There is provided a diagnostic support program capable of displaying movement of an organ. A diagnostic support program that analyzes images of a human organ and displays analysis results. The program acquires a plurality of frame images of a human organ and calculates a frequency characterizing a state of the organ based on each of the frame images. A phase difference between a waveform in a previously acquired organ model and a waveform corresponding to the calculated frequency is calculated. A signal is then output indicating the phase difference.

A method for generating a subtraction image for digital subtraction angiography to reduce noise and movement artifacts. Obtaining a plurality of mask images of an object takes place before administering a contrast agent into the object and obtaining a map of the object after administering a contrast agent into the object. A first sum image is obtained from the plurality of mask images in that the plurality of mask images is summed in each case multiplied by an individual weighting. The individual weightings for each of the plurality of mask images are automatically determined by an optimization method, and the subtraction image is ascertained by subtraction of the sum image from the map.

Provided herein are systems and methods for automated identification of volumes of interest in volumetric brain images using artificial intelligence (AI) enhanced imaging to diagnose and treat acute stroke. The methods can include receiving image data of a brain having header data and voxel values that represent an interruption in blood supply of the brain when imaged, extracting the header data from the image data, populating an array of cells with the voxel values, applying a segmenting analysis to the array to generate a segmented array, applying a morphological neighborhood analysis to the segmented array to generate a features relationship array, where the features relationship array includes features of interest in the brain indicative of stroke, identifying three-dimensional (3D) connected volumes of interest in the features relationship array, and generating output, for display at a user device, indicating the identified 3D volumes of interest.

The present disclosure provides systems and methods to receiving OCT or IVUS image data frames to output one or more representations of a blood vessel segment. The image data frames may be stretched and/or aligned using various windows or bins or alignment features. Arterial features, such as the calcium burden, may be detected in each of the image data frames. The arterial features may be scored. The score may be a stent under-expansion risk. The representation may include an indication of the arterial features and their respective score. The indication may be a color coded indication.

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.

In part, the disclosure relates to methods, and systems suitable for evaluating image data from a patient on a real time or substantially real time basis using machine learning (ML) methods and systems. Systems and methods for improving diagnostic tools for end users such as cardiologists and imaging specialists using machine learning techniques applied to specific problems associated with intravascular images that have polar representations. Further, given the use of rotating probes to obtain image data for OCT, IVUS, and other imaging data, dealing with the two coordinate systems associated therewith creates challenges. The present disclosure addresses these and numerous other challenges relating to solving the problem of quickly imaging and diagnosis a patient such that stenting and other procedures may be applied during a single session in the cath lab.

A SyntheticMulti-Exposure Speckle Imaging (syMESI) methodology necessarily utilizing an optical interferometer apparatus as part of the speckle imaging system to overcome the optical detector noise that conventionally limits the reliable and accurate determination of a speckle contrast characteristic at low exposure times. The use of such methodology enabled a quantitative determination of absolute value(s) of changes of motion at the target object (such as blood flow changes in tissue) at low photon budget of less than 40 counts of average detection intensity and/or quantitative imaging of the blood flow at the object in interoperative setting with a low-cost camera sensor.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry acquires image data including image data of a blood vessel of a subject. The processing circuitry performs analysis related to the blood vessel by using the image data, and specifies a region of interest in the blood vessel based on a result of the analysis. The processing circuitry performs fluid analysis on a region other than the region of interest at a first accuracy, and performs fluid analysis on the region of interest at a second accuracy that is higher than the first accuracy.

A computer implemented method for blood flow imaging including: obtaining CT or MRI 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; extracting at least one image feature from the CT or MRI image data; providing the at least one image feature and an associated at least one non-image feature to a machine learning model to generate a predicted value of area under a time-enhancement curve of the contrast agent within the cardiovasculature of interest, the machine learning model trained with training inputs of the at least one image feature with the at least one non-image feature, and associated with an area under a time-enhancement curve value as ground truth; converting the predicted value of area under the time-enhancement curve to a time rate of change of contrast agent concentration in the cardiovasculature of interest; determining a blood flow characteristic in the cardiovasculature of interest based on a ratio of mass of the contrast agent in the cardiovasculature of interest to the time rate of change of contrast agent concentration in the cardiovasculature of interest. Systems for blood flow imaging are also described.

Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parametrized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.