Abstract: A modulation device includes a substrate, an electrostatic discharge protection element, an electronic element, and a driving element. The substrate has an active region. The electrostatic discharge protection element is arranged around the active region. The electronic element is disposed in the active region. The driving element is electrically connected to the electronic element.

Abstract: A method of predicting therapeutic effects of a treatment for cognitive impairment for an individual using the individual's predicted age difference (PAD), the method comprising scanning the individual's brain with a scanning device so as to acquire at least one medical brain image at the beginning of the treatment; processing the medical brain image to obtain at least one feature of the image; generating a PAD value of the individual based on the at least one feature of the image; comparing the PAD value with a reference value; and predicting the therapeutic effects of the treatment for cognitive impairment using the comparison result of the PAD value and the reference value.

Abstract: A method of predicting therapeutic effects of a treatment for cognitive impairment for an individual using the individual's predicted age difference (PAD), the method comprising scanning the individual's brain with a scanning device so as to acquire at least one medical brain image at the beginning of the treatment; processing the medical brain image to obtain at least one feature of the image; generating a PAD value of the individual based on the at least one feature of the image; comparing the PAD value with a reference value; and predicting the therapeutic effects of the treatment for cognitive impairment using the comparison result of the PAD value and the reference value.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The invention provides a method of automatically calculating a linking strength of brain fiber tracts. The method includes the steps as follows: providing a brain reference template with a plurality of reference fiber bundles; providing an object image with an image information; co-registering the image information according to the brain reference template, deforming and mapping the reference fiber bundles on the object image, so as to make the object image have a plurality of clear object fiber bundles; defining a plurality of regions of interest (ROIs) from the object image, analyzing and calculating a number and a length of the object fiber bundles between each two ROIs, and obtaining a plurality of mean object information from connections between the ROIs; dividing the number by the length, and then multiplying a value above by the mean object information to generate a plurality of link strength values of connections between the ROIs used to be made as a matrix image.

Abstract: A transformation method for diffusion spectrum imaging includes: receiving an original DSI dataset and a template DSI dataset; computing an energy function; computing, for each time point, first-order and second-order derivatives of the energy function with respect to velocity fields in an image space and in a q-space; computing, for each time point, the velocity fields in the image space and in the q-space based upon the first-order and second-order derivatives; performing integration on the velocity fields over time to obtain a deformation field; and generating a transformed DSI dataset according to the deformation field.

Abstract: The present invention provides a method of automatically analyzing brain fiber tracts information. The method includes the steps as follows: providing a brain reference template with a plurality of reference fiber tracts, wherein the reference fiber tracts have at least a coordinate information; providing an object image with a plurality of object fiber tracts, and each object fiber tract includes at least an object information; transforming the object image according to the brain reference template; sampling the transformed object image with the brain reference template to make the object information co-register with the coordinate information, and further to get an analyzing result of each object information.

Abstract: A voxel-based transformation method includes: a) obtaining a MRI dataset in a subject space associated with subject voxel coordinates, subject sampling directions, and subject voxel spin amounts, and a dataset of a co-registration template associated with template voxel coordinates, each subject voxel coordinate corresponding to a template voxel coordinate according to a mapping function; b) through an inverse of the mapping function, obtaining subject voxel coordinates and a Jacobian matrix; and c) obtaining template voxel spin amounts, each being a function of a template sampling direction and a template voxel coordinate, using the Jacobian matrix and image data.

Abstract: A transformation method for diffusion spectrum imaging includes: receiving an original DSI dataset and a template DSI dataset; computing an energy function; computing, for each time point, first-order and second-order derivatives of the energy function with respect to velocity fields in an image space and in a q-space; computing, for each time point, the velocity fields in the image space and in the q-space based upon the first-order and second-order derivatives; performing integration on the velocity fields over time to obtain a deformation field; and generating a transformed DSI dataset according to the deformation field.

Abstract: A method is adapted for providing microstructural information of a biological target from a plurality of diffusion weighted MR images corresponding to a specific area of the biological target. Each of the diffusion weighted MR images is obtained using a respective q-space sampling vector and is sampled at a plurality of sample points thereof to obtain a group of diffusion weighted MR image data. The diffusion weighted MR image data are processed to obtain a spin distribution function from which the microstructural information of the biological target can be obtained.

Abstract: A voxel-based transformation method includes: a) obtaining a MRI dataset in a subject space associated with subject voxel coordinates, subject sampling directions, and subject voxel spin amounts, and a dataset of a co-registration template associated with template voxel coordinates, each subject voxel coordinate corresponding to a template voxel coordinate according to a mapping function; b) through an inverse of the mapping function, obtaining subject voxel coordinates and a Jacobian matrix; and c) obtaining template voxel spin amounts, each being a function of a template sampling direction and a template voxel coordinate, using the Jacobian matrix and image data.

Abstract: A method of analyzing a diffusion-weighted magnetic resonance image is adapted to analyze an orientation distribution function ?0 of nerve fibers at a vector in space. The method includes the following steps: (A) finding an orientation {circumflex over (?)}a with the largest probability in the ODF ?0; (B) maximizing a single fiber ODF ??; (C) analyzing the maximized single fiber ODF ?? to obtain quantitative information; (D) deducting the maximized single fiber ODF ?? obtained in step (B) from the entire ODF ?0; and (E) stopping computation if the result obtained in step (D) is determined to be smaller than a predetermined value, and repeating steps (A), (B), (C), and (D) if otherwise.

Abstract: A method is adapted for providing microstructural information of a biological target from a plurality of diffusion weighted MR images corresponding to a specific area of the biological target. Each of the diffusion weighted MR images is obtained using a respective q-space sampling vector and is sampled at a plurality of sample points thereof to obtain a group of diffusion weighted MR image data. The diffusion weighted MR image data are processed to obtain a spin distribution function from which the microstructural information of the biological target can be obtained.

Abstract: A method of analyzing a diffusion-weighted magnetic resonance image is adapted to analyze an orientation distribution function ?0 of nerve fibers at a vector in space. The method includes the following steps: (A) finding an orientation {circumflex over (?)}a with the largest probability in the ODF ?0; (B) maximizing a single fiber ODF ??; (C) analyzing the maximized single fiber ODF ?? to obtain quantitative information; (D) deducting the maximized single fiber ODF ?? obtained in step (B) from the entire ODF ?0; and (E) stopping computation if the result obtained in step (D) is determined to be smaller than a predetermined value, and repeating steps (A), (B), (C), and (D) if otherwise.