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.

Some embodiments are directed to a method of estimating a velocity of a contrast agent. The method includes receiving a plurality of video frames that were produced using a dynamic contrast enhanced imaging process, each video frame including a plurality of pixels/voxels. Information from the video frames is used to estimate velocity vectors indicating the velocity and direction of the agent with the vascular networks. The estimated velocity can be used to diagnose cancer, such as prostate cancer. Instead of velocity vectors, agent trajectories can be determined also used for the same purpose.

Methods and systems for characterizing fluids from a patient are disclosed. The method includes receiving a time series of images of a conduit receiving fluids from the patient, identifying a conduit image region in each of the images, classifying a flow type through the conduit based on an evaluation of the conduit image region in the time series of images, and estimating at least one of a volume of fluids and a quantity of a blood component that has passed through the conduit within a predetermined period of time, based at least in part on the classification of the flow type.

Systems and methods are disclosed for assessing the quality of medical images of at least a portion of a patient's anatomy, using a computer system. One method includes receiving one or more images of at least a portion of the patient's anatomy; determining, using a processor of the computer system, one or more image properties of the received images; performing, using a processor of the computer system, anatomic localization or modeling of at least a portion of the patient's anatomy based on the received images; obtaining an identification of one or more image characteristics associated with an anatomic feature of the patient's anatomy based on the anatomic localization or modeling; and calculating, using a processor of the computer system, an image quality score based on the one or more image properties and the one or more image characteristics.

Methods and apparatus for predicting performance of an individual on a task, the method comprises receiving brain imaging data for the individual, wherein the brain imaging data comprises structural brain data, determining values for at least one characteristic of the structural brain data within regions of interest defined for a population of individuals having different performance levels, and predicting based on the determined values, a performance potential of the individual.

A method is for providing decision-supporting data. In an embodiment, the method includes receiving photon-counting computed tomography data relating to an examination region; determining a location of a thrombus in the examination region, based on the photon-counting computed tomography data received; generating the decision-supporting data, relating to at least one of the thrombus and a vascular wall in a region of the thrombus, based on the photon-counting computed tomography data received and the location of the thrombus determined; and providing the decision-supporting data generated.

Methods for personalized treatment of tumor lesions in subject with metastatic cancer are disclosed.

Methods and systems for characterizing tissue of a subject are disclosed. The method includes receiving a time series of fluorescence images of the tissue of the subject wherein the images define a plurality of calculation regions, generating a plurality of time-intensity curves for the plurality of calculation regions, creating a set of parameter values for each calculation region, generating a total rank value for each calculation region by comparing the sets of parameter values, and converting the total rank value into a ranking map image. Also disclosed are methods and systems for characterizing a wound in tissue by generating a wound index value.

The invention provides for a medical imaging system (100, 300) comprising a processor (106) for controlling the medical imaging system. Execution of machine executable instructions (112) causes the processor to receive (200) a static angiographic image (114) of a region of interest (322), receive (202) a time series of angiographic images (116, 116) of the region of interest, construct (204) an image mask (118) using the static angiographic image, determine (206) a time dependent signal (120) for each voxel within the image mask using the time series of angiographic images, construct (208) a composite angiographic image by: assigning (210) a fill time (126) to each voxel within the image mask using an extremum (124) of the time dependent signal if the extremum deviates from an average of the time dependent signal more than a predetermined threshold, and identifying (212) voxels within the image mask as being unfilled voxels.

Systems for non-contact imaging measurement of blood oxygen saturation and perfusion in a sample are provided including a control unit configured to facilitate acquisition of data from a sample; a data acquisition module coupled to the control unit, the data acquisition module configured to illuminate a field of view (FOV) of the sample using a plurality of wavelengths to provide a plurality of images corresponding to each of the plurality of wavelengths responsive to control signals from the control unit; and an image processing module configured calculate image saturation parameters and reflectance for each of the plurality of images having a unique acquisition time and unique wavelength and extracting blood volume and oxygen saturation data in the FOV using the calculated image saturation parameters and reflectance for each of the plurality of images having a unique acquisition time and unique wavelength.