Concepts for the identification of a gain parameter value for ultrasound hemodynamic analysis are proposed. Overall, an output B-mode gain value may be determined which can be used to produce a B-mode ultrasound image having an optimized contrast-to-noise ratio in a region of interest of the ultrasound image. The global B-mode gain may be adjusted, resulting in an improvement of the contrast-to-noise ratio in the region of interest. Using the proposed concept(s), an output B-mode gain may be automatically generated which is suitable for hemodynamic analysis of a blood vessel of a subject, without the need for intervention by a skilled operator.

A computer implemented method for generating a 3D printable model of a patient specific anatomic feature from 2D medical images is provided. A 3D image is automatically generated from a set of 2D medical images. A machine learning based image segmentation technique is used to segment the generated 3D image. A 3D printable model of the patient specific anatomic feature is created from the segmented 3D image.

A method for correcting blood flow velocity is provided, including receiving a plurality of medical images depicting a target blood vessel, receiving electrocardiogram data of an object of measurement, calculating blood flow velocity of the target blood vessel using the plurality of medical images, and correcting the calculated blood flow velocity based on the electrocardiogram data and standard blood flow velocity data of the target blood vessel.

A method includes identifying stenosed region within vessel in vascular image data and generating a revascularized model of the vessel based on the vascular image data with a lumen boundary in the stenosed region adjusted to have a same cross-sectional area as healthy sections of the vessel. The method includes determining a first pressure distribution for the revascularized model at hyperemic flow, determining a second pressure distribution for the vessel in the vascular image data, and calculating a subtracted pressure distribution by subtracting the second pressure distribution from the first pressure distribution. The method includes determining an asymptotic value for the subtracted pressure distribution and calculating a value by subtracting the asymptotic value from a pressure value obtained from the second pressure distribution at a location at a beginning of the stenosed region. The method includes normalizing the value to obtain a location independent FFR value for the stenosed region.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

Described herein is blood flow imaging based on an area under a time-enhancement curve predicted by a computer learning model. Image data comprising a plurality of corresponding images capturing at least a portion of one or both an increase phase and a decline phase of a contrast agent in a cardiovasculature of interest is inputted to a machine learning model to predict an area under a time-enhancement curve of the contrast agent within the cardiovasculature of interest, the predicted area under the time-enhancement curve representing the total sum of contrast agent concentration time product within the cardiovasculature of interest. In an example, a computer implemented method for blood flow imaging comprising: obtaining image data comprising a plurality of corresponding images capturing at least a portion of one or both an increase phase and a decline phase of a contrast agent in a cardiovasculature of interest; providing the image data to a machine learning model to predict an area under a time-enhancement curve of the contrast agent within the cardiovasculature of interest; determining a blood flow characteristic through the region of interest based on the area under the time-enhancement curve. Systems and non-transitory computer-readable media for executing the method are also described.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

According to one embodiment, a medical image processing apparatus includes first specifier, second specifier, determiner and display controller. First specifier collates an ischemic region calculated from a blood vessel visualized into a three-dimensional image in a plurality of phases with a dominating region of the blood vessel, and specifies a culprit vessel in the ischemic region. Second specifier specifies a culprit stenosis in the culprit vessel based on a pressure index calculated from the blood vessel. Determiner determines a connection position to connect a bypass vessel that makes a detour around the culprit stenosis. Display controller displays the determined connection position on a display.

A dynamic analysis apparatus may include a setting section which sets a target region in a lung region of a chest dynamic image; a conversion section which calculates a representative value of a pixel signal value in the target region, and converts the pixel signal value; an extraction section which extracts a pulmonary blood flow signal from the image; and a calculation section which calculates a change in the pulmonary blood flow signal, and calculates a feature amount regarding pulmonary blood flow. The setting section may determine a size of the target region based on a size of a body part other than a lung blood vessel, a movement amount of a body part other than the lung blood vessel or subject information of the chest dynamic image, the subject information regarding a subject of the radiation imaging, and the setting section may set the target region.

An X-ray diagnostic apparatus in embodiments includes a calculating module, a generator, and a changing module. The calculating module calculates feature quantity concerning a flow of a contrast material for each pixel in a predetermined section based on temporal transition in signal intensity of the contrast material in a predetermined section of a plurality of X-ray images radiographed with time by using the contrast material. The generator generates a first color image in which color information corresponding to the feature quantity concerning the flow of the contrast material in a first section as the predetermined section is reflected in each pixel. The changing module changes the predetermined section to a second section that is within the first section. the generator generates a second color image in which color information corresponding to the feature quantity concerning the flow of the contrast material in the second section is reflected in each pixel based on the color information corresponding to the second section out of the color information corresponding to the feature quantity concerning the flow of the contrast material in the first section and the feature quantity calculated in the second section.