Abstract: The invention discloses a kind of micrometer-scale porous sodium ferrous sulfate/carbon composite cathode materials and sodium-ion or sodium batteries with it. The materials are prepared by a co-precipitation and solid-phase calcination method, and/or involve the metal-doped elements. The materials feature a porous structure with a three-dimensional conductive network, and have a size distribution (2-30 ?m) of particles, consisting of a close stack of primary particles of 80-200 nm tightly covered by an amorphous carbon. Micrometer-scale cathode materials deliver a high vibration density, which helps to improve the volumetric energy density of batteries. As a cathode for sodium-ion or sodium batteries, the materials have the advantages of abundant raw materials, low cost, high operating voltage, good rate performance and cycling stability, and simple preparation process. The assembled sodium-ion or sodium batteries show a broad market application prospect with the advantage of high energy density.

Abstract: A magnetic resonance fingerprint (MRF) method includes obtaining an MRF undersampled image, and determining a corresponding radiofrequency transmit magnetic field strength for a pixel of the image. The method also includes obtaining a tissue property from an MRF dictionary based on the pixel, and correcting the tissue property based on the determined radiofrequency transmit magnetic field strength. According to another aspect, a MRF method includes generating a full MRF dictionary based on a nominal radiofrequency transmit magnetic field strength and, for each of a plurality of radiofrequency transmit magnetic field strengths different than the nominal field strength, subsampling the full MRF dictionary and generating a corrected MRF dictionary based on the subsampled dictionary and the different field strengths. The method also includes determining a transformation matrix between the full MRF dictionary and each of the corrected MRF dictionaries.

Abstract: A muscle segmentation method and system utilizes a trained machine learning system to identify and segment skeletal muscle from input magnetic resonance images of the shoulder. Properties of the muscle can be determined from the segmentation, and clinical treatment decisions can be more accurately made based on the determined properties.

Abstract: A method for creating a dictionary for a magnetic resonance fingerprinting (MRF) reconstruction includes training a semi-supervised learning system based on at least a set of MRF data and a set of control variables and generating a plurality of signal evolutions using the trained semi-supervised learning system. The method also includes generating an MRF dictionary using the plurality of signal evolutions generated using the trained semi-supervised learning system and storing the MRF dictionary in a memory. In one embodiment, the semi-supervised learning system is a MRF generative adversarial network (GAN).

Abstract: A system and method is provided for generating a map of a tissue property in a subject using magnetic resonance fingerprinting (MRF) and a compressed MRF dictionary, where the compressed MRF dictionary has a significantly reduced memory requirement relative to a standard MRF dictionary. The method includes performing a randomized singular value decomposition (rSVD) on a MRF dictionary to produce the compressed MRF dictionary. MRF data is then acquired and compared to the MRF dictionary to identify the tissue property from the region of interest in the subject. A tissue property map is then generated based on the tissue in the region of interest of the subject.

Abstract: A method for creating a dictionary for a magnetic resonance fingerprinting (MRF) reconstruction includes training a semi-supervised learning system based on at least a set of MRF data and a set of control variables and generating a plurality of signal evolutions using the trained semi-supervised learning system. The method also includes generating an MRF dictionary using the plurality of signal evolutions generated using the trained semi-supervised learning system and storing the MRF dictionary in a memory. In one embodiment, the semi-supervised learning system is a MRF generative adversarial network (GAN).

Abstract: Apparatus for hyperspectral imaging, the apparatus including input optics that receive radiation reflected or radiated from a scene, a spatial modulator that spatially samples radiation received from the input optics to generate spatially sampled radiation, a spectral modulator that spectrally samples the spatially sampled radiation received from the spatial modulator to generate spectrally sampled radiation, a sensor that senses spectrally sampled radiation received from the spectral modulator and generates a corresponding output signal and at least one electronic processing device that controls the spatial and spectral modulators to cause spatial and spectral sampling to be performed, receives output signals and processes the output signals in accordance with performed spatial and spectral sampling to generate a hyperspectral image.

Abstract: A system and method is provided for generating a map of a tissue property in a subject using magnetic resonance fingerprinting (MRF) and a compressed MRF dictionary, where the compressed MRF dictionary has a significantly reduced memory requirement relative to a standard MRF dictionary. The method includes performing a randomized singular value decomposition (rSVD) on a MRF dictionary to produce the compressed MRF dictionary. MRF data is then acquired and compared to the MRF dictionary to identify the tissue property from the region of interest in the subject. A tissue property map is then generated based on the tissue in the region of interest of the subject.

Abstract: Apparatus for hyperspectral imaging, the apparatus including input optics that receive radiation reflected or radiated from a scene, a spatial modulator that spatially samples radiation received from the input optics to generate spatially sampled radiation, a spectral modulator that spectrally samples the spatially sampled radiation received from the spatial modulator to generate spectrally sampled radiation, a sensor that senses spectrally sampled radiation received from the spectral modulator and generates a corresponding output signal and at least one electronic processing device that controls the spatial and spectral modulators to cause spatial and spectral sampling to be performed, receives output signals and processes the output signals in accordance with performed spatial and spectral sampling to generate a hyperspectral image.