A method comprises providing a current state image and at least one reference image, taken from a similar angle range. The image and the at least one reference image are superimposed and a visual representation visualizing the relation between the image and the reference image is provided in order to track displacements of the bone during subsequent operation steps. A system is provided which can use the image data to track displacements and determine deviation from a current state of the elements in question to a target state.

Methods, apparatuses, systems, and implementations for creating 3-dimensional (3D) representations exhibiting geometric and surface characteristics of post-contrast brain lesions are disclosed. 3D MRI images of the brain may be created and acquired. After administration of a contrast substance, brain lesions and other abnormalities may be identified and isolated from the 3D MRI images, with each lesion serving as a region of interest (ROI). 3D region of contrast enhancement images may be created from segmented 3D MRI images and different regions of contrast enhancement of the brain lesion may be depicted. Saved regions of contrast enhancement may be converted into stereolithography format, maximum intensity projection (MIP) images, and/or orthographic projection images. Data corresponding to these resulting 3D region of contrast enhancement images may be used to create 3D printed models of the isolated region of contrast enhancement using 3D printing technology. Analysis of the 3D brain region of contrast enhancement images and the 3D printed region of contrast enhancement models may enable a more efficient an accurate way of determining brain lesion risk factors and effective treatment regimes.

Some aspects include a method of detecting change in biological subject matter of a patient positioned within a low-field magnetic resonance imaging device, the method comprising: while the patient remains positioned within the low-field magnetic resonance device: acquiring first magnetic resonance image data of a portion of the patient; acquiring second magnetic resonance image data of the portion of the patient subsequent to acquiring the first magnetic resonance image data; aligning the first magnetic resonance image data and the second magnetic resonance image data; and comparing the aligned first magnetic resonance image data and second magnetic resonance image data to detect at least one change in the biological subject matter of the portion of the patient.

A medical image processing apparatus comprises processing circuitry configured to acquire a first blood vessel image based on X-rays that are irradiated from a first direction and a second blood vessel image based on X-rays that are irradiated from a second direction; determine a corresponding point on the second blood vessel image, which is a point corresponding to a subject point on the first blood vessel image, by using an epipolar line corresponding to the subject point and blood-flow information based on a change of a density of a contrast agent over time at the subject point; and reconstruct a three-dimensional blood vessel image by using information about the subject point and the corresponding point.

A method and device are for automatic determination of the change of a hollow organ. The method includes providing a first medical image of the organ recorded at a first time; computing a first representation of the organ in the first image; computing a first reference-line of the organ based on the first representation and providing a second medical image of the organ recorded at a second point. The method further includes computing a second representation of the organ in the second image; computing a second reference-line of the organ based on the second representation of the organ; registering of the first and second reference-line to obtain at least one of matched representations of the organ and features derived from the matched representations of the organs; and comparing at least one of the matched representations of the organs and the features derived from the matched representations of the organ.

The present disclosure is directed to a method and system for automatically predicting a physiological parameter based on images of vessel. The method includes receiving the images of a vessel acquired by an imaging device. The method further includes determining a sequence of temporal features at a sequence of positions on a centerline of the vessel based on the images of the vessel, and determining a sequence of structure-related features at the sequence of positions on the centerline of the vessel. The method also includes fusing the sequence of structure-related features and the sequence of temporal features at the sequence of positions respectively. The method additionally includes determining the physiological parameter for the vessel at the sequence of positions, by using a sequence-to-sequence neural network configured to capture sequential dependencies among the sequence of fused features.

An information processing apparatus includes an acquisition unit, a determination unit, and an output unit. The acquisition unit acquires history information of image processing using a plurality of medical images. The determination unit determines, using the acquired history information, whether each of a plurality of candidate images that are candidates of a second medical image used for image processing together with a first medical image selected by an operator has already been processed. The output unit outputs a notification in accordance with the result of the determination.

An image is accepted by one or more processing circuits from a user depicting the user's facial skin. Machine learning models stored in one or more memory circuits are applied to the image to classify facial skin characteristics. A regimen recommendation is provided to the user based on the classified facial skin characteristics.

In some embodiments, a set of eye images related to a subject may be provided to a prediction model. A first prediction may be obtained via the prediction model, where the first prediction is derived from a first eye image and indicates whether an eye condition is present in the subject. A second prediction may be obtained via the prediction model, where the second prediction is derived from a second eye image and indicates that the eye condition is present in the subject. An aspect associated with the first prediction may be adjusted via the prediction model based on the second prediction's indication that the eye condition is present in the subject. One or more predictions related to at least one eye condition for the subject may be obtained from the prediction model, where the prediction model generates the predictions based on the adjustment of the first prediction.

A computer-implemented method for automatically generating a fully quantitative myocardial blood flow map, comprising: receiving myocardial perfusion magnetic resonance imaging (MRI) images and arterial input function (AIF) MRI images; correcting a motion of a heart in the myocardial perfusion MRI images and the AIF MRI images, thereby obtaining motion corrected myocardial perfusion MRI images and motion corrected AIF images; correcting an intensity of the motion corrected myocardial perfusion MRI images and an intensity of the motion corrected AIF images, thereby obtaining surface coil intensity corrected MRI images and surface coil intensity corrected AIF images; using the surface coil intensity corrected MRI images and the surface coil intensity corrected AIF images, determining time-signal intensity characteristics and segmenting a left ventricle myocardial tissue region; and generating the myocardial blood flow map using the motion corrected myocardial perfusion MRI images, the left ventricle myocardial tissue region segmentation and the time-signal intensity characteristics.