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.

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.

Described here are systems and methods for separating magnetic resonance signals that are changing over a scan duration (i.e., dynamic signals) from magnetic resonance signals that are static over the same duration. As such, the systems and methods described in the present disclosure can be used to remove artifacts associated with dynamic signals from images of static structures, or to better image the dynamic signal (e.g., pulsatile blood flow or respiratory motion).

Described here are systems and methods for separating magnetic resonance signals that are changing over a scan duration (i.e., dynamic signals) from magnetic resonance signals that are static over the same duration. As such, the systems and methods described in the present disclosure can be used to remove artifacts associated with dynamic signals from images of static structures, or to better image the dynamic signal (e.g., pulsatile blood flow or respiratory motion).

Methods and apparatuses for training a magnetic resonance imaging model, electronic devices and computer readable storage media are provided. A method may include: acquiring a magnetic resonance image data set; constructing a ring deep neural network to be trained; inputting an under-sampled magnetic resonance image and a full-sampled magnetic resonance image respectively to two neural networks included in the ring deep neural network, to generate respective simulated magnetic resonance images; inputting a first simulated full-sampled magnetic resonance image and the full-sampled magnetic resonance image to a pre-constructed first simulated magnetic resonance image class discrimination model, to obtain a first discrimination result indicating whether or not the first simulated full-sampled magnetic resonance image is of a simulated magnetic resonance image class; and adjusting a network parameter of the ring deep neural network based on a preset loss function, to obtain a trained magnetic resonance imaging model.

Methods and apparatuses for training a magnetic resonance imaging model, electronic devices and computer readable storage media are provided. A method may include: acquiring a magnetic resonance image data set; constructing a ring deep neural network to be trained; inputting an under-sampled magnetic resonance image and a full-sampled magnetic resonance image respectively to two neural networks included in the ring deep neural network, to generate respective simulated magnetic resonance images; inputting a first simulated full-sampled magnetic resonance image and the full-sampled magnetic resonance image to a pre-constructed first simulated magnetic resonance image class discrimination model, to obtain a first discrimination result indicating whether or not the first simulated full-sampled magnetic resonance image is of a simulated magnetic resonance image class; and adjusting a network parameter of the ring deep neural network based on a preset loss function, to obtain a trained magnetic resonance imaging model.

The present invention relates to an apparatus for task determination during brain activity analysis. The apparatus comprises an input unit (20), a processing unit (30), and an output unit (40). The input unit is configured to provide the processing unit with measurement data of the brain of a patient performing a task during brain activity analysis. The processing unit is configured to determine a measure of brain activity based on the measurement data of the brain. The processing unit is configured to determine: that the patient should perform a different task to the task they are currently performing and select the different task; or that the patient should continue performing the task that they are currently performing; or that the patient should stop performing the task; The determination comprises utilization of information relating to the task and the determined measure of brain activity and information relating to a plurality of reference tasks and associated plurality of reference measures of brain activity. The output unit is configured to output an indication that the patient should perform the different task, that the patient should continue performing the task, or that the patient should stop performing the task.

A method of analyzing thoracic insufficiency syndrome (TIS) in a subject by performing quantitative dynamic magnetic resonance imaging (QdMRI) analysis. The QdMRI analysis includes performing four-dimensional (4D) image construction of a TIS subject's thoracic cavity. The 4D image includes a sequence of two dimensional (2D) images of the TIS subject's thoracic cavity over a respiratory cycle of the TIS subject. The QdMRI analysis also includes segmenting a region of interest (ROI) within the 4D image, determining TIS measurements within the ROI, comparing the TIS measurements to normal measurements determined from ROIs in 4D images of the thoracic cavities of normal subjects that are not afflicted by TIS, and outputting quantitative markers indicating deviation of the thoracic cavity of the TIS subject relative to the thoracic cavities of the normal subjects.

Embodiments may provide self-guided, self-directed diagnostics and treatment of neural conditions. For example, a system may comprise a processor, memory accessible by the processor, and program instructions and data stored in the memory, a plurality of stimulation devices connected to signal output circuitry interfacing the processor with the stimulation devices, program instructions and data to control the stimulation devices to generate and transmit stimulation signals, a plurality of sensing devices connected to signal input circuitry interfacing the processor with the sensing devices, program instructions and data to receive sensed signals from the sensing devices, a communication device adapted to wirelessly communicate with a server computer system, and program instructions and data to perform dynamic closed loop feedback of the stimulation signals based on the received sensed signals to provide self-guided, self-directed diagnostics and treatment of neural conditions using at least one recipe for a treatment strategy guided by artificial intelligence.

A computer apparatus and methods generate multi-parametric color composite images from multi-echo chemical shift encoded (CSE) MRI. Some embodiments use inherently co-registered images (i.e., image maps) that are combined into a single intuitive composite color image. The color (e.g., brightness, hue, and/or saturation) reflects in part the water and fat content, and other properties, particularly T2* relaxation (related to magnetic susceptibility) of the tissue.