A method is for computed tomography imaging. In an embodiment, the method includes provisioning a CT data set of an object, the CT data set being previously recorded via a multispectral recording method; suppressing a contrast, caused by a tissue type, and generating a contrast-suppressed data set from the CT data set provisioned; and analyzing at least the contrast-suppressed data set generated or a data set generated via a machine learning algorithm based on the contrast-suppressed data set, the analyzing being configured to identify at least one change in the tissue type. A corresponding device, a control device for a computed tomography system or a diagnosis system, and a diagnosis system and a computed tomography system are also disclosed.

Images can be generated with overlays indicating an amount of brain tissue damage based on the disruption of blood supply. Imaging data can be analyzed to determine perfusion parameters with respect to regions of the brain of an individual. The thresholds for the perfusion parameters with respect to the presence of damaged brain tissue can be based on a period of time elapsed since the onset of a biological condition disrupting blood flow to one or more regions of the brain of the individual. The imaging data can also be analyzed to determine measures of hypodensity with respect to regions of the brain of the individual. A likelihood of the measures of hypodensity corresponding to damaged brain tissue can also be determined based on the period of time elapsed since the onset of the biological condition.

Provided is a dynamic analysis apparatus that predicts a respiratory function value based on frame images showing dynamics of chest. The dynamic analysis apparatus includes a hardware processor that obtains a first lung size value of a removal target site and a second lung size value of a left or right lung field including the removal target site, calculates a proportion between the first and second lung size values as a size proportion, calculates a first feature amount concerning respiratory function of the left or right lung field including the removal target site and a second feature amount concerning respiratory function of the lung fields as a whole, calculates a proportion between the first and second feature amounts as a feature amount proportion, and calculates an expected rate of the respiratory function without the removal target site, based on a product of the size proportion and the feature amount proportion.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to obtain a medical image. The processing circuitry is configured to calculate a first blood flow direction on the basis of a structure of a region of interest rendered in the medical image. The processing circuitry is configured to calculate a second blood flow direction on the basis of a structure in the surroundings of the region of interest. The processing circuitry is configured to identify a condition of the region of interest, on the basis of the first blood flow direction and the second blood flow direction.

A method for vascular assessment is disclosed. The method, in some embodiments, comprises receiving a plurality of 2-D angiographic images of a portion of a vasculature of a subject, and processing the images to produce a stenotic model over the vasculature, the stenotic model having measurements of the vasculature at one or more locations along vessels of the vasculature. The method, in some embodiments, further comprises obtaining a flow characteristic of the stenotic model, and calculating an index indicative of vascular function, based, at least in part, on the flow characteristic in the stenotic model.

There is provided a medical image processing apparatus which includes a first extraction unit configured to extract coronary arteries depicted in images of a plurality of time phases relating to the heart, and to extract at least one stenosed part depicted in each coronary artery; a calculation unit configured to calculate a pressure gradient of each of the extracted coronary arteries, based on tissue blood flow volumes of the coronary arteries; a second extraction unit configured to extract an ischemic region depicted in the images; and a specifying unit configured to specify a responsible blood vessel of the ischemic region by referring to a dominance map, in which each of the extracted coronary arteries and a dominance territory are associated, for the extracted ischemic region, and to specify a responsible stenosis, based on the pressure gradient corresponding to a stenosed part in the specified responsible blood vessel.

Embodiments for assessing flow at an anatomical region of interest are disclosed. One embodiment uses pulsed contrast media injections at a known frequency along with corresponding image data to derive a measurement of blood flow velocity at the region of interest. Another embodiment uses incremental changes in known contrast media injection flow rates to match the blood flow rate relative to one of these known contrast media injection flow rates based on the presence of a particular indicia in image data. For example, this indicia can be the flow of contrast media out from a coronary artery back into the aorta or the onset of a steady state pixel density. A further embodiment uses contrast media injections that are synchronized with the cardiac cycle. For example, contrast media injections can be synchronized with the diastolic and/or systolic phases and used to measure blood flow accordingly.

Methods and systems are provided for cardiac computed tomography imaging. In one embodiment, a method comprises reconstructing an image from projection data acquired during a scan with a reconstruction time determined based on a model relating a timing of an event to be imaged to a heart rate measured during the scan. In this way, the timing of a reconstruction may be consistently applied for a series of reconstructions, thereby inherently registering the reconstructions.

Methods and systems are described for assessing a vessel obstruction. The methods and systems obtain a volumetric image dataset of a myocardium and at least one coronary vessel, wherein the myocardium comprises muscular tissue of the heart. A three-dimensional (3D) image corresponding to a coronary vessel of interest is created from the volumetric image dataset. Feature data that represents features of both the myocardium and the coronary vessel of interest is generated. At least some of the feature data is determined by a first machine learning-based model based on the 3D image. A second machine learning-based model is used to determine at least one parameter based on the feature data, wherein the at least one parameter represents functionally significant coronary lesion severity of the coronary vessel of interest.

A method of automatically determining a calcium score for at least one coronary artery is disclosed. The method comprises receiving cardiac non-contrast CT data indicative of a cardiac non-contrast CT scan carried out on a patient, analysing the cardiac non-contrast CT data in a calcified components identifier to detect candidate coronary artery calcified components, and analysing cardiac non-contrast CT data associated with the candidate coronary artery calcified components using a radiomics analyser to determine radiomic characteristics of the candidate coronary artery calcified components. The method also comprises applying machine learning to the determined radiomic characteristics associated with each candidate coronary artery calcified component to identify any calcifications that are located on a coronary artery, analysing the cardiac non-contrast CT data to identify at least one body component in the cardiac non-contrast CT data not associated with a coronary artery of the patient, and using the identified at least one body component in the cardiac non-contrast CT data to remove or avoid misclassification of calcifications on a coronary artery that are located on the at least one identified body component.