Systems and methods are provided for automated analysis of angiographic images. An angiographic imaging system is configured to capture a first image of a region of interest, representing a first time, and a second image of a region of interest, representing a second time. A registration component is configured to register the first image to the second image. A difference component is configured to generate a difference image from the first image and the second image. A pattern recognition component is configured to assign a clinical parameter to the region of interest from the difference image and at least one of the first image and the second image.

A system (100) includes a computer readable storage medium (122) with computer executable instructions (124), including: a biophysical simulator (126) configured to determine a fractional flow reserve value. The system further includes a processor (120) configured to execute the biophysical simulator (126), which employs machine learning to determine the fractional flow reserve value with spectral volumetric image data. The system further includes a display configured to display the determine fractional flow reserve value.

There is provided a method of processing a cerebrovascular medical image, the method comprising receiving magnetic resonance angiography (MRA) image associated with a cerebrovascular tissue comprising blood vessels and brain tissues other than blood vessels; segmenting MRA image using a prior appearance model for generating first prior appearance features representing a first-order prior appearance model and second appearance features representing a second-order prior appearance model of the cerebrovascular tissue, wherein current appearance model comprises a 3D Markov-Gibbs Random Field (MGRF) having a 2D rotational and translational symmetry such that MGRF model is 2D rotation and translation invariant; segmenting MRA image using current appearance model for generating current appearance features distinguishing blood vessels from other brain tissues; adjusting MRA image using first and second prior appearance features and current appearance futures; and generating an enhanced MRA image based on said adjustment. There is also provided a system for doing the same.

A method for precision cancer treatment by identifying drug resistance is provided. In some embodiments, the method may include: detecting tumor oxygenated perfusion by having a patient breathe air to acquire MRI baseline data; inhalation of hyperoxia gas to generate higher than baseline HbO.sub.2 blood circulating in body to acquire MRI enhanced data; the region-of-interest (ROI), which in this case is a tumor volume (V.sub.0), and which may be performed by volume contour tracing/region-of-interest (ROI) analysis and 3D tumor volumetry methods; calculating voxel's enhanced signal intensity (SI); calculating tumor oxygenated perfusion percentage (OPP %); selecting different threshold and calculating maps such as a reconstruction OPP % pseudo color map; calculating tumor volume change ratio (Vt %); overlaying reconstruction OPP % pseudo color map to original images for visualizing tumor response data; drawing or plotting the OPP % and Vt % on a cancer treatment response information diagram, and identifying the type of drug resistance, classifying the drug resistance being caused by poor drug distribution factor or cells-specific factor based on pooled collected data.

A system for estimating post-virtual stenting fractional flow reserve (FFR) capable of: receiving geometrical parameters of a blood vessel segment, the geometrical parameters comprising first, second and third geometrical parameters; with a proximal end as a reference point, deriving a reference lumen diameter function and calculating a pre-virtual stenting percent diameter stenosis based on the above geometrical parameters and the distance from position along the segment to the reference point; receiving a virtual stenting location; calculating a geometrical parameter of a virtual lumen of the post virtually-stented segment, deriving a post-virtual stenting geometrical parameter difference function and calculating a percent diameter stenosis based on the third geometrical parameter, the virtual stenting location and the reference lumen diameter function; taking derivative difference functions of the post-virtual stenting geometrical parameter difference function in multiple scales; and obtaining FFR based on the multiple scales of derivative difference functions and a maximum post-virtual stenting mean blood flow velocity. Post-virtual stenting FFR is estimated based on changes in percent diameter stenosis and the maximum mean blood flow velocity after virtual stent implantation using a multi-scale calculation method.

A control device according to an embodiment of the present technology includes an acquisition section, a block control section, and a calculator. The acquisition section acquires an image signal of a tissue of a living body irradiated with laser light and on which image-capturing has been performed. The block control section controls a size of a pixel block according to an image-capturing condition for the image-capturing on the tissue of a living body. The calculator calculates speckle data based on the acquired image signal, using the pixel block of which the size is controlled.

A dynamic analysis system includes a diagnostic console which calculates at least one index value representing variation in a target portion of a human body from at least one dynamic image acquired by performing radiographic imaging to a subject containing the target portion, and evaluates flexibility of the target portion based on the calculated index value.

A medical-information processing apparatus according to an embodiment includes processing circuitry. The processing circuitry acquires medical image data that is obtained during imaging on the subject in a resting state in the time phase where the relationship between the volume of blood flow and the pressure in a blood vessel in the cardiac cycle of the subject indicates a proportional relationship. The processing circuitry extracts the structure of a blood vessel, included in the medical image data, applies fluid analysis to the structure of the blood vessel to obtain a first index value, which is obtained based on the pressure in the blood vessel on the upstream side of a predetermined position within the blood vessel and the relation equation between the volume of blood flow and the pressure in the blood vessel in the resting state, and a second index value, which is obtained based on the pressure in the blood vessel on the downstream side of the predetermined position and the relation equation, and calculates the pressure ratio, which is the ratio of the first index value to the second index value.

A method is described for determination of an initialization time point of imaging of a region of interest of an object to be examined. A slice of the object is selected for bolus-tracking images in which the flow of a fluid flowing toward the region of interest is observable. A plurality of bolus-tracking images of the slice is taken. Time-density curves are determined based upon intensity values assigned to the individual image points acquired in the plurality of bolus-tracking images for the selected slice. Individual image points are divided into groups according to similarity of time-density curves assigned to the individual image points. Finally, the time at which an intensity value assigned to one of the groups exceeds a threshold value is determined. Also described are a method for carrying out the imaging of a region of interest; an initialization time point determination device; and a computed tomography system.

A pressure measuring device 30 installs on a tube 11 for transferring a medium (e.g., blood in a extracorporeal blood circulator) so as to measure a pressure of the medium inside the tube 11. The pressure measuring device 30 includes a main body portion 31 mountable to the tube 11, an image acquisition unit 32 disposed in the main body portion 31 so as to acquire image information on a pressure receiver that is deformed in response to the received pressure of the medium inside the tube 11, and a control unit 100 that converts the image information acquired by the image acquisition unit into pressure information about the pressure.