Abstract: Mechanisms are provided to implement an automated echocardiograph measurement extraction system. The automated echocardiograph measurement extraction system receives medical imaging data comprising one or more medical images and inputs the one or more medical images into a deep learning network. The deep learning network automatically processes the one or more medical images to generate an extracted echocardiograph measurement vector output comprising one or more values for echocardiograph measurements extracted from the one or more medical images. The deep learning network outputs the extracted echocardiograph measurement vector output to a medical image viewer.

Abstract: Mechanisms are provided to implement an automated echocardiograph measurement extraction system. The automated echocardiograph measurement extraction system receives medical imaging data comprising one or more medical images and inputs the one or more medical images into a deep learning network. The deep learning network automatically processes the one or more medical images to generate an extracted echocardiograph measurement vector output comprising one or more values for echocardiograph measurements extracted from the one or more medical images. The deep learning network outputs the extracted echocardiograph measurement vector output to a medical image viewer.

Abstract: Systems and methods for developing a disease detection model. One method includes training the model using an image study and an associated disease label mined from a radiology report. The image study including a sequence of a plurality of two-dimensional slices of a three-dimensional image volume, and the model including a convolutional neural network layer and a convolutional long short-term memory layer.

Abstract: Systems and methods for developing a disease detection model. One method includes training the model using an image study and an associated disease label mined from a radiology report. The image study including a sequence of a plurality of two-dimensional slices of a three-dimensional image volume, and the model including a convolutional neural network layer and a convolutional long short-term memory layer. Training the model includes individually extracting a set of features from each of the plurality of two-dimensional slices using the convolutional neural network layer, sequentially processing the features extracted by the convolutional neural network layer for each of the plurality of two-dimensional slices using the convolutional long short-term memory layer, processing output from the convolutional long short-term memory layer for each of the plurality of two-dimensional slices to generate a probability of the disease, and updating the model based on comparing the probability to the label.

Abstract: An intervention system employs an interventional device (10), and a sensor wire (20) manually translatable within a lumen (11). The intervention system further employs a reconstruction controller (44) for reconstructing a shape of the interventional tool (10) responsive to a sensing of a manual translation of the sensor wire (20) within the lumen (11) (e.g., a EM sensor being attached to/embedded within a guide wire), and for determining a reconstruction accuracy of a translation velocity of the sensor wire (20) within the lumen (11) to thereby facilitate an accurate reconstruction of the shape of the interventional tool (10). The reconstruction accuracy may be determined by the reconstruction controller (44) as an acceptable translation velocity being less than an acceptable threshold, an unacceptable translation velocity being greater than an unacceptable threshold, and/or a borderline translation velocity being greater than the acceptable threshold and less than the unacceptable threshold.

Abstract: The invention relates to a determination apparatus for determining the pose and shape of an introduction element like a catheter within a living being, wherein the introduction element is adapted to be used by a therapy apparatus for introducing a energy source close to a target object to be treated. A position determination element like guidewire with an electromagnetic tracking element is introduced into the introduction element such that it is arranged at different locations within the introduction element, wherein the positions of the position determination element within the introduction element are determined. The determined positions are then acquired depending on the determined positions for determining the pose and shape of the introduction element within the living being. This can lead to a determination procedure with reduced user interaction, thereby simplifying the determination procedure for the user.

Abstract: Mechanisms are provided for evaluating the normality of a medical condition of a patient based on a medical image. A medical image segmentation receives a medical image and segments the medical image to generate an extracted contour representing an anatomical feature. The medical image segmentation engine correlates the extracted contour with a template shape corresponding to the anatomical feature. A feature extraction engine extracts one or more features from a region of the medical image corresponding to the template shape. A normality classification engine performs a normality classification operation on the extracted one or more features to generate a normality score for the medical image and outputs the normality score to a computing device.

Abstract: Medical imaging study summary engine mechanisms are provided. The mechanisms receive a medical imaging study having data representing a plurality of medical images of a patient. The mechanisms generate a temporal trajectory data structure of at least a subset of the medical images in the plurality of medical images, wherein the temporal trajectory data structure specifies topological changes in temporally subsequent medical images in the plurality of medical images. The mechanisms select medical image data corresponding to selected medical images from the medical imaging study data structure based on the temporal trajectory data structure. The mechanisms output the selected medical image data via a medical imaging study user interface.

Abstract: A method includes generating a hyperthermia heat plan for tissue of interest, generating a hyperthermia adapted radiation therapy plan for the tissue of interest, controlling a heat source (126) to deliver heat to the tissue of interest according to the hyperthermia heat plan, and controlling a radiation source of a radiation therapy system (100) to deliver radiation to the tissue of interest according to the hyperthermia adapted radiation therapy plan. A system includes a radiation treatment planner (124) configured to generate a hyperthermia adapted radiation therapy plan for the tissue of interest, a radiation therapy system (100) configured to deliver radiation in accordance with the hyperthermia adapted radiation therapy plan, and a hyperthermia heat delivery system (126) configured to deliver heat in accordance with a hyperthermia plan.

Abstract: A registration apparatus registers an elongated introduction element (18), like a catheter or a needle, during an interventional procedure, for instance, a brachytherapy or a biopsy. A guide element (13) includes at least one opening which guides the introduction element when being introduced into a living being (2) through the opening. A temperature profile generation element (14) associated with the opening generates a characteristic temperature profile at the opening for being transferred to the introduction element when being present in the opening. A temperature determination unit (15) determines a temperature along the introduction element, and a registration unit (16) registers the introduction element. The registering includes identifying the transferred characteristic temperature profile on the introduction element based on the determined temperature. This allows the registration unit to register the introduction element during the introduction process in a simple and efficient manner.

Abstract: A determination apparatus determines the pose and shape of an introduction element like a catheter within a living being, wherein the introduction element is adapted to be used by a brachytherapy apparatus for introducing a radiation source close to a target object to be treated. A position determination element like a guidewire with an electromagnetic tracking element is introduced into the introduction element such that it is arranged at different locations within the introduction element, wherein the positions of the position determination element within the introduction element are determined. The determined positions are then acquired depending on the determined positions for determining the pose and shape of the introduction element within the living being. This can lead to a determination procedure with reduced user interaction, thereby simplifying the determination procedure for the user.

Abstract: An electromagnetic field quality assurance system employing an electromagnetic field generator (10) for emitting an electromagnetic field (12), and one or more quality assurance electromagnetic sensors (11, 21, 31, 41, 50) for sensing the emission of the electromagnetic field (12). The system further employs a quality assurance controller (74) for assessing a tracking quality of the electromagnetic field (12) derived from a monitoring of a sensed position of each quality assurance electromagnetic sensor (11, 21, 31, 41, 50) within a field-of-view of the electromagnetic field (12). The electromagnetic field generator (10), an ultrasound probe (20), an ultrasound stepper (30) and/or a patient table (40) may be equipped with the quality assurance electromagnetic sensor(s) (11, 21, 31, 41, 50).

Abstract: Mechanisms are provided to implement an automated medical image processing pipeline selection (MIPPS) system. The MIPPS system receives medical image data associated with a patient electronic medical record and analyzes the medical image data to extract evidence data comprising characteristics of one or more medical images in the medical image data indicative of a medical image processing pipeline to select for processing the one or more medical images. The evidence data is provided to a machine learning model of the MIPPS system which selects a medical image processing pipeline based on a machine learning based analysis of the evidence data. The selected medical image processing pipeline processes the medical image data to generate a results output.

Abstract: An electromagnetic (“EM”) tracking configuration system employs an EM quality assurance (“EMQA”) (30) and EM data coordination (“DC”) system (70). For the EMQA system (30), an EM sensor block (40) includes EM sensor(s) (22) positioned and oriented to represent a simulated electromagnetic tracking of interventional tool(s) inserted through electromagnetic sensor block (40) into an anatomical region. As an EM field generator (20) generates an EM field (21) encircling EM sensor(s) (22), an EMQA workstation (50) tests an EM tracking accuracy of an insertion of the interventional tool(s) through the EM sensor block (40) into the anatomical region.

Abstract: Mechanisms are provided to implement an automated medical image processing pipeline selection (MIPPS) system. The MIPPS system receives medical image data associated with a patient electronic medical record and analyzes the medical image data to extract evidence data comprising characteristics of one or more medical images in the medical image data indicative of a medical image processing pipeline to select for processing the one or more medical images. The evidence data is provided to a machine learning model of the MIPPS system which selects a medical image processing pipeline based on a machine learning based analysis of the evidence data. The selected medical image processing pipeline processes the medical image data to generate a results output.

Abstract: A method for processing medical images includes analyzing a medical image to detect a medical condition from a list of medical conditions, wherein the list of medical conditions includes aortic dissection, pulmonary embolism, and coronary stenosis. Responsive to determining the medical image includes a first medical condition, the method generates a first report that includes information on a detection of the first medical condition. The method identifies, a medical specialist based on availability and medical expertise and sends to the identified medical specialist, the medical image and the first report for a decision on the detection of the first medical condition. Responsive to receiving the decision from the medical specialist, the method sends to a second electronic device, the decision, the medical image, and the first report.

Abstract: A method for processing medical images includes analyzing a medical image to detect a medical condition from a list of medical conditions, wherein the list of medical conditions includes aortic dissection, pulmonary embolism, and coronary stenosis. Responsive to determining the medical image includes a first medical condition, the method generates a first report that includes information on a detection of the first medical condition. The method identifies, a medical specialist based on availability and medical expertise and sends to the identified medical specialist, the medical image and the first report for a decision on the detection of the first medical condition. Responsive to receiving the decision from the medical specialist, the method sends to a second electronic device, the decision, the medical image, and the first report.

Abstract: Mechanisms are provided for evaluating the normality of a medical condition of a patient based on a medical image. A medical image segmentation receives a medical image and segments the medical image to generate an extracted contour representing an anatomical feature. The medical image segmentation engine correlates the extracted contour with a template shape corresponding to the anatomical feature. A feature extraction engine extracts one or more features from a region of the medical image corresponding to the template shape. A normality classification engine performs a normality classification operation on the extracted one or more features to generate a normality score for the medical image and outputs the normality score to a computing device.

Abstract: Medical imaging study summary engine mechanisms are provided. The mechanisms receive a medical imaging study having data representing a plurality of medical images of a patient. The mechanisms generate a temporal trajectory data structure of at least a subset of the medical images in the plurality of medical images, wherein the temporal trajectory data structure specifies topological changes in temporally subsequent medical images in the plurality of medical images. The mechanisms select medical image data corresponding to selected medical images from the medical imaging study data structure based on the temporal trajectory data structure. The mechanisms output the selected medical image data via a medical imaging study user interface.

Abstract: The invention relates to an imaging apparatus (24) for imaging an introduction element (17) like a needle or a catheter for performing a brachytherapy or a biopsy. A tracking unit (3, 4) tracks the location of the introduction element within a living being (2), an imaging unit (6) like an ultrasound imaging unit generates an image showing an inner part of the living being, which includes the tracked location of the introduction element, based on the tracked location, and a display (7) displays the image. During the brachytherapy or biopsy the display can always show the introduction element, without requiring a manual control. For instance, it is not necessary that a physician manually controls the position and image plane of the imaging unit. This allows for an accurate and fast insertion of the introduction element into the living being such that a target region is reliably reached.