An information processing device, an information processing method, and a non-transitory computer-readable medium are disclosed. The information processing device includes a processor configured to: acquire transverse tomographic images of a plurality of frames obtained by imaging a blood vessel of a patient, calculate plaque burden at each position of the blood vessel along an axial direction of the blood vessel based on the transverse tomographic images of the plurality of frames, divide a longitudinal cross section of the blood vessel into a first region in which the plaque burden is equal to or greater than a threshold and a second region in which the plaque burden is less than the threshold, and determine, in each of the regions obtained by the division, regions in which both ends of a treatment area of the blood vessel with a predetermined treatment device are to be located.

An image processing apparatus includes at least one processor. The processor is configured to execute region-of-interest image generation processing of generating a region-of-interest image from a projection image, which is obtained at an irradiation position closest to a position facing a detection surface of a radiation detector, among a series of projection images obtained by irradiating a breast with radiations and imaging the breast, and shape type determination processing of determining a type of a shape of a calcification image included in the region-of-interest image generated by the region-of-interest image generation processing.

Within the field of elastography, a new approach analyzes the limiting case of shear waves established as a reverberant field. In this framework, it is assumed that a distribution of shear waves exists, oriented across all directions in 3D (e.g. 2D space+time). The simultaneous multi-frequency application of reverberant shear wave fields can be accomplished by applying an array of external sources that can be excited by multiple frequencies within a bandwidth, for example 50, 100, 150, . . . 500 Hz, all contributing to the shear wave field produced in the liver or other target organ. This enables the analysis of the dispersion of shear wave speed as it increases with frequency, indicating the viscoelastic and lossy nature of the tissue under study. Furthermore, dispersion images can be created and displayed alongside the shear wave speed images. Studies on breast and liver tissues using the multi-frequency reverberant shear wave technique, employing frequencies up to 700 Hz in breast tissue, and robust reverberant patterns of shear waves across the entire liver and kidney in obese patients are reported. Dispersion images are shown to have contrast between tissue types and with quantitative values that align with previous studies.

Systems and methods are disclosed for simulating microvascular networks from a vascular tree model to simulate tissue perfusion under various physiological conditions to guide diagnosis or treatment for cardiovascular disease. One method includes: receiving a patient-specific vascular model of a patient's anatomy, including a vascular network; receiving a patient-specific target tissue model in which a blood supply may be estimated; receiving joint prior information associated with the vascular model and the target tissue model; receiving data related to one or more perfusion characteristics of the target tissue; determining one or more associations between the vascular network of the patient-specific vascular model and one or more perfusion characteristics of the target tissue using the joint prior information; and outputting a vascular tree model that extends to perfusion regions in the target tissue, using the determined associations between the vascular network and the perfusion characteristics.

An information processing apparatus includes an acquisition unit, a determination unit, and an output unit. The acquisition unit acquires history information of image processing using a plurality of medical images. The determination unit determines, using the acquired history information, whether each of a plurality of candidate images that are candidates of a second medical image used for image processing together with a first medical image selected by an operator has already been processed. The output unit outputs a notification in accordance with the result of the determination.

Embodiments of the disclosure provide methods, apparatuses, and computer-readable media for generating image reports. In one embodiment, the method includes: obtaining an image to be analyzed; determining at least one reference image corresponding to the image to be analyzed, the at least one reference image corresponding to a respective reference image report; and generating, based on the reference image report, an image report corresponding to the image to be analyzed. In the method for generating an image report provided by the embodiments, by obtaining an image to be analyzed is obtained, at least one reference image corresponding to the image to be analyzed is determined; and an image report corresponding to the image to be analyzed is generated based on a respective reference image report corresponding to the at least one reference image. Therefore, medical staff are assisted in quickly writing an image report, thereby ensuring the quality and the efficiency of the image report, reducing labor costs and time costs required for collating the image report, and further improving the practicability of the method.

The current disclosure provides for mapping medical images to style transferred medical images using deep neural networks, while maintaining clinical quality of the style transferred medical image, thereby enabling a clinician to evaluate medical images in a preferred style without loss of clinically relevant content. In one embodiment the current disclosure provides for a method comprising, acquiring a medical image of an anatomical region of a subject, wherein the medical image is in a first style, selecting a target style, wherein the target style is distinct from the first style, selecting a clinical quality metric, selecting a trained style transfer network based on the target style and the clinical quality metric, mapping the medical image to a style transferred medical image using the trained style transfer network, wherein the style transferred medical image is in the target style, and displaying the style transferred medical image via a display device.

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 non-transitory computer-readable medium (CRM) storing computer program code executed by a computer processor that executes a process of acquiring a medical image generated based on a signal detected by a catheter inserted to a lumen organ, estimating a position of an object at least included in the acquired medical image by inputting the medical image to a first learning model for estimating a position of an object included in the medical image, extracting from the medical image an image portion by using the estimated position of the object as a reference, and recognizing the object included in the extracted image portion by inputting the image portion to a second learning model for recognizing an object included in the image portion.