A method of quantitatively evaluating a cognitive impairment 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 impairment and predict its future change based on the at least one feature obtained.

A method of quantitatively evaluating a cognitive impairment 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 impairment and predict its future change based on the at least one feature obtained.

A magnetic resonance (MR)-radiotherapy (RT) hybrid system for treating a patient is disclosed. The MR-RT hybrid system comprises: an MR imaging (MRI) apparatus comprising bi-planar magnets configured to generate a magnetic field; a radiation source configured to supply a radiation beam to treat the patient; a gantry configured to couple the MR apparatus at a first end and the radiation source so that they can rotate in unison; a treatment support configured to support the patient; a motor configured to move the treatment support; and a controller. The controller comprises a processor and memory having stored thereon instructions, which when executed by the processor, cause the motor to move the treatment support in order to avoid collision between the MRI apparatus and the patient when the MRI apparatus is rotated. A method for positioning the treatment support within the MR-RT hybrid system is also disclosed.

A magnetic resonance (MR)-radiotherapy (RT) hybrid system for treating a patient is disclosed. The MR-RT hybrid system comprises: an MR imaging (MRI) apparatus comprising bi-planar magnets configured to generate a magnetic field; a radiation source configured to supply a radiation beam to treat the patient; a gantry configured to couple the MR apparatus at a first end and the radiation source so that they can rotate in unison; a treatment support configured to support the patient; a motor configured to move the treatment support; and a controller. The controller comprises a processor and memory having stored thereon instructions, which when executed by the processor, cause the motor to move the treatment support in order to avoid collision between the MRI apparatus and the patient when the MRI apparatus is rotated. A method for positioning the treatment support within the MR-RT hybrid system is also disclosed.

The present disclosure provides an operating method of a dopamine transporter check system, and the operation method includes steps as follows. A scan image of a subject's brain is obtained from a scan machine, and the scan image is a three-dimensional image. The scan image is aligned to a standard brain space to obtain a standardized scan image. Intensity normalization is performed on the standardized scan image. The standardized scan image after the intensity normalization is converted into a two-dimensional image. A plurality of image data are got from at least one region of interest in the two-dimensional image, and the at least one region of interest includes a left caudate, a left putamen, a right caudate and a right putamen. A dopamine neuron loss degree measurement and evaluation model based on the image data is established through a transfer learning.

The present disclosure provides an operating method of a dopamine transporter check system, and the operation method includes steps as follows. A scan image of a subject's brain is obtained from a scan machine, and the scan image is a three-dimensional image. The scan image is aligned to a standard brain space to obtain a standardized scan image. Intensity normalization is performed on the standardized scan image. The standardized scan image after the intensity normalization is converted into a two-dimensional image. A plurality of image data are got from at least one region of interest in the two-dimensional image, and the at least one region of interest includes a left caudate, a left putamen, a right caudate and a right putamen. A dopamine neuron loss degree measurement and evaluation model based on the image data is established through a transfer learning.

A method of passively detecting radiofrequency (RF) signals spontaneously emitted by a non-equilibrium population of electrons that are spin polarized by flowing through a chiral media during relaxation of the spin polarized electrons to equilibrium at a frequency corresponding to a Zeeman spin-flip energy of the spin polarized electrons under influence of a magnetic field (MF). The MF is applied to the chiral media for a predefined time period to shift a frequency and magnitude of the spontaneously emitted RF signals in line with Zeeman effect. The shifted emitted RF signals is passively detected and stored for medical use applications using a receiver antenna tuned to a resonant frequency of the shifted emitted RF signals.

A method of passively detecting radiofrequency (RF) signals spontaneously emitted by a non-equilibrium population of electrons that are spin polarized by flowing through a chiral media during relaxation of the spin polarized electrons to equilibrium at a frequency corresponding to a Zeeman spin-flip energy of the spin polarized electrons under influence of a magnetic field (MF). The MF is applied to the chiral media for a predefined time period to shift a frequency and magnitude of the spontaneously emitted RF signals in line with Zeeman effect. The shifted emitted RF signals is passively detected and stored for medical use applications using a receiver antenna tuned to a resonant frequency of the shifted emitted RF signals.

Systems and methods are provided for performing automated reconstruction of a dynamic MRI dataset that is acquired without a fixed temporal resolution. On one or more image quality metrics (IQMs) are obtained by processing a subset of the acquired dataset. In one example implementation, at each stage of an iterative process, one or more IQMs of the image subset is computed, and the parameters controlling the reconstruction and/or the strategy for data combination are adjusted to provide an improved or optimal image reconstruction. Once the IQM of the image subset satisfies acceptance criteria based on an estimate of the overall temporal fidelity of the reconstruction, the full reconstruction can be performed, and the estimate of the overall temporal fidelity can be reported based on the IQM at the final iteration.

Systems and methods are provided for performing automated reconstruction of a dynamic MRI dataset that is acquired without a fixed temporal resolution. On one or more image quality metrics (IQMs) are obtained by processing a subset of the acquired dataset. In one example implementation, at each stage of an iterative process, one or more IQMs of the image subset is computed, and the parameters controlling the reconstruction and/or the strategy for data combination are adjusted to provide an improved or optimal image reconstruction. Once the IQM of the image subset satisfies acceptance criteria based on an estimate of the overall temporal fidelity of the reconstruction, the full reconstruction can be performed, and the estimate of the overall temporal fidelity can be reported based on the IQM at the final iteration.