Abstract: The present disclosure provides a magnetic resonance (MR) image analysis method for a patient who underwent radiotherapy. The method includes the steps: receiving an MR image set of a patient and a dose map of a radiotherapy plan; converting the dose intensity distribution of the dose map into the relative spatial positions in the MR image set; selecting a radiation dose and a radiation exposure region, wherein the radiation exposure region has radiation intensity being equal to or higher than the radiation dose; using the radiation exposure region to determine a region of interest (ROI) in the MR image set; classifying the voxels inside the ROI of the MR image set into different clusters according to the grayscale values of the voxels inside the ROI; and calculating the volume or ratios of the different clusters inside the ROI. The present disclosure also provides a method for evaluating risks of radiotherapy.

Abstract: A method comprises steps of capturing first images for displaying a three-dimensional brain structure, capturing second images for displaying blood vessels of the brain and capturing third images displaying implanted electrodes in the brain. These second images are set as standards to respectively adjust and align the first images and the third images. Contrast of the blood vessels is enhanced and the blood vessels are colored with a first color. Contrast of the electrodes is enhanced and the electrodes are colored with a second color. The aligned first images, the colored second images and the colored third images are integrated to obtain integrated viewable information of a brain, (intracranial) electrodes and blood vessels for medical reference. So, spatial positions of implanted electrodes and blood vessels of brains can be confirmed before conducting a medical surgery. And, the physicians can avoid bleeding problem due to accidental touch on blood vessels.

Abstract: The present disclosure provides a magnetic resonance (MR) image analysis method for a patient who underwent radiotherapy. The method includes the steps: receiving an MR image set of a patient and a dose map of a radiotherapy plan; converting the dose intensity distribution of the dose map into the relative spatial positions in the MR image set; selecting a radiation dose and a radiation exposure region, wherein the radiation exposure region has radiation intensity being equal to or higher than the radiation dose; using the radiation exposure region to determine a region of interest (ROI) in the MR image set; classifying the voxels inside the ROI of the MR image set into different clusters according to the grayscale values of the voxels inside the ROI; and calculating the volume or ratios of the different clusters inside the ROI. The present disclosure also provides a method for evaluating risks of radiotherapy.

Abstract: Many electrodes are positioned inside a skull for obtaining several electric voltage variation wave information. A processing unit computes subtract operation between two neighboring electrical voltage variation wave information respectively and then can obtain electrical voltage difference waves. Furthermore, each electrical voltage difference wave is separated into a first band wave signal and a second band wave signal. After which, several first band wave envelope signals and second band wave envelope signals are obtained. It conducts a co-relation processing about the first band wave envelope signals and the second band wave envelope signals. So, a co-relation table is obtained for determining a position where an intracranial brain wave abnormality occurs. In this invention, a unique scientific approach to determine the spatial position of seizure onset occurred inside the brain. In addition, the position of intracranial brain wave abnormality can be concretized and visualized by the co-relation table.

Abstract: An electrode module is positioned inside an intracranial portion of a human head. Then, it captures brain images of the human head so multiples two dimensional (2D) cross-sectional images are obtained. The electrodes can be seen in one or more 2D cross-sectional images. A brain functional map adjusting portion is provided to obtain the 2D cross-sectional images and then to conduct a proportional deformation process for the images in the brain functional map database. By combining the processed images in the brain functional map database and the 2D cross-sectional images, multiple combined cross-sectional images can be obtained for display. So, the effects of intracranial electrodes are better than the traditional way. In addition, the brain structure information of a patient contains the precise positions of the electrodes and the corresponding brain functional areas.

Abstract: A magnetic resonance imaging white matter hyperintensities region recognizing method and system are disclosed herein. The white matter hyperintensities region recognizing method includes receiving and storing a FLAIR MRI image, a spin-lattice relaxation time weighted MRI image, and a diffusion weighted MRI image. Registration and fusion are preformed, and a white matter mask is determined. An intersection image of the FLAIR MRI image and the white matter mask is taken, a first region is determined after normalizing the intersection image, a cerebral infarct region is removed from the first image through the diffusion weighted MRI image, and then a determination is made as to whether to remove a remaining region in order to form a white matter hyperintensities region in the FLAIR MRI image.

Abstract: A method for detecting a cerebral infarct includes receiving an image of a brain of a subject from a magnetic resonance imaging scanner, wherein the image has a plurality of voxels, and each of the voxels has a voxel intensity. Then, the voxel intensities are normalized, wherein the normalized voxel intensities have a distribution peak, and the normalized voxel intensity of the distribution peak is Ipeak. A threshold is determined, which is the Ipeak+ a value. Voxel having the normalized voxel intensity larger than the threshold is selected, wherein the selected voxel is the cerebral infarct. A method for quantifying the cerebral infarct is also provided.

Abstract: A magnetic resonance imaging white matter hyperintensities region recognizing method and system are disclosed herein. The white matter hyperintensities region recognizing method includes receiving and storing a FLAIR MRI image, a spin-lattice relaxation time weighted MRI image, and a diffusion weighted MRI image. Registration and fusion are preformed, and a white matter mask is determined. An intersection image of the FLAIR MRI image and the white matter mask is taken, a first region is determined after normalizing the intersection image, a cerebral infarct region is removed from the first image through the diffusion weighted MRI image, and then a determination is made as to whether to remove a remaining region in order to form a white matter hyperintensities region in the FLAIR MRI image.

Abstract: A method for detecting a cerebral infarct includes receiving an image of a brain of a subject from a magnetic resonance imaging scanner, wherein the image has a plurality of voxels, and each of the voxels has a voxel intensity. Then, the voxel intensities are normalized, wherein the normalized voxel intensities have a distribution peak, and the normalized voxel intensity of the distribution peak is Ipeak. A threshold is determined, which is the Ipeak+ a value. Voxel having the normalized voxel intensity larger than the threshold is selected, wherein the selected voxel is the cerebral infarct. A method for quantifying the cerebral infarct is also provided.

Abstract: An endoscopic navigation method includes the steps of: receiving an image from an endoscopic navigation system; performing image classification to determined whether the image is usable; performing a first image process on the image to filter out dark areas of the image to produce a first processed image; performing a first determination procedure to identify the dark areas of the first processed image; producing a first result image for indicating lumen direction; performing a second image process to filter out fold curves of the image to produce a second processed image when there is no dark area in the image; performing a second determination procedure to identify the fold curves of the second processed image; producing a second result image for indicating lumen direction according to the fold curves; and outputting the first or the second result image to a display device to assist users in operating the photographic device.

Abstract: An endoscopic navigation method includes the steps of: receiving an image from an endoscopic navigation system; performing image classification to determined whether the image is usable; performing a first image process on the image to filter out dark areas of the image to produce a first processed image; performing a first determination procedure to identify the dark areas of the first processed image; producing a first result image for indicating lumen direction; performing a second image process to filter out fold curves of the image to produce a second processed image when there is no dark area in the image; performing a second determination procedure to identify the fold curves of the second processed image; producing a second result image for indicating lumen direction according to the fold curves; and outputting the first or the second result image to a display device to assist users in operating the photographic device.