Abstract: Methods and apparatus for MRI reconstruction and data acquisition are provided. The method for MRI reconstruction includes: obtaining MRI images and k-space training data and dividing into anatomical sections; training reconstruction models to predict MRI images from k-space data for individual anatomical sections; while scanning an object, identifying the anatomical sections by scout scans or navigator signals; selecting suitable reconstruction models; reconstructing anatomical sections using the selected models, and merging the images from anatomical sections. Reconstructed images obtained by the above methods and apparatus have better image quality such as lesser noise and artifacts, and less MRI data is needed for the same image quality.

Abstract: A system and method to detect abnormality of subjects directly from MRI k-space data are provided. The system includes: at least one computer hardware processor, at least one non-transitory computer-readable storage medium, and at least one computer program stored in the at least one non-transitory computer-readable storage medium and executable on the at least one computer hardware processor, wherein the at least one computer program includes: an acquisition module, configured to obtain target MRI k-space data by scanning a subject, wherein the target MRI k-space data are fully-sampled or undersampled or sparse MRI k-space data; a detection module, configured to obtain and output detection outcome from the target MRI k-space data using detection models; and a model training module, configured to train the detection models based on training data. Hence, the MRI scan time and related cost are reduced, and the accuracy of the detecting results is increased.

Abstract: A magnetic resonance imaging (MRI) system with low-power miniaturization, a power supply system, and a power management system are provided. The MRI system includes: a permanent magnet using samarium-cobalt material, an MR console, a transmission RF chain, a receiving RF chain, gradient coils, gradient amplifiers, and a terminal device for user interactions. Each of above systems is a ultra-light and ultra-low-power ultra-low field brain MRI system for highly accessible healthcare applications.

Abstract: A computer-implemented magnetic resonance imaging method for subject with a metal implant, including steps of: S1: building an ultra-low field MR system with a field strength ranged from 10 to 100 mT; S2: setting sequences with optimized parameters of the ultra-low field MR system for minimizing the sensitivity to field distortion and improving SNR; S3: magnetic resonance imaging with the ultra-low field MR system using the sequences for the subject with a metal implant to obtain MR images; and S4: improving SNR of obtained MR images by deep learning based image reconstruction.

Abstract: A conduction-cooled radiofrequency coil subsystem of MRI system with high signal-to-noise ratio imaging capability at low field and/or ultra-low field. The conduction-cooled RF coil subsystem includes a radiofrequency (RF) coil module having at least one RF instrumentation; a cryocooler; and a thermal conduction line thermally connected between the cryocooler and the RF instrumentation. The RF coil module further includes a housing defining a thermally insulated vessel for accommodating the RF instrumentation. The thermal conduction line is thermally coupled to the cryocooler which is located outside the housing of the RF coil module and the RF instrumentation in the thermally insulated vessel to conduction cool the RF instrumentation. The at least one RF instrumentation includes one or more of an RF transceiver coil, an RF receiver coil, an RF preamplifier and an RF electronics module.

Abstract: Methods and apparatus for MRI reconstruction and data acquisition are provided. The method for MRI reconstruction includes: obtaining MRI images and k-space training data and dividing into anatomical sections; training reconstruction models to predict MRI images from k-space data for individual anatomical sections; while scanning an object, identifying the anatomical sections by scout scans or navigator signals; selecting suitable reconstruction models; reconstructing anatomical sections using the selected models, and merging the images from anatomical sections. Reconstructed images obtained by the above methods and apparatus have better image quality such as lesser noise and artifacts, and less MRI data is needed for the same image quality.

Abstract: A computer-implemented method for reducing or eliminating artifacts in MRI, includes steps of: S1: acquiring a plurality of MR signals mixed with EMI with different weightings from a plurality of receiver elements of at least one array coil; S2: obtaining EMI eliminated MR signals for each receiver element based on the MR signals mixed with EMI obtained in step S1; and S3: obtaining MR image based on the EMI eliminated MR signals obtained in step S2.

Abstract: A computer-implemented magnetic resonance imaging method for subject with a metal implant, including steps of: S1: building an ultra-low field MR system with a field strength ranged from 10 to 100 mT; S2: setting sequences with optimized parameters of the ultra-low field MR system for minimizing the sensitivity to field distortion and improving SNR; S3: magnetic resonance imaging with the ultra-low field MR system using the sequences for the subject with a metal implant to obtain MR images; and S4: improving SNR of obtained MR images by deep learning based image reconstruction.

Abstract: A magnetic resonance imaging (MRI) system with low-power miniaturization, a power supply system, and a power management system are provided. The MRI system includes: a permanent magnet using samarium-cobalt material, an MR console, a transmission RF chain, a receiving RF chain, gradient coils, gradient amplifiers, and a terminal device for user interactions. Each of above systems is a ultra-light and ultra-low-power ultra-low field brain MRI system for highly accessible healthcare applications.

Abstract: A system and method to detect abnormality of subjects directly from MRI k-space data are provided. The system includes: at least one computer hardware processor, at least one non-transitory computer-readable storage medium, and at least one computer program stored in the at least one non-transitory computer-readable storage medium and executable on the at least one computer hardware processor, wherein the at least one computer program includes: an acquisition module, configured to obtain target MRI k-space data by scanning a subject, wherein the target MRI k-space data are fully-sampled or undersampled or sparse MRI k-space data; a detection module, configured to obtain and output detection outcome from the target MRI k-space data using detection models; and a model training module, configured to train the detection models based on training data. Hence, the MRI scan time and related cost are reduced, and the accuracy of the detecting results is increased.

Abstract: A conduction-cooled radiofrequency coil subsystem of MRI system with high signal-to-noise ratio imaging capability at low field and/or ultra-low field. The conduction-cooled RF coil subsystem includes a radiofrequency (RF) coil module having at least one RF instrumentation; a cryocooler; and a thermal conduction line thermally connected between the cryocooler and the RF instrumentation. The RF coil module further includes a housing defining a thermally insulated vessel for accommodating the RF instrumentation. The thermal conduction line is thermally coupled to the cryocooler which is located outside the housing of the RF coil module and the RF instrumentation in the thermally insulated vessel to conduction cool the RF instrumentation. The at least one RF instrumentation includes one or more of an RF transceiver coil, an RF receiver coil, an RF preamplifier and an RF electronics module.