A method is disclosed for providing a virtual tomographic stroke follow-up examination image. In an embodiment, the method includes: receiving a sequence of temporally successive tomographic perfusion imaging data sets of a region for examination; calculating the virtual tomographic stroke follow-up examination image of the region for examination by applying a trained machine learning algorithm to the sequence of temporally successive tomographic perfusion imaging data sets received; and providing the virtual tomographic stroke follow-up examination image calculated.

The present disclosure provides a method of DDCE-MRF. The method can include: a) introducing two or more contrast agents to a region of interest (ROI) of a subject, the two or more contrast agents having different relaxivities; b) measuring a T1 relaxation time and a T2 relaxation time for locations within the ROI using magnetic resonance fingerprinting (MRF); c) determining, using equations that relate the different relaxivities, the T1 relaxation time, the T2 relaxation time, and concentrations of the two or more contrast agents, the concentrations of the two or more contrast agents for each of the locations within the ROI; and d) producing an image depicting the ROI based, at least in part, on the concentrations of the two or more contrast agents.

Embodiments predict prostate cancer (PCa) biochemical recurrence (BCR) employing an image acquisition circuit that accesses a first pre-treatment image and a second pre-treatment image of a region of tissue demonstrating PCa, a distension feature circuit that extracts a set of distension features from the first pre-treatment image, and computes a first probability of PCa BCR based on the set of distension features, a radiomics circuit that extracts a set of radiomics features from the second pre-treatment image, and computes a second probability of PCa recurrence based on the set of radiomics feature, a combined tumor induced organ distension with tumor radiomics (COnTRa) circuit that computes a joint probability that the region of tissue will experience PCa BCR based on the first probability and the second probability, and a display circuit that displays the joint probability.

The purpose of this embodiment is to describe a one stop shop for staging prostate cancer and a novel application of supervised target detection algorithms to spatially registered multiparametric MRI images in order to non-invasively detect, locate, and score prostate cancer at the voxel level and measure the tumor volume and assign color to the spatially registered MRI to highlight and display tumors, and detect metastases (specifically in the seminal vesicle). To test the approach advanced by the embodiment, a retrospective study analyzes MRI from 26 patients that had also undergone robotic prostatectomy. Whole-mount sections were stained for histopathologic evaluation and matched to the MRI. The stained sections were independently reviewed by pathologists. All slices of various types of MRI were spatially registered and stitched together. Signatures or image-based biomarkers from registered multiparametric MRI training sets were extracted. The untransformed and whitened-dewhitened transformed signatures (based on the statistics of the normal prostate) from a battery of Gleason scores were applied to the stitched hypercubes. Each voxel in the supervised target map was polled to find the signature that achieved the highest Gleason score likelihood. The Gleason scoring and volume measurements were quantitatively validated by comparing the results from 10 patients with prostate adenocarcinoma to the pathologist's assessment of the histology. High correlation between supervised target detection using whitened-dewhitened transformed signatures and histology was observed (p<0.02). Assigning red, green, and blue to the registered MRI hypercubes effectively displays tumors relative to normal prostate tissue. With only minor modifications, supervised target detection and transformation of target signatures and color display may be used to find metastases, specifically to the seminal vesicles. This novel application of supervised target detection algorithms to spatially registered multi-parametric MRI non-invasively detects, locates, and scores prostate cancer at each voxel level and measures the tumor volume.

In a method and processor for evaluating a contrast agent-enhanced two-dimensional magnetic resonance slice image of a heart of a patient in order to determine picture elements revealing contrast agent deposits in the myocardium, an endocardium contour in the magnetic resonance slice image, taking into consideration deposition information describing picture elements potentially revealing contrast agent deposits and determined by image analysis on the basis of a shape assumption for the heart structure that is to be examined, in particular the left ventricle, such that picture elements potentially revealing contrast agent deposits are avoided as much as possible as a contour component. An epicardium contour enclosing the endocardium contour is then determined. Picture elements are marked that indicate contrast agent enhancement in the myocardium lying between the epicardium contour and the endocardium contour as contrast agent deposit.

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.

An image processing method includes: obtaining DCE magnetic resonance images corresponding to a plurality of time points for a same detection target; determining average pixel grayscale values of images of a same lesion region in the DCE magnetic resonance images of the plurality of time points respectively; determining a time to peak according to the average pixel grayscale values corresponding to the plurality of time points; and generating a first-stage time-intensity image before the time to peak and a second-stage time-intensity image after the time to peak respectively according to the DCE magnetic resonance images and the time to peak. The first-stage time-intensity image and the second-stage time-intensity image are 3D images. A pixel grayscale value of each pixel in the first-stage time-intensity image and the second-stage time-intensity image represents a change rate of blood supply intensity and reflects a severity level of a lesion corresponding to the lesion region.

A method includes obtaining image data, selecting image datasets from the image data, creating three-dimensional (3D) matrices based on the selected image dataset, refining the 3D matrices, applying one or more matrix operations to the refined 3D matrices, selecting corresponding matrix columns from the 3D matrices, applying big data convolution algorithm to the selected corresponding matrix columns to create a two-dimensional (2D) matrix, and applying a reconstruction algorithm to create a super-resolution biomarker map image.

A system, method, and computer program product for predicting, anticipating, and/or assessing tissue characteristics obtains measurement information associated with a parameter of a voxel of tissue of a patient measured at two or more time points, the two or more time points occurring before one or more characteristics of the voxel of the tissue are separable in an image generated based on the parameter of the voxel measured at a single time point of the two or more time points, and determines, based on the parameter of the voxel at the two or more time points, the one or more characteristics of the voxel of the tissue.

The present disclosure relates to a method. The method may be performed by accessing data derived from one or more routine clinical medical imaging scans including a lesion in which the lesion and associated vasculature are segmented in a three-dimensional segmentation. At least two features are extracted from the three-dimensional segmentation of the associated vasculature. The at least two features include at least one feature indicative of a morphology of the associated vasculature or a portion thereof, and at least one feature indicative of a function of the associated vasculature or a portion thereof. The at least two features, and/or one or more statistics of the at least two features, are provided to a machine learning model trained to make a prediction concerning the lesion. The prediction concerning the lesion is received from the machine learning model.