Abstract: A computer-implemented method of providing a neural network for predicting a three-dimensional shape of an interventional device disposed within a vascular region, includes: training (S140) a neural network (140) to predict, from received X-ray image data (120) and received volumetric image data (110), a three-dimensional shape of the interventional device constrained by the vascular region (150). The training includes constraining the adjusting of parameters of the neural network such that the three-dimensional shape of the interventional device predicted by the neural network (150) fits within the three-dimensional shape of the vascular region represented by the received volumetric image data (110).

Abstract: The present disclosure describes systems configured to recognize indicators of a medical condition within a diagnostic image and predict the progression of the medical condition based on the recognized indicators. The systems can include neural networks trained to extract disease features from diagnostic images and neural networks configured to model the progression of such features at future time points selectable by a user. Modeling the progression may involve factoring in various treatment options and patient-specific information. The predicted outcomes can be displayed on a user interface customized to specific representations of the predicted outcomes generated by one or more of the underlying neural networks. Representations of the predicted outcomes include synthesized future images, probabilities of clinical outcomes, and/or descriptors of disease features that may be likely to develop over time.

Abstract: A method predicts an interpretation time for a medical image examination of a subject comprising one or more medical images. A plurality of data inputs is obtained, where the data inputs are associated with the medical image examination or the subject of the medical image examination, and the data points represent parameters affecting the interpretation time. The plurality of data inputs are input to a trained artificial intelligence algorithm, wherein the algorithm automatically provides a predicted interpretation time based on said plurality of data inputs. The predicted interpretation time is output to a clinical management system. A clinical management system incorporating the aforementioned method and a computer program product encoded with the aforementioned method are also provided.

Abstract: A method (100) for providing information to a sonograph user, including providing (130) a sonogram atlas comprising one or more sonogram preferences of each of a number of different radiograph interpreters, the sonogram preferences comprising at least one of a preferred view and a measurement for each of a number of sonogram types, receiving (140) a request for information from the sonogram atlas about a sonogram being obtained by a sonograph user from a patient, wherein the request comprises one or more of demographic information about the patient, a type of exam, a reason for imaging, an identification of which of the number of different radiograph interpreters ordered the sonogram, and an identification of which of the one or more sonogram types was ordered by the identified radiograph interpreter(s) retrieving (150) the one or more sonogram preferences of the identified radiograph interpreter(s).

Abstract: A controller (150) for interventional medical devices includes a memory (151) and a processor (152). The memory (151) stores instructions that the processor (152) executes. When the instructions are executed, the instructions cause the controller (150) to obtain at least one location of a distal end of the interventional medical device (101), identify motion at a proximal end of an interventional medical device (101), apply a first trained artificial intelligence to the motion at the proximal end of the interventional medical device (101) and to the at least one location of the distal end of the interventional medical device (101), and predict motion along the interventional medical device (101) towards a distal end of the interventional medical device (101) during the interventional medical procedure. The controller (150) also obtains images of the distal end of the interventional medical device (101) from a medical imaging system (120) to determine when the actual motion deviates from the predicted motion.

Abstract: A method and system (100) for augmented interpretation of shear wave elastography between first and second imaging modalities comprises performing an elastography measurement via a second imaging modality (20), different from a first imaging modality (10), to obtain at least one second imaging modality elastography value (32, 60) of a region of interest (33). At least one corresponding first imaging modality elastography value (36, 38, 62) is predicted based on the obtained second imaging modality elastography value. A graphical user interface or smart report dashboard (50) is generated that shows (i) a fibrosis level (521) of the region of interest, wherein the fibrosis level is determined as a function of (i)(a) the at least one second imaging modality elastography value (32) and/or (i)(b) the predicted at least one corresponding first imaging modality elastography value (36, 38).

Abstract: An ultrasound imaging system includes a processor circuit that stores, in a memory in communication with the processor circuit, a target parameter representative of a target anatomical scan window. The processor circuit receives a first ultrasound image acquired by a first ultrasound probe with a first anatomical scan window during a first acquisition period. The processor circuit determines a first parameter representative of the first anatomical scan window. The processor circuit retrieves the target parameter from the memory. The processor circuit compares the target parameter and the first parameter. The processor circuit outputs a visual representation of the comparison to a display in communication with the processor circuit.

Abstract: A controller (150) for selecting images for presentation includes a memory (151), a processor (152) and a receiver. The memory (151) stores instructions. The processor (152) executes the instructions. The receiver receives a stream of a plurality of images. When executed by the processor (152), the instructions cause the controller (150) to apply trained artificial intelligence to the plurality of images and generate, based on the trained artificial intelligence, a 1-dimensional stream of a plurality of values corresponding respectively to the plurality of images. Each of the plurality of values of the 1-dimensional stream indicates a significance of a corresponding image of the plurality of images according to the trained artificial intelligence. The instructions also cause the controller (150) to output the plurality of values of the 1-dimensional stream for display with the plurality of images.

Abstract: System (SYS) and delated methods for supporting an imaging operation of an imaging apparatus (IA) capable of assuming different imaging geometries. The system comprises an input interface (IN) for receiving a current image acquired by the imaging apparatus (IA) of a current region of interest (ROI_1) at a current imaging geometry (p). A pre-trained machine learning component (MLC) computes output data that represents an imaging geometry change (?p) for a next imaging geometry (p?) in relation to a next region of interest (ROI_2). An output interface (OUT) outputs a specification of the imaging geometry change.

Abstract: A lung injury monitoring system (200) configured to monitor a patient's lungs during ventilation, comprising: an ultrasound device (280) configured to obtain an ultrasound image of the patient's lungs during ventilation of the patient; a processor (220) configured to: (i) receive the obtained ultrasound image; (ii) determine a ventilation phase of the ventilator; (iii) analyze the received ultrasound image; and (iv) determine a risk score for a potential ventilator-associated lung injury (VALI); and a user interface (240) configured to display the determined risk score for the potential VALI.

Abstract: A method (100) for analyzing ultrasound image data, comprising: (i) receiving (120) ultrasound image data for a patient, comprising at least one lung-related clinical feature; (ii) selecting (130) a first clinical feature; (iii) analyzing (140), using an image processing algorithm, the ultrasound image data to extract one or more image parameters of the selected clinical feature, wherein the image processing algorithm is defined to analyze the selected clinical feature; (iv) analyzing (150), using a trained machine learning algorithm, the ultrasound image data to extract one or more learned parameters of the selected clinical feature, wherein the trained machine learning algorithm is trained to analyze the selected clinical feature; (v) combining (160) the image parameters and the learned parameters to generate a final lung-related clinical feature analysis; and (vi) providing (170) the generated final lung-related clinical feature analysis.

Abstract: A method (100) for analyzing ultrasound image data, comprising: (i) receiving (120) a temporal sequence of ultrasound image data for one or more of a plurality of different zones of one or both lungs of a patient; (ii) analyzing (130), using a first trained clinical lung feature identification algorithm, the received ultrasound image data to identify a first clinical feature in a lung of the patient, wherein identifying the first clinical feature comprises analysis of multiple frames in the temporal sequence, and wherein identifying the first clinical feature comprises identification of a location of the first clinical feature within the multiple frames; (iii) analyzing (140), using a trained clinical lung feature severity algorithm, the identified first clinical feature to characterize a severity of the identified first clinical feature; and (iv) providing (180), via a user interface, the identified first clinical feature and the characterized severity of the first clinical feature.

Abstract: An ultrasound imaging system includes an a processor circuit that stores, in a memory in communication with the processor circuit, a target parameter representative of a target anatomical scan window. The processor circuit receives a first ultrasound image acquired by a first ultrasound probe with a first anatomical scan window during a first acquisition period. The processor circuit determines a first parameter representative of the first anatomical scan window. The processor circuit retrieves the target parameter from the memory. The processor circuit compares the target parameter and the first parameter. The processor circuit outputs a visual representation of the comparison to a display in communication with the processor circuit.

Abstract: A system (SYS) and related method for facilitating collimator adjustment in X-ray imaging or a radiation therapy delivery. The system comprises an input interface (IN) for receiving input data including i) an input image and/or ii) user input data including a partial collimator setting for a collimator (COL) of an X-ray imaging apparatus (IA). A collimator setting estimator (CSE) of the system computes a complemented collimator setting for the collimator based the input data. Preferably, the system uses machine learning.

Abstract: A system and method for registering two dimensional (2D) acoustic images to a three dimensional (3D) reference image.

Abstract: A method (100) for providing information to a sonograph user, including providing (130) a sonogram atlas comprising one or more sonogram preferences of each of a number of different radiograph interpreters, the sonogram preferences comprising at least one of a preferred view and a measurement for each of a number of sonogram types, receiving (140) a request for information from the sonogram atlas about a sonogram being obtained by a sonograph user from a patient, wherein the request comprises one or more of demographic information about the patient, a type of exam, a reason for imaging, an identification of which of the number of different radiograph interpreters ordered the sonogram, and an identification of which of the one or more sonogram types was ordered by the identified radiograph interpreter(s) retrieving (150) the one or more sonogram preferences of the identified radiograph interpreter(s).

Abstract: A method (100) for analyzing ultrasound image data, comprising: (i) receiving (120) ultrasound image data for a patient, comprising at least one lung-related clinical feature; (ii) selecting (130) a first clinical feature; (iii) analyzing (140), using an image processing algorithm, the ultrasound image data to extract one or more image parameters of the selected clinical feature, wherein the image processing algorithm is defined to analyze the selected clinical feature; (iv) analyzing (150), using a trained machine learning algorithm, the ultrasound image data to extract one or more learned parameters of the selected clinical feature, wherein the trained machine learning algorithm is trained to analyze the selected clinical feature; (v) combining (160) the image parameters and the learned parameters to generate a final lung-related clinical feature analysis; and (vi) providing (170) the generated final lung-related clinical feature analysis.

Abstract: A method for processing an ultrasound frame includes obtaining an ultrasound frame of a lung and identifying a pleural line in the lung; quantifying the pleural line identified in the lung to obtain a quantification of the pleural line; comparing the quantification of the pleural line to a predetermined value; and identifying at least one of irregularity or thickening in the pleural line based on comparing the quantification of the pleural line to the predetermined value.

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

Abstract: A system (200) and method (900): access (910) a 3D reference image of a region of interest (ROI) (10) in a subject which was obtained using a first 3D imaging modality; segment (920) the ROI in the 3D reference image to define (930) a reference 3D coordinate system; acquire (940) 2D acoustic images of the ROI without absolute spatial tracking; generate (950) a standardized predicted pose for the 2D acoustic images with respect to a standardized 3D coordinate system (400) which was defined for a plurality of previously-obtained 3D images of corresponding ROIs in a plurality of other subjects which were obtained using the first 3D imaging modality; convert (960) the standardized predicted poses for the 2D acoustic images from the standardized 3D coordinate system to reference predicted poses in the reference 3D coordinate system; and use (970) the reference predicted poses for the 2D acoustic images to register the 2D acoustic images to the 3D reference image.