4 results about "Ultra low field" patented technology

A system of field cycled magnetic resonance system and a method of operating a field cycled magnetic resonance system are described. In accordance with various embodiments, the disclosed system includes a static field magnet, wherein the magnet is configured to provide a low static external magnetic field to a given field of view, a radio frequency coil, and a field cycling magnet. In accordance with various embodiments, the method includes providing a static field magnet configured to image a tissue sample within a given field of view, applying a low static external magnetic field to the given field of view, providing a radio frequency coil configured to produce cycling radio frequency field, providing a field cycling magnet, altering the low static external magnetic field within the given field of view, and collecting images from the system.

The application relates to an ultra-low-field magnetic resonance imaging noise suppression method and an ultra-low-field magnetic resonance imaging system. The method comprises the following steps: acquiring original magnetic resonance signals of a main imaging signal channel and reference noise signals of a plurality of reference channels; dividing time periods according to a scanning sequence and generating synchronization control signals corresponding to different time periods; in response to a first synchronization control signal, entering a training mode in a non-signal acquisition window of the scanning sequence, wherein the training mode comprises updating weight coefficients of an adaptive filter set based on the reference noise signals and the original magnetic resonance signals; in response to a second synchronization control signal, entering a scanning mode in a signal acquisition window of the scanning sequence, wherein the scanning mode comprises freezing or reducing the update speed of the weight coefficients of the adaptive filter set, and using the updated weight coefficients to perform noise cancellation on the original magnetic resonance signals and output purified magnetic resonance signals. The image signal-to-noise ratio of the ultra-low-field magnetic resonance imaging is improved.

The present disclosure provides systems and methods for deep learning super-resolution training and / or image generation for low and ultra-low field magnetic resonance imaging. In some aspects, a method includes obtaining a first image of a brain with a low field strength magnetic resonance imaging system. The first image has a first resolution. The method further includes obtaining a deep learning brain model based on a high field strength image. The deep learning brain model can be configured to be applied by a neural network comprising a plurality of layers. The method further includes applying the deep learning brain model to the first image to generate a second image of the brain. The second image has a second resolution, and the second resolution is greater than the first resolution.

The present application relates to the technical field of ultra-low field magnetic resonance system, and particularly relates to a battery state of charge detection method and system based on closed-loop adaptive ultra-low field MRI, comprising: collecting the phase change of water molecule H signal caused by the magnetic field disturbance of the battery to be detected to reconstruct a first signal phase image; extracting a high-information-area mask, then resampling to obtain a second signal phase image; inputting the second signal phase image into a state of charge detection model to predict the state of charge value of the battery to be detected. The battery magnetic resonance detection scheme in the prior art needs to use a high-field magnetic resonance system, which is not conducive to the use of the production line. The detection equipment is replaced with an ultra-low field magnetic resonance system, so that the shielding room and other structures in the traditional magnetic resonance system are omitted, and the imaging accuracy is improved by first locating the high-information-area and then resampling the area, thereby improving the accuracy of the detection model in estimating the state of charge.