Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a medical imaging story board creation engine. The medical imaging story board creation engine executing in the data processing system receives a patient data structure comprising a medical imaging study comprising a plurality of electronic medical images. The medical imaging story board creation engine analyzes the patient data structure to determine a modality of the medical imaging study. The medical imaging story board creation engine determines, based on the determined modality of the medical imaging study, for each electronic image in the medical imaging study, at least one of an image mode or viewpoint.

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: According to one or more embodiments, a method, a computer program product, and a computer system for detecting and characterizing aortic pathologies are provided. The method may include receiving, by a computer, one or more tomograph scan images corresponding to a patient's aorta. The one or more received tomograph scan images may be analyzed by the computer for one or more image features associated with one or more aortic pathologies, such as aortic dissection or an aortic aneurysm. One or more image features associated with the one or more aortic pathologies may be identified in the one or more analyzed tomograph scan images, which may allow the determination of an aortic pathology associated with the patient's aorta based on the identification of the image features. A portion of the aorta and one or more branch arteries corresponding to the determined aortic pathology may then be identified.

Abstract: The invention relates to an assisting apparatus for assisting in performing brachytherapy. The position of an introduction element (17) like a catheter is tracked particularly by using electromagnetic tracking, while a group of seeds is introduced into a living object (2). This provides a rough knowledge about the position of the seeds within the object. An ultrasound image showing the group is generated depending on the tracked position of the introduction element and, thus, depending on the rough knowledge about the position of the seeds, in order to optimize the ultrasound visualization with respect to showing the introduced seeds. Based on this optimized ultrasound visualization the position of a seed of the group is determined, thereby allowing for an improved determination of seed positions and correspondingly for an improved brachytherapy performed based on the determined positions.

Abstract: A method for determining a surrogate respiratory signal for four-dimensional computed tomography using ultrasound data includes acquiring computed tomography data with a computed tomography imaging system (402), acquiring ultrasound data with an ultrasound probe of an ultrasound imaging system (404) concurrently with acquiring the computed tomography data during one or more respiratory cycles, wherein the ultrasound probe is aligned to acquire an image of a diaphragm of a subject, synchronizing the acquired computed tomography data and the acquired ultrasound data, and determining a surrogate respiratory signal from the acquired ultrasound data.

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: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a medical imaging story board creation engine. The medical imaging story board creation engine executing in the data processing system receives a patient data structure comprising a medical imaging study comprising a plurality of electronic medical images. The medical imaging story board creation engine analyzes the patient data structure to determine a modality of the medical imaging study. The medical imaging story board creation engine determines, based on the determined modality of the medical imaging study, for each electronic image in the medical imaging study, at least one of an image mode or viewpoint.

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 system for selecting a calibration includes a data structure (138) including non-transitory computer readable storage media having a plurality of calibration entries stored therein and indexed to position and/or orientation criteria for a field generator. The field generator is configured for placement in an environment for sensor tracking. A calibration selection module (140) is configured to determine a position and/or orientation of the field generator and, based on the position and/or orientation, determine, using the data structure, corresponding calibration information stored in the data structure. The calibration information is optimized based upon the position and/or orientation of the field generator.

Abstract: The invention relates to a system (10) for providing an object (2) in a body (1), a processor (18) arranged to be used in the system (10) for providing an object (2) in a body (1), an instrument (12) for providing an object (2) into a body (1), a method for detecting a providing of an object (2) in a body (1) and a software product for detecting a providing of an object (2) in a body (1). In order to allow for a providing of an object (2) in a body (1) and a detecting hereof while avoiding the drawbacks on the known approaches, e.g. giving an opportunity for reliable localization in ultrasound images used for real-time monitoring of a medical procedure with reduced error proneness to electromagnetic interference, the invention utilizes the finding that the characteristics of a reception or transmission of an ultrasound transducer (24, 26) are influenced by the surrounding environment of the ultrasound transducer (24, 26).

Abstract: The generation of a pattern and for an adaptation to the specific geometry requires a lot of manual work. The workflow for the clinician is simplified during treatment planning. A treatment planning system is configured for determining a set of catheter or needle insertion positions to be used during treatment. The treatment planning system includes an image providing module for providing a medical image from which at least one treatment target structure can be derived. Further the treatment planning system includes a pattern providing module for providing one or a set of standard patterns for catheter or needle insertion including a plurality of catheter or needle insertion positions. The catheter or needle positions relate to treatment positions in the at least one treatment target structure.

Abstract: A brachytherapy seed localization system for localizing radioactive seeds within a diseased tissue, the brachytherapy seed localization system employs a tool tracking machine (50) for generating a tracked seed distribution map (51) of delivered locations of the radioactive seeds within the diseased tissue, and a tissue imaging machine (60) for generating a seed distribution image (61) of projected locations of the radioactive seeds within the diseased tissue including at least one false projected location. The brachytherapy seed localization system further employs a brachytherapy seed localizer (70) for generating a composite seed distribution map (71) of estimated locations of the radioactive seeds within the diseased tissue derived from a combination of the tracked seed distribution map (51) and the seed distribution image (61) excluding any false projected location(s) within the seed distribution image (61).

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: According to one or more embodiments, a method, a computer program product, and a computer system for detecting and characterizing aortic pathologies are provided. The method may include receiving, by a computer, one or more tomograph scan images corresponding to a patient's aorta. The one or more received tomograph scan images may be analyzed by the computer for one or more image features associated with one or more aortic pathologies, such as aortic dissection or an aortic aneurysm. One or more image features associated with the one or more aortic pathologies may be identified in the one or more analyzed tomograph scan images, which may allow the determination of an aortic pathology associated with the patient's aorta based on the identification of the image features. A portion of the aorta and one or more branch arteries corresponding to the determined aortic pathology may then be identified.

Abstract: A system (100) includes an imaging system (130), and a therapy control device (122). The imaging system (130) generates temperature maps (140) and strain maps (142) of localized tissues of a patient. The therapy control device (122) includes one or more computer processors configured to detect at least one failure mode (300, 302, 304, 400) of generated mild hyperthermia in the localized tissues of the patient according to at least one of the temperature maps, the strain maps, or a signal indicative of detected inertial cavitation. In some embodiments, the therapy control device either halts therapy or issues a warning.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a medical imaging story board creation engine. The medical imaging story board creation engine executing in the data processing system receives a patient data structure comprising a medical imaging study comprising a plurality of electronic medical images. The medical imaging story board creation engine analyzes the patient data structure to determine a modality of the medical imaging study. The medical imaging story board creation engine determines, based on the determined modality of the medical imaging study, for each electronic image in the medical imaging study, at least one of an image mode or viewpoint.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement an intelligent medical image viewing engine. The intelligent medical image viewing engine receives a medical imaging study data structure comprising a plurality of electronic medical images from a medical image database. An image processing component executing within the intelligent medical image viewing engine analyzes the medical imaging study data structure to identify, for each electronic medical image in the plurality of electronic medical images, a corresponding set of image attributes.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a medical imaging story board creation engine. The medical imaging story board creation engine executing in the data processing system receives a patient data structure comprising a medical imaging study comprising a plurality of electronic medical images. The medical imaging story board creation engine analyzes the patient data structure to determine a modality of the medical imaging study. The medical imaging story board creation engine determines, based on the determined modality of the medical imaging study, for each electronic image in the medical imaging study, at least one of an image mode or viewpoint.

Abstract: According to one or more embodiments, a method, a computer program product, and a computer system for detecting and characterizing aortic pathologies are provided. The method may include receiving, by a computer, one or more tomograph scan images corresponding to a patient's aorta. The one or more received tomograph scan images may be analyzed by the computer for one or more image features associated with one or more aortic pathologies, such as aortic dissection or an aortic aneurysm. One or more image features associated with the one or more aortic pathologies may be identified in the one or more analyzed tomograph scan images, which may allow the determination of an aortic pathology associated with the patient's aorta based on the identification of the image features. A portion of the aorta and one or more branch arteries corresponding to the determined aortic pathology may then be identified.

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.