Abstract: A neural network system implements a model for generating an output image based on a received input image. The model is learned through a training process during which parameters associated with the model are adjusted so as to maximize a difference between a first image predicted using first parameter values of the model and a second image predicted using second parameter values of the model, and to minimize a difference between the second image and a ground truth image. During a first iteration of the training process the first image is predicted and during a second iteration the second image is predicted. The first parameter values are obtained during the first iteration by minimizing a difference between the first image and the ground truth image, and the second parameter values are obtained during the second iteration by maximizing the difference between the first image and the second image.

Abstract: An X-ray imaging apparatus includes an imaging device configured to capture a camera image of a target; a controller configured to stitch a plurality of X-ray images of respective divided regions of the target to generate one X-ray image of the target; and a display configured to display a settings window that provides a GUI for receiving a setting of an X-ray irradiation condition for the respective divided regions, and display the camera image in which positions of the respective divided regions are displayed.

Abstract: A radiographic imaging system having a number of useable digital radiographic detectors selectively activates one of the detectors to capture a radiographic image. The DR detectors each include a movement detector configured to transmit a movement signal when a movement of the corresponding detector is sensed. A portion of the system is disabled in response to receiving a movement signal transmitted from a motion sensor in a detector other than the active detector.

Abstract: An analysis method is for automatically determining radiological result data from radiology data sets. In an embodiment, the method includes provisioning a first radiology data set at least based on X-ray data of a first X-ray energy spectrum; provisioning at least one second radiology data set at least based on X-ray data of a second X-ray energy spectrum; provisioning an analysis unit including a neural network, to analyze radiology data sets including an input layer with a plurality of cells, a number of intermediate layers and an output layer representing a radiological result; analyzing the first radiology data set and the at least one second radiology data set, at least subsets of the first radiology data set and the at least one second radiology data set being assigned to different cells of the input layer for joint processing in the neural network; and acquiring result data on the output layer.

Abstract: Methods and apparatus for computer-aided prostate condition diagnosis are disclosed. An example computer-aided prostate condition diagnosis apparatus includes memory to store instructions and a processor. The example processor can detect a lesion from an image of a prostate gland and generate a mapping of the lesion from the image to a sector map, the generating the mapping of the lesion comprising identifying a depth region of the lesion, wherein the depth region indicates a location of the lesion along a depth axis. The processor can also provide the sector map comprising a representation of the lesion within the prostate gland mapped from the image to the sector map.

Abstract: A method is disclosed for generating an image. An embodiment of the method includes detecting a first projection data set via a first group of detector units, the first group including a first plurality of first detector units, each having more than a given number of detector elements; detecting a second projection data set via a second group of detector units, the second group including a second plurality of second detector units, each including, at most, the given number of detector elements; reconstructing first image data based on the first projection data set; reconstructing second image data based on the second projection data set; and combining the first image data and the second image data. A non-transitory computer readable medium, a data processing unit, and an imaging device including the data processing unit are also disclosed.

Abstract: Provided is a dental image registration device comprising: an outermost boundary detection unit for detecting, from first teeth image data, a first outermost boundary region which is the outermost boundary region of dentition, and detecting, from second teeth image data, a second outermost boundary region which is the outermost boundary region of the dentition; and an image registration unit which registers the first and second teeth image data on the basis of a first inscribed circle inscribed within the first outermost boundary region and a second inscribed circle inscribed within the outermost boundary region, or registers the first and second teeth image data on the basis of a first central point of the first outermost boundary region and a second central point of the second outermost boundary region.

Abstract: Systems and methods for determining a target region of interest (ROI) for image registration is provided. The methods may include obtaining a target image of a subject including a target portion of the subject and obtaining feature information related to a movement of one or more feature portions of the subject. The methods may further include identifying, from the one or more feature portions, one or more reference portions of the subject based on the feature information. A position of the target portion of the subject is unrelated to a movement of the one or more reference portions. The methods may further include determining, in the target image, the target ROI based on corresponding feature information related to the one or more reference portions.

Abstract: Radiation beam alignment for a LINAC including (1) for each beam alignment parameter value of a set: (a) with a beam alignment parameter of a LINAC set to the beam alignment parameter value, using a gantry to generate a radiation beam; (b) using an imaging device to acquire a radiation transmission image indicative of a radiation field of the radiation beam after passing by a radiation opaque marker; (c) determining a location of a beam axis of the radiation beam and a center of a shadow of the marker based on the radiation transmission image; and (d) determining a target-to-beam-axis distance between the location of the beam axis and the center of the shadow of the radiation opaque marker; and (2) determining an optimum beam alignment parameter value based on the beam alignment parameter values and the target-to-beam-axis distances determined with the LINAC set to the beam alignment parameter values.

Abstract: For training a machine learning system for representing a patient body a plurality of stored medical imaging data sets each representing at least a part of a respective patient are obtained. A first one of the plurality of stored medical imaging data sets represents a different part of the patient body than a second one of the plurality of stored medical imaging data sets. A plurality of landmarks in the stored medical imaging data sets are estimated, and each of the stored medical imaging data sets are aligned to a predefined pose using the plurality of landmarks. A plurality of points in the aligned medical imaging data sets are sampled, and the machine learning system is trained based on at least the plurality of points. The learned parameters of the machine learning system are then stored and used in a method for inferring a body representation.

Abstract: Computer systems and computer-implemented analysis methods may be used for assistance in planning and/or performing a medical procedure, such as percutaneous nephrolithotomy or percutaneous nephrolithotripsy. The method may include receiving one or more radiographic images of an anatomical structure of a patient, generating a display of the radiographic image(s), generating at least one request for user input to identify features of the anatomical structure, receiving user input identifying the features of the anatomical structure, identifying at least one access plan based on the received user input, and generating a display of the identified access plan(s) associated with the radiographic image(s). The method may include generating a patient template that indicates an insertion site according to the identified access plan(s).

Abstract: Various methods and systems are provided for mammogram imaging. In one example, a method for an x-ray mammography system includes determining, based on a pre-exposure image of a subject acquired with the x-ray mammography system before acquisition of one or more main images, that the subject has an implant; automatically adjusting one or more imaging parameters in response to determining that the subject has the implant; and acquiring the one or more main images with the adjusted one or more imaging parameters.

Abstract: A spinal surgery training process includes the steps of capturing a plurality of 2D images for each of a plurality of spines, generating a curve of each spine from the respective 2D images based on locations of select vertebrae in each of the spines, grouping the spines into one of a number of groups based on similarity to produce groups of spines having similarities, performing the capturing, generating, determining and grouping steps at least once prior to surgery and at least once after surgery to produce pre-operative groups and their resultant post-operative groups, and assigning surgical methods and a probability to each of the post-operative groups indicating the probability that a spinal shape of the post-operative group can be achieved using the surgical methods. An outcome prediction process for determining surgical methods can be implemented once the training process is complete.

Abstract: In acquisition of spatial transcriptomic information, a plurality of images representing a common field of view of a sample are obtained and registered. Each pixel of the registered images is decoded by identifying a code word from a plurality of code words in a code book that provides a best match to data values in the plurality of registered images for the pixel. For each code word identified as a best match and each pixel, whether a bit ratio for an image word for the pixel meets a threshold for the code word is determined. The image word is formed from the data values in the plurality of registered images for the pixel. For at least one pixel that is determined to meet the threshold, a gene associated with the code word is determined. Pixels for which the bit ratio does not meet the threshold are screened.

Abstract: A medical care support device includes an acquisition unit that acquires medical information including medical image data representing a medical image obtained by imaging a bone of a subject and age information representing an age of the subject in a case where the medical image is imaged, a derivation unit that derives presence or absence of a fracture in a growth plate of the subject, based on the medical information acquired by the acquisition unit and a learned model learned in advance using a plurality of pieces of learning medical information including the medical image data and the age information, and a warning information output unit that outputs warning information representing a warning in a case where the growth plate of the subject is fractured.

Abstract: A compression plate of a mammography apparatus has an upper surface which is irradiated with radiation, a lower surface which is opposite to the upper surface and comes into contact with the breast, and a surface which is parallel to the upper surface between the upper surface and the lower surface. In the compression plate, a compression plate scale for identifying an in-plane position of each surface is given to any one of the surfaces. A first display control unit of a console performs control to display a radiographic image captured by the mammography apparatus on a display unit. A second display control unit of the console performs control to display an image scale which indicates a position on the radiographic image corresponding to the in-plane position of the compression plate on the radiographic image displayed on the display unit.

Abstract: Method and system for grading a medical image. For example, a system for grading a medical image comprising a grading network configured to provide a grading result corresponding to the medical image based on at least the medical image and/or a list of lesion candidates generated by a lesion identification network.

Abstract: A system for determining accuracy of a surgical procedure to implant an implant on a patient bone. The system including at least one computing device configured to perform the following steps. Receive preoperative patient data including preoperative images of the patient bone and planned implant position and orientation data. Receive postoperative patient data including postoperative images of the patient bone and an implant implanted on the patient bone. Segment the patient bone and the implant from the postoperative images of the patient bone and the implant. Register separately the patient bone and the implant from the postoperative images to the patient bone from the preoperative images. And compare an implanted position and orientation of the implant from the postoperative images relative to the patient bone from the preoperative images to the planned implant position and orientation data relative to the patient bone from the preoperative images.

Abstract: A method for calibrating an image sensor begins by illuminating a portion of the image sensor with an input light spectrum, where the input light spectrum includes light of known wavelength and intensity. The method continues by sampling an output for each optical sensor of the image sensor, where each optical sensor is associated with one or more optical filters and where each optical filter being associated with a group of optical filters of a plurality of groups of optical filters. Each optical filter of a group of optical filters is configured to pass light in a different wavelength range and at least some optical filters in different groups of the plurality of groups of optical filters are configured to pass light in substantially a same wavelength range.

Abstract: A computer-implemented image analysis method and system. The method comprises: quantifying one or more features segmented and identified from a medical image of a subject; extracting clinically relevant features from non-image data pertaining to the subject; assessing the features segmented from the medical image and the features extracted from the non-image data with a trained machine learning model; and outputting one or more results of the assessing of the features.

Abstract: A computer implemented method, a data processing system and a computer program product to determine a likelihood of pneumothorax of a patient, the method including assessing a digital image of a chest x-ray of the patient, applying a standard detection pipeline to the digital image, applying a confounding factor detector to the digital image, and applying a high-resolution detection pipeline to the digital image.

Abstract: A method, applied to an apparatus for mammographic multi-view mass identification, includes receiving a main image, a first auxiliary image, and a second auxiliary image. The main image and the first auxiliary image are images of a breast of a person, and the second auxiliary image is an image of another breast of the person. The method further includes detecting the nipple location based on the main image and the first auxiliary image; generating a first probability map of the main image based on the main image, the first auxiliary image, and the nipple location; generating a second probability map of the main image based on the main image, the second auxiliary image, and the nipple location; and generating and outputting a fused probability map based on the first probability map and the second probability map.

Abstract: A robotic catheter procedure system includes a bedside system and a workstation. The bedside system includes an actuating mechanism configured to engage and to impart movement to a percutaneous device. The workstation includes a user interface and a control system configured to be operatively coupled to the user interface, the bedside system, and a medical imaging system. The control system is responsive to a first input and to a second input, and the user interface receives the second input from a user. The control system is configured to generate a first control signal to the medical imaging system based on the first input, and the medical imaging system captures at least one image in response to the first control signal. The control system is configured to generate a second control signal to the actuating mechanism based on the second input, and the actuating mechanism causes movement of the percutaneous device in response to the second control signal.

Abstract: An apparatus for analyzing coronary vessels and a corresponding method are provided in which simulated pullback data obtained from (non-invasively) acquired diagnostic images is co-registered with invasively acquired intravascular pullback data and the co-registration is used to identify disparities in the pullback data obtained using the two modalities. These disparities allow for deriving further information about the vessel geometry and/or the blood flow through the vessel. They may therefore be used to improve the physiological model.

Abstract: A medical scan triaging system is operable to train a computer vision model and to generate abnormality data indicating abnormality probabilities for medical scans via the computer vision model. A first subset of medical scans is determined by identifying medical scans with abnormality probabilities greater than a first probability value of a triage probability threshold. A second subset of medical scans is determined by identifying medical scans with abnormality probabilities less than the first probability value. An updated first subset of medical scans is determined by identifying medical scans with abnormality probabilities greater than a second probability value of an updated triage probability threshold. An updated second subset of the plurality of medical scans is determined by identifying medical scans with a abnormality probabilities less than the second probability value. The updated first subset of medical scans is transmitted to client devices.

Abstract: An electronic device includes a processor configured to generate a position movement prediction field indicating prediction of a potential positional change of a branch path by a patient's biological activity for one or more branch paths based on a blood vessel image of a reference frame, correct guidewire information extracted from a blood vessel image of a target frame with respect to a catheter position of the reference frame, and select a branch path to dispose the guidewire information, among one or more branch paths of a blood vessel region based on the position movement prediction field and the corrected guidewire information; and a display configured to visualize the guidewire information on the selected branch path.

Abstract: Embodiments of the present application provide a method, an apparatus, a computer device for convolution operation and a computer readable storage medium. The method includes: obtaining input data of a network layer in a convolutional neural work; extracting each time a plurality of data points from the input data according to a preset step size; mapping the plurality of data points extracted each time to the same position at different depth levels of three-dimensional data to obtain rearranged data; and performing convolution operation on the rearranged data with a convolution kernel of a preset size to obtain a convolution result. Through the present solution, the operation efficiency of the convolutional neural network can be improved.

Abstract: Embodiments described herein provide a platform, device and process for digital pathology that enable multi-level annotation and visualization of histopathologic slides using a modular arrangement of deep convolutional neural networks (CNNs). The CNNs can be trained using pathology images (e.g., in some cases increasing the base of data by breaking larger fields of view into smaller ones) to learn features consistent with certain pathologies. The platform can use the CNNs to visually annotate pathology slides at an interface tool of a display device. The platform can automate the process of selection, as well as provide an opportunity for the pathologist to see a depiction of predicted results. The platform can use the CNNs to identify regions of interest on pathology slides. The interface tool can enable a predicted region of interest (ROI) type to be visually presented on a surface map showing the basis of the prediction.

Abstract: A non-transitory computer-readable storage medium storing a set of instructions that are executable by a processor, the set of instructions, when executed by the processor, causing the processor to perform operations comprising receiving an imaging study including a medical image of a patient; extracting image context from the imaging study; generating a search query based on the image context; sending the search query to a data source; receiving results of the query from a data source; and providing the results to a user.

Abstract: A device comprising a circuitry configured to obtain a sequence of digital images from an image sensor; select a region of interest within a digital image of the sequence of digital images; perform motion compensation on the region of interest to obtain a motion compensated region of interest based on motion information obtained from the sequence of digital images and a predefined accumulated time interval; define a mask pattern based on the compensated region of interest; apply the mask pattern to an electronic light valve.

Abstract: A methods and apparatuses for image processing are provided. The method may include acquiring initial image data from an image sensor. The method may further include deconstructing the initial image data in a plurality of image pyramid layers. The method may further include collapsing a first image pyramid layer with a second image pyramid layer in order to create an intermediate layer. The method may further include collapsing the intermediate layer with the subsequent image pyramid layer to create a new intermediate layer and generating the final image based on the new intermediate layer and a last image pyramid layer. A tone-mapping operator is applied to at least one of the intermediate layers.

Abstract: An image interpretation support apparatus includes: an acquisition unit that acquires a plurality of projection images obtained by tomosynthesis imaging in which a radiation is irradiated to a breast from different irradiation angles by a radiation source and a projection image is captured at each irradiation angle by a radiation detector; a first generation unit that generates a plurality of tomographic images on each of a plurality of tomographic planes of the breast from the plurality of projection images; a second generation unit that generates a synthetic two-dimensional image from a plurality of images among the plurality of projection images and the plurality of tomographic images; a detection unit that detects an object of interest candidate region estimated to include an object of interest from the synthetic two-dimensional image; and a determination unit that determines whether or not the object of interest is included in the object of interest candidate region on the basis of the plurality of tomog

Abstract: A method for providing corrected x-ray images of a recording object and a correspondingly configured x-ray system are provided. In the method, a first x-ray image recorded prior to introducing a contrast agent and a second x-ray image of the recording object recorded after introducing the contrast agent are provided. A ring correction for eliminating ring artifacts is applied to the first x-ray image and the second x-ray image in each case. In order to provide corrected x-ray images with an improved image quality, provision is made with the ring correction of the first x-ray image for a ring image, which contains artifact data extracted from the first x-ray image, to be obtained and stored and for the ring correction of the second x-ray image for the ring image obtained with the ring correction of the first x-ray image to be used.

Abstract: An image processing apparatus includes an acquisition unit configured to acquire image data, a calculation unit configured to calculate, for each unit region of the image data, a confidence that the unit region is an extraction subject, the confidence as the extraction subject being calculated for each unit region of the image data by inputting the image data into a trained model of a neural network that has been trained using images of an existing extraction subject or a region of interest as training data, a modification unit configured to modify a reference value of the confidence, which is used to extract a region of interest, an extraction unit configured to extract the region of interest on the basis of the calculated confidence of each unit region and the modified reference value, and a display unit configured to display the extracted region of interest.

Abstract: Systems, methods, and devices for treating chronic pains effectively are disclosed. The system is based on the use of randomly generated non-pulsed waveform between a frequency of 5 Hz to 2 KHz. The waveforms generated have characteristics which are pre-defined or based on the input and feedback provided by the patient and/or by the clinician, at the same time conforming to certain safety rules and precautions ensuring patient safety. This disclosure also describes a novel approach of implementing a secure memory stick which can be used to exchange data securely between one device and another device where device could be the device mentioned earlier, an off-line server, or a PC.

Abstract: A route selection assistance system enabling easy selection of a route of a living body lumen for delivering a medical instrument to a site within a living body via the living body lumen, a recording medium on which a route selection assistance program is recorded, and a route selection assistance method are disclosed. The route selection assistance system includes: a receiving section configured to receive an input of site information specifying a target site; an image obtaining section configured to obtain image information on a living body of a target patient; a route extracting section configured to extract a plurality of routes of a living body lumen; a ranking assigning section configured to assign rankings to the plurality of routes according to ease of delivery of the medical instrument and patient scores determined according to magnitude of a burden imposed on the target patient.

Abstract: A non-destructive inspection apparatus is provided. The non-destructive inspection apparatus includes at least one memory configured to store commands for performing predetermined operations, and at least one processor operatively coupled to the at least one memory and configured to execute the commands. The at least one processor is configured to obtain information on a transmission amount of an X-ray by emitting the X-ray to a part of a bicycle, generate a gray scale image based on the information on the transmission, measure an amount of change in a gray value from one end to the other end of the part of the bicycle represented in the gray scale image along an extending direction of the part, and detect an area in which the amount of change in the gray value is equal to or greater than a threshold, as an abnormal area.

Abstract: Radar, lidar, and other active 3D imaging techniques require large, heavy sensors that consume lots of power. Passive 3D imaging techniques based on feature matching are computationally expensive and limited by the quality of the feature matching. Fortunately, there is a robust, computationally inexpensive way to generate 3D images from full-motion video acquired from a platform that moves relative to the scene. The full-motion video frames are registered to each other and mapped to the scene coordinates using data about the trajectory of the platform with respect to the scene. The time derivative of the registered frames equals the product of the height map of the scene, the projected angular velocity of the platform, and the spatial gradient of the registered frames. This relationship can be solved in (near) real time to produce the height map of the scene from the full-motion video and the trajectory.

Abstract: Some embodiments of the present disclosure provide a pneumothorax detection method performed by a computing device. The method may comprise obtaining predicted pneumothorax information, predicted tube information, and a predicted spinal baseline with respect to an input image from a trained pneumothorax prediction model; determining at least one pneumothorax representative position for the predicted pneumothorax information and at least one tube representative position for the predicted tube information, in a prediction image in which the predicted pneumothorax information and the predicted tube information are displayed; dividing the prediction image into a first region and a second region by the predicted spinal baseline; and determining a region in which the at least one pneumothorax representative position and the at least one tube representative position exist among the first region and the second region.

Abstract: A system and method for image processing are provided. A pre-processed image may be obtained. The pre-processed image may be decomposed into a low-frequency image and a high-frequency image. At least one grayscale transformation range may be determined based on the low-frequency image. At least one grayscale transformation parameter may be determined based on the at least one grayscale transformation range. The low-frequency image may be transformed based on the at least one grayscale transformation parameter to obtain a transformed low-frequency image. A transformed image may be generated by reconstructing the transformed low-frequency image and the high-frequency image.

Abstract: Systems and techniques are disclosed for improvement of machine learning systems based on enhanced training data. An example method includes providing a visual concurrent display of a set of images of features, the features requiring classification by a reviewing user. The user interface is provided to enable the reviewing user to assign classifications to the images, the user interface being configured to create, read, update, and/or delete classifications. The user interface is responsive to the user, with the user response indicating at least two images with a single classification. The user interface is updated to represent the single classification.

Abstract: An apparatus for feature recognition of two-dimensional prints is illustrated. The apparatus comprise a processor and a memory communicatively connected to the processor. The memory contains instructions configuring the processor to receive a two-dimensional print of a part for manufacture, scale two-dimensional print so that the two-dimensional print is within a predetermined area, identify a curve feature of the two-dimensional print as a function of scaling of the two-dimensional print, wherein the curve feature comprises a plurality of line segments, and classify a line type of the curve feature using line observations as a function of the curve feature identification.

Abstract: Techniques for training machine learning models for improved accuracy at classifying medical imaging data sets by trimming ambiguous samples from training data sets are described herein. In some embodiments, a machine learning model is trained using a data set, where a subset of the data set comprises data with a conflict between a first label based on an expert opinion and a second label based on a ground truth based on a medical examination. During some epochs of training the machine learning model, loss values for each data sample in the epoch are compared against a loss threshold, with data samples with corresponding loss values that exceed the loss threshold that also belong to the subclass trimmed from the data set for subsequent epochs of training. The loss threshold for the next epoch is then updated based on loss values of the trimmed data set.

Abstract: An X-ray image processing method, including obtaining a first X-ray image of an object including a plurality of materials including a first material and a second material; obtaining a first partial image generated by imaging the first material and a second partial image generated by imaging the first material overlapping the second material from the first X-ray image; obtaining first information related to a stereoscopic structure of the first material, based on the first partial image included in the first X-ray image; and obtaining second information about the second material based on the first information and the second partial image.

Abstract: A method for determining a view angle of a thermal image from a user and generating a suggestion to enable the user for adaptive positioning of a subject for capturing the thermal image is provided. The method includes (i) receiving a thermal image of a body of a subject, (ii) automatically determining a view angle of the thermal image from a user using a view angle estimator, (iii) determining an angular adjustment to be made to a view position of the thermal imaging camera or a position of the subject by comparing the determined view angle with a required view angle as per thermal imaging protocol when the thermal image does not meet the required view angle and (iv) generating instructions to the user for adjusting the view position of the thermal imaging camera for capturing a new thermal image at the required view angle as per thermal imaging protocol.

Abstract: Methods and systems and computer readable media are provided for processing digitized radiology images of a patient's tissue to perform a tissue density assessment. A set of radiology images is analyzed with a tissue density image classifier, wherein the tissue density classifier includes a plurality of classifiers. A features vector is generated, for each processed image in the set of radiology images, based on the processing. The features vector is provided, for each processed image in the set of radiology images, to a tissue density classifier to obtain an output. A final tissue density score is assigned based on the output of the tissue density classifier.

Abstract: Apparatus for providing muscular biofeedback, comprising: facewear supporting biosensors arranged such that, in use, the biosensors are situated for detecting activity of a predetermined set of facial muscles; a feedback unit for providing biofeedback to a wearer of the facewear; and a control unit coupled to the biosensors and the feedback unit and arranged to identify patterns in the signals from the biosensors characteristic of one or more predefined imbalances in muscle activity between the left and right sides of the face; wherein the control unit is configured to, on identifying a pattern in the signals from biosensors characteristic of a first predefined imbalance in muscle activity between the left and right sides of the face, cause the feedback unit to provide biofeedback in correspondence with the imbalance.

Abstract: Methods, apparatuses, systems, storage media, and computing devices for updating a measurement are disclosed. One of the methods includes: detecting that an application device initiates a measurement update, wherein the measurement update includes at least one of: an object update that updates a measurement object, and a policy update that updates a policy; and performing measurement update processing upon verifying that the measurement update satisfies a predetermined condition, wherein the measurement update processing includes performing an update process on at least one of content included in an execution of a measurement process, and wherein the measurement process includes calculating a measurement object using a predetermined algorithm, comparing a calculation result with a pre-stored verification reference value, and determining that an integrity of the measurement object is not corrupted if a comparison result is consistent.

Abstract: Medical imaging methods for processing a three-dimensional (3D) image data set with two-dimensional X-ray images from an X-ray machine using a target function. Methods can include providing a 3D image data set of at least one examination zone in which anatomical structures are present, segmenting the image data set to provide a 3D vascular structure model and a 3D bone structure model, recording a first two-dimensional (2D) X-ray image containing at least a portion of the vascular structure and at least a portion of the bone structure, recording a second 2D X-ray image of the examination zone at a different contrast agent concentration, and subtracting the first and second 2D X-ray images to generate a subtraction image. An optimum projective geometry may then be determined using a three-part target function based on the 3D image data and the 2D X-ray images.

Abstract: A method and system is provided for recording a health care event, optimizing medical procedures and calculating reimbursement. The method includes: acquiring metadata comprising a patient identifier, a practitioner identifier, a health care site identifier, an entry time and a medical reason for the health care event; receiving a reimbursement request; generating a procedures list based on the medical reason; selecting a procedure; generating a list of required data types and a list of required quality data for the procedure; acquiring raw data comprising the procedure, a medical device identifier, an entry time and one or more quality data from the medical device for the procedure; and calculating a reimbursement for the health care event based on the procedure, the medical device identifier, the required quality data and the quality data from the medical device. The method implements iterative learning using the collected data to determine optimal health care procedures.