The invention provides for a method of training a neural network (322) configured for providing a further processing location (326). The method comprises providing (200) a labeled medical image (100), wherein the labeled medical image comprises multiple labels each indicating a truth processing location (102, 104, 106). The method further comprises inputting (202) the labeled medical image into the neural network to obtain one trial processing location. The one trial processing location comprises a most likely trial processing location (108). The method further comprises determine (204) the closest truth processing location (106) for the most likely trial processing location. The method further comprises calculating (206) an error vector (110) using the closest truth processing location and the most likely trial processing location. The method further comprises training (208) the neural network using the error vector.

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.

An image processing apparatus comprises processing circuitry configured to: obtain first medical image data captured at a first time and second medical image data captured at a second time different from the first time, the first medical image data and the second medical image data including data representing a bolus of contrast material in a tubular anatomical structure, wherein the bolus of contrast material has moved between the first time and the second time; determine an expected motion of the bolus of contrast material through the tubular anatomical structure between the first time and the second time; and. perform a registration process to obtain a registration of the first medical image data and the second medical image data based at least in part the expected motion of the bolus of contrast material through the tubular anatomical structure.

The invention provides systems and methods for evaluating and classifying one or more lesions formed in intravascular and/or intracardiac tissue for assisting in the diagnosis and/or treatment of a cardiac-related condition.

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.

For radial sampling in magnetic resonance imaging (MRI), a rescaling factor is determined from k-space data for each coil. The rescale factor is inversely proportional to the streak energy in the k-space data. The k-space data from the coils is rescaled for reconstruction, such as weighting the k-space data by the rescale factor in a data consistency term of iterative reconstruction. The rescale factor is additionally or alternatively used to determine a correction field for correction of intensity bias applied to intensities in the image-object space after reconstruction. These approaches may result in a diagnostically useful bias-corrected image with reduced streak artifact while benefiting from the efficient computation (i.e., computer operates to reconstruct more quickly).

A method of analysing the magnitude of Magnetic Resonance Imaging (MRI) data is described. The method comprising: using the magnitude only of the multi-echo MRI data of images from the subject, where images are acquired at arbitrarily timed echoes including at least one echo time where water and fat are not substantially in-phase; fitting the magnitude of said multi-echo MRI data to a single signal model to produce a plurality of potential solutions for the relative signal contributions for each of the at least two species from the model, by using a plurality of different starting conditions to generate a particular cost function value for each of the plurality of starting conditions, where said cost function values are independent of a field map term for the MRI data; analysing said cost function values to calculate relative signal separation contribution for each species at each voxel of the images.

A computer-implemented method of reducing noise and artifacts in medical images is provided. The method includes receiving a series of medical images along a first dimension, wherein the signals in the medical images having a higher correlation in the first dimension than the noise and the artifacts in the medical images. The method further includes, for each of a plurality of pixels in the medical images, deriving a series of data points along the first dimension based on the series of medical images, inputting the series of data points into a neural network model, and outputting the component of signals in the series of data points. The neural network model is configured to separate a component of signals from a component of noise and artifacts in the series of data points. The method further includes generating a series of corrected medical images based on the outputted component of signals.

Systems and methods for generating a synthesized contrast enhanced medical image are provided. An input medical image is received. A synthesized contrast enhanced medical image is generated based on the input medical image using a trained machine learning based generator network. The synthesized contrast enhanced medical image includes one or more synthesized contrast enhanced regions of pathological tissue. The synthesized contrast enhanced medical image is output.

An exemplary system, method, and computer-accessible medium for detection of functional disorder(s) or aging progression of patient(s) can be provided which can include, for example, receiving magnetic resonance imaging (MRI) information of the portion(s), generating gadolinium (“Gd”) enhanced map(s) based on the MRI information using a machine learning procedure(s), and detecting the functional disorder(s) or aging progression of the patient(s) based on the Gd enhanced map(s). The Gd enhanced map(s) can be a full dosage Gd enhanced map which can be a full dosage Gd enhanced cerebral blood volume map(s). The machine learning procedure can be a convolutional neural network. The MRI information can include (i) a low-dosage Gd MRI scan(s), and/or (ii) a Gd-free MRI scan(s). Functional disorder(s) or age progression can include a neurodegenerative disease, a neuropsychiatric disease, a neurodevelopment disorder or aging.