The medical image processing apparatus according to the present embodiment includes processing circuitry. The processing circuitry is configured to acquire contrast image data generated by imaging a subject. The processing circuitry is configured to input the acquired contrast image data to a learned model to generate a time phase data classified according to a contrast state of a lesion area with a contrast agent included in the acquired contrast image data, the learned model being for generating the time phase data based on the acquired contrast image data.

An image processing method for image-based physiological measurement, includes converting at least one user's image signal into image data; determining at least one region of interest within the image data; analyzing image information inside the region of interest to generate physiological information of the user; determining a feedback control signal or a control signal to optimize the physiological information of the user; and adjusting an image sensing unit or an image signal processing unit according to the feedback control signal or the control signal.

An information processing apparatus includes an obtaining unit, an inference section, and a selection unit. The obtaining unit is configured to obtain a temporal subtraction image between a first medical image captured at a first point of time and a second medical image captured at a second point of time. The inference section includes a plurality of inference units, each for making an inference from the temporal subtraction image. The selection unit is configured to select, based on a region of interest in the obtained temporal subtraction image, at least one inference unit from the plurality of inference units in the inference section. In response to being selected by the selection unit, the at least one inference unit so selected makes the inference from the temporal subtraction image.

Characterizing myocardial perfusion pathology includes analyzing a plurality of medical images of at least a portion of the heart of a subject of interest (20), acquired in a consecutive manner by a medical imaging modality (10). Intensities of selected myocardial image positions from the plurality of medical images are sampled and assigned an index representing an order of acquisition to the respective sampled intensities of the myocardial image positions to obtain intensity curves (60). An index number (64, 66) indicative of a spatio-temporal perfusion inhomogeneity or perfusion dephasing among at least a subset of myocardial segments of the plurality of myocardial segments is calculated, based on the obtained intensity curves (60).

A method and apparatus for estimating heart rate of a subject from a video image of the subject. Regions of interest are generated by: detecting and tracking feature points through the video image sequence, triangulating the feature points and generating square regions of interest corresponding to the in-circles of the triangles; or, according to size and location probability distributions which are defined to have a high probability for image areas away from strong intensity gradients and which generate good quality signals. In an alternative embodiment, the intensity variations from the square regions of interest through the frame sequence are taken as time series signals and those signals which have a strong peak in the power spectrum are selected and subject to principal component analysis. The principal component with a highest signal quality is selected and its frequency is found and used to estimate the heart rate.

A system and method for automated characterization of solid tumors using medical imaging. The system comprises an interface that is configured to acquire data from medical imaging devices, one or more processors, and an outputting device that reports the characterization of said solid tumor. The method of automated characterization, which is implemented by the system, acquires a sequence of images from the medical imager using a Dynamic Contrast Enhanced (DCE) imaging protocol, performs image registration, detects the contour of the solid tumor, and dividing the contours to segments. For each segment, the method calculating a displacement of the contrast material, fitting the displacement to a flow model and extracting an estimation of the interstitial fluid velocity. The estimated interstitial fluid velocity of the segments provide characterization of the solid tumor and includes an assessment of the tumor interstitial fluid pressure, the tumor drug delivery efficiency, and the tumor prognostic or metastasis risk.

Apparatus for operating MRI is disclosed. The apparatus comprises: a control for operating an MRI scanner to carry out an MRI scan; an input for receiving first and second MRI scans respectively at the beginning and end of a predetermined time interval post contrast administration; a subtraction map former for forming a subtraction map from the first and the second MRI scans by analyzing the scans to distinguish between a population in which contrast clearance from the tissue is slower than contrast accumulation, and a population in which clearance is faster than accumulation; and an output to provide an indication of distribution of the populations. The control is configured to carry out the first scan at least five minutes and no more than twenty minutes post contrast administration and to carry out the second scan such that the predetermined time period is at least twenty minutes.

Apparatus, systems, and methods to improve automated identification, monitoring, processing, and control of a condition impacting a patient using image data and artificial intelligence classification are disclosed. An example image processing apparatus includes an artificial intelligence classifier to: process first image data for a patient from a first time to determine a first classification result indicating a first severity of a condition for the patient; and process second image data for the patient from a second time to determine a second classification result indicating a second severity of the condition for the patient. The example image processing apparatus includes a comparator to compare the first classification result and the second classification result to determine a change and a progression of the condition associated with the change. The example image processing apparatus includes an output generator to trigger an action when the progression corresponds to a worsening of the condition.

A dynamic analysis system includes a hardware processor and an output device. The hardware processor obtains a cycle of temporal change in a feature amount relevant to a function to be diagnosed from each of dynamic images obtained by imaging of a dynamic state of a living body with radiation. The hardware processor further adjusts the obtained cycle, thereby generating a plurality of cycle-adjusted data having cycles of the temporal change in the feature amount being equal to one another. The hardware processor further generates difference information at each phase in the plurality of cycle-adjusted data. The output device outputs the difference information.

A medical scan diagnosing system is operable to receive a medical scan. Diagnosis data of the medical scan is generated by performing a medical scan inference function on the medical scan. The first medical scan is transmitted to a first client device associated with a user of the medical scan diagnosing system in response to the diagnosis data indicating that the medical scan corresponds to a non-normal diagnosis. The medical scan is displayed to the user via an interactive interface displayed by a display device corresponding to the first client device. Review data is received from the first client device, where the review data is generated by the first client device in response to a prompt via the interactive interface. Updated diagnosis data is generated based on the review data. The updated diagnosis data is transmitted to a second client device associated with a requesting entity.