Methods are provided for dynamically visualizing information in image data of an object of interest of a patient, which include an offline phase and an online phase. In the offline phase, first image data of the object of interest acquired with a contrast agent is obtained with an interventional device is present in the first image data. The first image data is used to generate a plurality of roadmaps of the object of interest. A plurality of reference locations of the device in the first image data is determined, wherein the plurality of reference locations correspond to the plurality of roadmaps. In the online phase, live image data of the object of interest acquired without a contrast agent is obtained with the device present in the live image data, and a roadmap is selected from the plurality of roadmaps. A location of the device in the live image data is determined. The reference location of the device corresponding to the selected roadmap and the location of the device in the live image data is used to transform the selected roadmap to generate a dynamic roadmap of the object of interest. A visual representation of the dynamic roadmap is overlaid on the live image data for display. In embodiments, the first image data of the offline phase covers different of phases of the cardiac cycle of the patient, and the plurality of roadmaps generated in the offline phase covers the different phases of the patient's cardiac cycle. Related systems and program storage devices are also described and claimed.

The present invention relates to the field of ultrasounds and imagining of the coronary blood flow of the heart. Patients with coronary microvascular dysfunction (CMD) have poor prognostic with significantly higher rates of cardiovascular events, including hospitalization for heart failure, sudden cardiac death, and myocardial infarction (MI). Despite the urgent clinical need, there are no non-ionizing and non-invasive techniques available in clinic to directly visualize the coronary microvasculature and assess the local coronary microvascular system. Flow imaging remains a difficult task to perform in the heart because of the fast movements of this organ. In order to overcome the limitations of actual imaging methods for the coronary blood flow, the inventors proposed an ultrasound ultrafast imaging method that automatically detect the time periods in which the myocardium velocity is low and estimate the coronary flow velocity and the tissue velocity from the same data acquisition.

An ultrasound imaging system may acquire short and/or undersampled radiofrequency ensembles for generating color Doppler images. The ultrasound imaging system may process the short and/or undersampled ensembles to simulate color Doppler images acquired from long radiofrequency ensembles. In some examples, the ultrasound imaging system may include a neural networks to process the ensembles. In some examples, the neural network may include two serial neural networks. In some examples, during training of the neural network, a power Doppler-based flow mask may be used on the output of the neural network. In some examples, during training of the neural network, an adversarial loss may be used on the output of the neural network.

A probe is configured with a flushing port and an evacuation port to establish a flow path to remove blood from a resected tissue. The probe comprises a balloon configured to expand and contact the resected tissue to compress filaments and improve access to the underlying blood vessels for coagulation with an energy source. An endoscope can be used to view the tissue, and the balloon may comprise a transparent material or a viewing port to allow imaging of the bleeding tissue through the balloon. The probe may have a light source to illuminate the tissue with a beam oriented at an oblique angle to the tissue surface, which can decrease interference from blood and may allow more localized coagulation of the blood vessel.

An ultrasound diagnosis apparatus including: an ultrasound probe having a transducer array; and a processor configured to: perform transmission and reception of an ultrasonic beam from the transducer array toward a subject, into which contrast media including microbubbles is introduced; image a reception signal output from the transducer array to generate an ultrasound image of the subject; acquire trajectories of the microbubbles in a one cross section of the subject by tracking movement of the microbubbles based on the ultrasound image corresponding to the one cross section of the subject; detect, as a feature point, a trajectory, in which a distance between a start point and an end point in a prescribed time range is less than a prescribed value, among the trajectories of the microbubbles; and display the ultrasound image, the acquired trajectories and the detected feature point on the display unit.

According to one embodiment, an analysis apparatus includes processing circuitry. The processing circuitry configured to generate a harmonic signal and a fundamental wave signal based on a reception signal that is collected by an ultrasound probe, the harmonic signal corresponding to a harmonic component of a reflected wave of a ultrasound generated in the subject, the fundamental wave signal corresponding to a fundamental wave component of the reflected wave, calculate a first index value indicating tissue properties of the subject based on the harmonic signal, and calculate a second index value indicating the tissue properties based on the fundamental wave signal, and display an analysis result based on the first index value and the second index value.

Methods and system for characterizing ligament properties using elastography are disclosed. An ultrasound system capable of performing shear wave elasticity imaging and/or supersonic shear imaging may retrieve one or more images from a proposed surgical site. The one or more images may be provided to a surgical planning system that identifies one or more properties of ligaments proximate to the surgical site. Musculoskeletal simulations may be performed using the identified properties to preoperatively identify a surgical plan. Preoperative identification of a surgical plan may enable a surgeon to select from more fine-tuning options for a joint replacement than conventional systems.

A non-transitory computer-readable medium (CRM) storing computer program code executed by a computer processor that executes a process, an information processing apparatus, and a model generation method that outputs complication information for a medical treatment. The process includes acquiring a medical image obtained by imaging a lumen organ of a patient before treatment, inputting the acquired medical image into a trained model so as to output complication information on a complication that is likely to occur after the treatment when the medical image is received, and outputting the complication information. Preferably, complication information including a type of the complication that is likely to occur and a probability value indicating an occurrence probability of the complication of the type is output.

A non-transitory computer-readable medium (CRM) storing computer program code executed by a computer processor that executes a process, an information processing apparatus, and model generation method that generates an image of a lumen organ. The process includes acquiring a first image obtained by imaging a lumen organ of a patient based on an ultrasound signal of a first frequency; and generating a second image by inputting the acquired first image into a model, the model being learned to generate, when the first image is input, the second image in which the lumen organ is imaged based on an ultrasound signal of a second frequency. Preferably, the second image, in which a part of an image region of the first image is converted into the second frequency, is generated using the model, and a synthesis image is generated in which the second image is superimposed to the first image.

A computer is caused to perform processing of: acquiring a plurality of medical images generated based on signals detected by a catheter inserted into a lumen organ while the catheter is moving a sensor along a longitudinal direction of the lumen organ, the lumen organ including a main trunk, a side branch branched from the main trunk, and a bifurcated portion of the main trunk and the side branch; and recognizing a main trunk cross-section, a side branch cross-section, and a bifurcated portion cross-section by inputting the acquired medical images into a learning model configured to recognize the main trunk cross-section, the side branch cross-section, and the bifurcated portion cross-section.