Embodiments of the present techniques provide apparatus and methods for analysing intracoronary images, for example to predict the likelihood of a disease, disease presentation or event, and/or to track performance of a drug or other treatment. The method may comprise: for each image in the set of images of a coronary artery: classifying the image, using a first neural network, for the presence or absence of diseased tissue; when the image is classified as having diseased tissue present, classifying the image, using a second neural network, for the presence or absence of an artefact; determining whether to analyse the image based on the classifying steps; when the image is to be analysed, analysing the image by identifying, using a third neural network, one or more features of interest in a coronary artery tissue; and measuring each identified feature of interest.

Methods that implement image-guided tissue analysis, MRI-based computational modeling, and imaging informatics to analyze the diversity and dynamics of molecularly-distinct subpopulations and the evolving competitive landscapes in human glioblastoma multiforme (“GBM”) are provided. Machine learning models are constructed based on multiparametric MRI data and molecular data (e.g., CNV, exome, gene expression). Models can also be built based on specific biological factors, such as sex and age. Inputting MRI data into the trained predictive models generates maps that depict spatial patterns of molecular markers, which can be used to quantify and co-localize regions molecularly distinct subpopulations in tumors and other regions, such as the non-enhancing parenchyma, or brain around tumor (“BAT”) regions.

A method and apparatus for analyzing diagnostic image data are provided in which correspondence detection between a first diagnostic image and a second diagnostic image of a vessel of interest in a patients vasculature is performed on the basis of at least one functional parameter by matching the one or more values of said functional parameter at particular positions along the vessel of interest as shown in the first diagnostic image and the second diagnostic image to one another, thereby determining a correlation between the positions in the basis of said functional parameter values rather than solely the vessel geometry.

The present disclosure relates to a system (100) for scoring motion of a heart wall (214, 218). The system (100) includes an imaging system (102) operable to acquire a first image (230) of the heart wall (214, 218) at a first time and a second image (240) of the heart wall (214, 218) at a second time. A processor (108) is provided to identify a first set of contour data in the first image (230); and a second set of contour data in the second image (240). The processor (108) defines at least one element (Ei) representing a cardiac cyclic change in a section of the heart wall (214, 218) in dependence on the first and second sets of contour data. Each element (Ei) is analysed to generate at least one metric which is compared with a reference data model to score the motion of the corresponding section of the heart wall (214, 218). The disclosure also relates to a method of scoring motion of a heart wall (214, 218); and a non-transitory computer-readable medium.

An exemplary method of determining a surgical spinal correction for a subject using analysis of motion capture images of the subject, which uses the steps of obtaining pre-operative three-dimensional images of a spinal region, obtaining a pre-operative time sequenced set of images of the subject during a movement progression of said subject, calculating in a plurality of the motion capture images, alignment parameters relating to upper and lower body regions of the subject, and determining if any of the calculated alignment parameters are outside their predetermined acceptable ranges in one or more of the images, iteratively adjusting anatomical elements in three-dimensional images until all of the calculated alignment parameters are within their predetermined acceptable ranges; and adjusting spinal anatomy in the three-dimensional images according to the degree of adjustment of spinal parameters in the motion capture images to determine a surgical spinal correction.

In one embodiment, a patient's brain is evaluated after onset of a stroke by capturing computed tomography angiography (CTA) images of the brain, analyzing the CTA images with a CTA image analysis program to evaluate the patient's brain, and generating results based upon the analysis that provide an assessment of the brain. In some cases, the CTA image analysis program comprises a machine-learning algorithm that has been trained on the results of perfusion imaging analysis.

Methods and systems for tracking orthodontic treatment are disclosed. In an embodiment, an orthodontic treatment tracking method for tracking progress of a tooth alignment treatment comprises: receiving a patients mouth image; comparing the patients mouth image with a tooth alignment treatment checkpoint image to determine if the tooth alignment treatment has reached a treatment checkpoint; and generating an alignment treatment progress indication indicating whether the tooth alignment treatment has reached the treatment checkpoint.

A method for measuring tracer uptake in cerebral tissue of a patient by analyzing cerebral images acquired by positron emission tomography, the cerebral images including an early image and a late image, includes selecting a region of interest within the early and late images, determining a first domain D.sub.E defined by all voxels of the region of interest of the early image whose intensity is greater than a first given value, determining a second domain D.sub.L defined by all voxels of the region of interest of the late image whose intensity is greater than a second given value, calculating average intensities <I.sup.L>|.sub.DE and <I.sup.L>|.sub.DL in the first domain D.sub.E and second domain D.sub.L, respectively, of the late image, and calculating ratio of the average intensities TDr=<I.sup.L>|.sub.DE/<I.sup.L>.sub.DL.

Systems and methods for predicting motion of a target using imaging are provided. In one aspect, a method includes receiving image data, acquired using an imaging system, corresponding to a region of interest (“ROI”) in a subject, and generating a set of reconstructed images from the image data. The method also includes processing the set of reconstructed images to obtain motion information associated with a target in the ROI, and applying the motion information in a motion prediction framework to estimate a predicted motion of the target. The method further includes generating a report based on the predicted motion estimated.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry acquires image data including image data of a blood vessel of a subject. The processing circuitry performs analysis related to the blood vessel by using the image data, and specifies a region of interest in the blood vessel based on a result of the analysis. The processing circuitry performs fluid analysis on a region other than the region of interest at a first accuracy, and performs fluid analysis on the region of interest at a second accuracy that is higher than the first accuracy.