A magnetic resonance imaging apparatus according to an embodiment includes a processing circuitry configured to generate a pulse sequence including a plurality of repetition times (TRs) each of which includes an echo train and a driven equilibrium pulse applied following the echo train, vary a flip angle of the driven equilibrium pulse, obtain magnetic resonance image data collected by executing the pulse sequence, and reconstruct a magnetic resonance image by using the magnetic resonance image data.

One or more tractograms of a global tractography of a tissue of interest are determined. At least one instance of diffusion magnetic resonance imaging data of the tissue of interest is obtained. A trained machine-learning algorithm generates the one or more tractograms based on the at least one instance of the diffusion magnetic resonance imaging data.

A method of quantitatively evaluating a cognitive status and its future change from a medical image of an individual's brain, the method comprising scanning the individual's brain with a scanning device so as to acquire at least one medical brain image; processing the medical brain image to obtain at least one feature of the image; using a pre-established prediction model to determine a condition of the cognitive status and predict its future change based on the at least one feature obtained.

An electromagnetic tomography system for gathering measurement data pertaining to a human head includes an image chamber unit, a control system, and a housing. The image chamber unit includes an antenna assembly defining a horizontally-oriented imaging chamber and including an array of antennas arranged around the imaging chamber. The antennas include at least some transmitting antennas and some receiving antennas. The control system causes the transmitting antennas to transmit a low power electromagnetic field that is received by the receiving antennas after passing through a patient's head in the imaging chamber. A data tensor is produced that may be inversed to reconstruct a 3D distribution of dielectric properties within the head and to create an image. The housing at least partially contains the antenna assembly and has a front entry opening into the imaging chamber. The head is inserted horizontally through the front entry opening and into the imaging chamber.

Machine-learning techniques are used to train a classifier to predict auditory and language skills improvement in a patient who is a candidate for cochlear implantation (CI). One or more images of portions of the patient's brain are obtained, and quantitative data is extracted that represents the composition of one or more brain areas related to auditory and/or cognitive processing. For training of the classifier, data is obtained for previous CI patients whose improvement in language skills has been measured. Once trained, the classifier can be used to predict a likely degree of improvement in a prospective CI patient's auditory and language skills.

Described herein are positron emission tomography (PET)-electron paramagnetic resonance imaging (EPRI) systems and methods of use. In one example, a PET-EPRI system includes a PET-EPR insert, a PET scanner including one or more solid-state photodetectors, and a subject module that can house a subject for scanning. The PET-EPR insert includes an EPR resonator that can nest inside the PET scanner. The EPR resonator includes a resonator that can receive the subject module, a shield encircling the resonator and one or more rapid scan coils (RS-coils) positioned around the shield. The shield can prevent electrical coupling between the RS-coils and the resonator while being transparent to annihilation photons and magnetic field scans.

A system and method for quantifying Alzheimer's disease (AD) risk using one or more interferometric micro-Doppler radars (IMDRs) and deep learning artificial intelligence to distinguish between cognitively unimpaired individuals and persons with AD based on gait analysis. The system utilizes IMDR to capture signals from both radial and transversal movement in three-dimensional space to further increase the accuracy for human gait estimation. New deep learning technologies are designed to complement traditional machine learning involving separate feature extraction followed-up with classification to process radar signature from different views including side, front, depth, limbs, and whole body where some motion patterns are not easily describable. The disclosed cross-talk deep model is the first to apply deep learning to learn IMDR signatures from two perpendicular directions jointly from both healthy and unhealthy individuals. Decision fusion is used to integrate classification results from feature-based classifier and deep learning AI to reach optimal decision.

Disclosed herein are methods for imaging a tissue in a subject that involves administering to the subject a composition comprising deuterium-labeled glycolytic or fatty acid substrate and imaging the subject with deuterium magnetic resonance imaging (DMI) to detect hydrogen-deuterium oxide (HDO) in tissues of the subject. The disclosed methods can be used to detect changes in metabolic activity in a tissue. The disclosed methods can also be used to detect cancers.

Arrangements described herein relate to a headset. The headset includes a device. The device includes a transducer configured to interact with a head of a subject. The headset further includes a manually-operated registration system configured to delineate a workspace of the transducer at the head of the subject.

A magnetic resonance imaging system including a memory configured to store machine executable instructions, pulse sequence commands, and a first machine learning model including a first deep learning network. The pulse sequence commands are configured for controlling the magnetic resonance imaging system to acquire a set of magnetic resonance imaging data. The first machine learning model includes a first input and a first output, a processor, wherein execution of the machine executable instructions causes the processor to control the magnetic resonance imaging system to repeatedly perform an acquisition and analysis process including: acquiring a dataset including a subset of the set of magnetic resonance imaging data from an imaging zone of the magnetic resonance imaging system according to the pulse sequence commands, providing the dataset to the first input of the first machine learning model, in response to the providing, receiving a prediction of a motion artifact level of the acquired magnetic resonance imaging data from the first output of the first machine learning model, the motion artifact level characterizing a number and/or extent of motion artifacts present in the acquired magnetic resonance imaging data.