diseased region; and generating a visualization of at least the diseased region.

Abstract: Systems and methods for generating synthesized medical images for training a machine learning based network. An input medical image in a first modality is received comprising a nodule region for each of one or more nodules, a remaining region and an annotation for each of the nodules. A synthesized medical image in a second modality is generated from the input medical image comprising the annotation for each of the nodules. A synthesized nodule image of each of the nodule regions and synthesized remaining image of the remaining region are generated in the second modality. It is determined whether a particular nodule is visible in the synthesized medical image based on the synthesized nodule image for the particular nodule and the synthesized remaining image. If at least one nodule is not visible in the synthesized medical image, the annotation for the not visible nodule is removed from the synthesized nodule image.

Abstract: A medical data processing apparatus according to one embodiment includes processing circuitry. The processing circuitry obtains a compressed channel of data generated by compressing a plurality of first medical channels of data defined by first domain representation and respectively corresponding to a plurality of components, via an intermediate channel of data defined by second domain representation. The processing circuitry decodes the compressed channel of data to a second medical channel of data defined by the first domain representation based on a conversion process from the plurality of first medical channels of data to the compressed dataset.

Abstract: An electronic device includes a display and an image capture device electronically capturing one or more images of a subject performing an activity. A wireless communication device can electronically transmit the one or more images to a remote electronic device across a network after a Procrustes superimposition operation is performed to compare the subject to a standard. The wireless communication device can electronically receive one or more electronically altered images identifying differences between one or more standard reference locations situated at one or more predefined features of the standard performing the activity and one or more corresponding subject reference locations situated at one or more predefined features of the subject performing the activity. These electronically altered images can be presented on the display of the electronic device to provide corrective feedback to the subject as how to better perform the activity.

Abstract: A system (300) includes a memory (324) configured to store an inflammation map generator module (328). The system further includes a processor (322) configured to: receive at least one of spectral projection data or spectral volumetric image data, decompose the at least one of spectral projection data or spectral volumetric image data using a two-basis decomposition to generate a set of vectors for each basis represented in the at least one of spectral projection data or spectral volumetric image data, compute a concentration of each basis within a voxel from the set of vectors for each basis, and determine a concentration of at least one of fat or inflammation within the voxel from the concentration of each basis. The system further includes a display configured to display the determined concentration of the at least one of fat or inflammation.

Abstract: A control device acquires a plurality of images including an ultrasound image and a photoacoustic image taken in an examination, selects a first image and a second image to be displayed superimposed on the first image, based on information relating to the examination, and generates an object for outputting at least information for displaying the second image superimposed on the first image to an external device.

Abstract: An image processing method is provided. The method includes obtaining at least two images, the at least two images being based on the same target object captured from different imaging angles, respectively; extracting, by using feature extraction networks included in an image processing model, target features of the at least two images, the feature extraction networks being configured to extract features of images corresponding to the different imaging angles, respectively; and determining, based on the target features, a classification result corresponding to the target object.

Abstract: This cross-section observation device bombards an object with a charged particle beam to repeatedly expose cross-sections of the object, bombards at least some of the cross-sections from among the plurality of the exposed cross-sections with a charged particle beam to acquire cross-sectional image information describing each of the at least some of the cross-sections, generates for each of these cross-sections a cross-sectional image described by the cross-sectional image information acquired, and generates a three-dimensional image in which the generated cross-sectional images are stacked together.

Abstract: In a multi-session imaging study, information from a previous imaging session is stored in a Binary Large Object (BLOB). Current emission imaging data are reconstructed into a non-attenuation corrected (NAC) current emission image. A spatial transform is generated aligning a previous NAC emission image from the BLOB to the current NAC emission image. A previous computed tomography (CT) image from the BLOB is warped using the spatial transform, and the current emission imaging data are reconstructed with attenuation correction using the warped CT image. Alternatively, low dose current emission imaging data and a current CT image are acquired, a spatial transform is generated aligning the previous CT image to the current CT image, a previous attenuation corrected (AC) emission image from the BLOB is warped using the spatial transform, and the current emission imaging data are reconstructed using the current CT image with the warped AC emission image as prior.

Abstract: A downloadable navigator for a mobile ultrasound unit having an ultrasound probe, implemented on a portable computing device. The navigator includes a trained orientation neural network to receive a non-canonical image of a body part from the mobile ultrasound unit and to generate a transformation associated with the non-canonical image, the transformation transforming from a position and rotation associated with a canonical image to a position and rotation associated with the non-canonical image; and a result converter to convert the transformation into orientation instructions for a user of the probe and to provide and display the orientation instructions to the user to change the position and rotation of the probe.

Abstract: An information processing apparatus includes a first acquisition unit that acquires a user's instruction to change medical information to be displayed on a display unit, a second acquisition unit that acquires a plurality of pieces of medical information to be sequentially displayed on the display unit, a group generation unit that groups the acquired plurality of pieces of medical information based on the acquired user's instruction, a third acquisition unit configured to acquire representative information indicating the medical information contained in the group generated by the group generation unit, and an output unit configured to output the acquired representative information to the display unit.

Abstract: A visualization system including multiple light sources, an image sensor configured to detect imaging data from the multiple light sources, and a control circuit is disclosed. At least one of the light sources is configured to emit a pattern of structured light. The control circuit is configured to receive the imaging data from the image sensor, generate a three-dimensional digital representation of the anatomical structure from the pattern of structured light detected by the imaging data, obtain metadata from the imaging data, overlay the metadata on the three-dimensional digital representation, receive updated imaging data from the image sensor, and generate an updated three-dimensional digital representation of the anatomical structure based on the updated imaging data. The visualization system can be communicatively coupled to a situational awareness module configured to determine a surgical scenario based on input signals from multiple surgical devices.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for contrast enhanced ultrasound quantification imaging are provided.

Abstract: Provided are an image fusion classification method and device. The method includes that: a three-dimensional weight matrix of a hyperspectral image is obtained by use of a Support Vector Machine (SVM) classifier (101); superpixel segmentation is performed on the hyperspectral image to obtain K superpixel images, K being a positive integer (102); the three-dimensional weight matrix is regularized by use of a superpixel-image-based segmentation method to obtain a regular matrix (103); and a class that a sample belongs to is determined according to the regular matrix (104).

Abstract: A combination unit generates a plurality of composite two-dimensional images from a plurality of tomographic images acquired by performing tomosynthesis imaging on an object using different generation methods. In this case, the combination unit generates a first composite two-dimensional image having a quality corresponding to a two-dimensional image acquired by simple imaging or a second composite two-dimensional image in which a structure included in the object has been highlighted as at least one of the plurality of composite two-dimensional images.

Abstract: Various methods and systems are provided for artifact reduction with resolution preservation. In one example, a method includes obtaining projection data of an imaging subject, identifying a metal-containing region in the projection data, interpolating the metal-containing region to generate interpolated projection data, extracting high frequency content information from the projection data in the metal-containing region, adding the extracted high frequency content information to the interpolated projection data to generate adjusted projection data, and reconstructing one or more diagnostic images from the adjusted projection data.

Abstract: A high efficiency method of processing images to provide perceptual high-contrast output. Pixel intensities are calculated by a weighted combination of a fixed number of static bounding rectangle sizes. This is more performant than incrementally growing the bounding rectangle size and performing expensive analysis on resultant histograms. To mitigate image artifacts and noise, blurring and down-sampling are applied to the image prior to processing.

Abstract: A method, a system and a computer readable medium for automatic segmentation of a 3D medical image, the 3D medical image comprising an object to be segmented, the method characterized by comprising: carrying out, by using a machine learning model, in at least two of a first, a second and a third orthogonal orientation, 2D segmentations for the object in slices of the 3D medical image to derive 2D segmentation data; determining a location of a bounding box (10) within the 3D medical image based on the 2D segmentation data, the bounding box (10) having predetermined dimensions; and carrying out a 3D segmentation for the object in the part of the 3D medical image corresponding to the bounding box (10).

Abstract: A system and method include training of an artificial neural network to generate an output data set, the training based on the plurality of sets of emission data acquired using a first imaging modality and respective ones of data sets acquired using a second imaging modality.

Abstract: In accordance with at least some embodiments of the present disclosure, a process to improve computed tomography (CT) to cone beam computed tomography (CBCT) registration is disclosed. The process may include receiving a CT image generated by CT-scanning of an object, and receiving a CBCT image generated by CBCT-scanning of the object. The process may include generating an image mask based on Digital Imaging and Communications in Medicine (DICOM) information extracted from the CBCT image. For a specific pixel in the CBCT image, the image mask contains a corresponding data-field indicating whether the specific pixel contains image data generated based on the CBCT-scanning of the object. The process may further include generating a registered image by utilizing the image mask to perform a DIR between the CT image and the CBCT image.

Abstract: A method for the artifact correction of three-dimensional volume image data of an object is disclosed. In an embodiment, the method includes receiving first volume image data via a first interface, the first volume image data being based on projection measurement data acquired via a computed tomography device, the computed tomography device including a system axis, and the first volume image data including an artifact including high-frequency first portions in a direction of a system axis and including second portions, being low-frequency relative to the high-frequency first portions, in a plane perpendicular to the system axis; ascertaining, via a computing unit, artifact-corrected second volume image data by applying a trained function to the first volume image data received; and outputting the artifact-corrected second volume image data via a second interface.

Abstract: A method for magnetic resonance imaging (MRI) is provided. The method may include obtaining scan data of a subject. The scan data may be acquired by an MR scanner at a time according to a pulse sequence. The method may include obtaining motion data of the subject. The motion data of the subject may be acquired by one or more sensors at the time. The motion data may reflect a motion state of the subject at the time. The method may also include determining, based on the motion data of the subject, a processing strategy indicating whether using the scan data to fill one or more k-space lines corresponding to the pulse sequence in a k-space. The method may further include obtaining k-space data based on the processing strategy.

Abstract: A device and a method for detecting a tumor using a medical image and diagnosing a shape and a property of the detected tumor are disclosed. An exemplary medical image-based tumor detection and diagnostic device includes: an input unit configured to obtain a medical image related to a patient; a preprocessing unit configured to preprocess the obtained medical image to observe a tumor region; an analysis unit configured to divide the preprocessed image into a plurality of regions by applying a deep neural network-based deep learning technique; and a measurement unit configured to group the plurality of divided regions by performing clustering on the plurality of divided regions. The measurement unit extracts a group feature value in respect to each of the grouped regions and derives diagnosis information related to the tumor based on the extracted group feature value.