A technique for determining a cardiac metric from rest and stress perfusion cardiac magnetic resonance (CMR) images is provided. A neural network system for determining at least one cardiac metric from CMR images comprises an input layer configured to receive at least one CMR image representative of a rest perfusion state and at least one CMR image representative of a stress perfusion state. The neural network system further comprises an output layer configured to output at least one cardiac metric based on the at least one CMR image representative of the rest perfusion state and the at least one CMR image representative of the stress perfusion state. The neural network system with interconnections between the input layer and the output layer is trained by a plurality of datasets. Each of the datasets comprises an instance of the at least one CMR image representative of the rest perfusion state and the at least one CMR image representative of the stress perfusion state for the input layer and the at least one cardiac metric for the output layer.

In a method for generating a biomarker quantifying spatial homogeneity of a medical parameter map, A) the parameter map is provided, B) at least two parameter classes are provided, where each parameter value is to be assigned to a parameter class, C) a sub-area of the examination area is selected, D) in each case, a frequency value for each parameter class for the sub-area is determined, E) a heterogeneity indicator for the sub-area is determined based on the frequency values of the parameter classes, F) further heterogeneity indicators are generated for at least two further sub-areas that are at least partially different from one another, G) a statistical homogeneity value is determined by statistical evaluation of the heterogeneity indicator of the sub-area and the further heterogeneity indicators, and H) the statistical homogeneity value is provided as a biomarker.

A magnetic resonance imaging apparatus captures a morphology image and a function image that are captured with respect to an equal imaging region of an object under examination. Processing for deforming the morphology image is performed using a deformation parameter and moving positions of structural objects included in the morphology image to respective positions of structural objects of a previously determined standard morphology. Then, the function image is deformed using a value of the deformation parameter used in deforming the morphology image to cause a position of a region included in the function image to coincide with a position of a corresponding region of the standard morphology or by using the standard morphology in an opposite direction using the value of the deformation parameter to cause a position of a region of the structural object thereof to coincide with a position of a corresponding region included in the function image.

A method of performing personalized neuromodulation on a subject is provided. The method includes acquiring functional magnetic resonance imaging (fMRI) data of a brain of the subject. The method also includes calculating functional connectivity of the brain between a voxel in a subcortical region of the brain and a voxel in a cortical region of the brain, based on the fMRI data. The method also includes identifying a target location in the brain to be targeted by neuromodulation based on the calculated functional connectivity.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Systems and methods execution of a magnetic resonance imaging pulse sequence including a first spin-echo echo planar imaging pulse sequence to acquire first data of a volume, a second spin-echo echo planar imaging pulse sequence comprising a first one or more diffusion gradient pulses to acquire first diffusion data of the volume, a third spin-echo echo planar imaging pulse sequence comprising a second one or more diffusion gradient pulses to acquire second diffusion data of the volume, and a fourth spin-echo echo planar imaging pulse sequence comprising a third one or more diffusion gradient pulses to acquire third diffusion data of the volume, generation of perfusion metrics based on the first data, and generation of diffusion metrics based on the first data, the first diffusion data, the second diffusion data, and the third diffusion data.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.