Abstract: A method for predicting clinical severity of a neurological disorder includes steps of: a) identifying, according to a magnetic resonance imaging (MRI) image of a brain, brain image regions each of which contains a respective portion of diffusion index values of a diffusion index, which results from image processing performed on the MRI image; b) for one of the brain image regions, calculating a characteristic parameter based on the respective portion of the diffusion index values; and c) calculating a severity score that represents the clinical severity of the neurological disorder of the brain based on the characteristic parameter of the one of the brain image regions via a prediction model associated with the neurological disorder.

Abstract: Disclosed herein is a method for diagnosing a neurological disorder based on at least one magnetic resonance imaging (MRI) image. The method includes identifying brain image regions that contain a respective portion of diffusion index values of at least one diffusion index. For each of the brain image regions, a characteristic parameter based on the respective portion of the diffusion index values is calculated. a diagnoses is then made for the brain using one of predetermined categories of the neurological disorder by performing classification on a combination of the characteristic parameters via a classifier.

Abstract: A method is to be implemented by a computing device and includes steps of: a) identifying, according to a magnetic resonance imaging (MRI) image, brain image regions each of which contains a respective portion of diffusion index values of at least one diffusion index, which results from image processing performed on said at least one MRI image; b) for each of the brain image regions, calculating a characteristic parameter based on the respective portion of the diffusion index values; and c) diagnosing the brain examined with one of predetermined categories of the neurological disorder by performing classification on a combination of the characteristic parameters via a classifier.

Abstract: A method for predicting clinical severity of a neurological disorder includes steps of: a) identifying, according to a magnetic resonance imaging (MRI) image of a brain, brain image regions each of which contains a respective portion of diffusion index values of a diffusion index, which results from image processing performed on the MRI image; b) for one of the brain image regions, calculating a characteristic parameter based on the respective portion of the diffusion index values; and c) calculating a severity score that represents the clinical severity of the neurological disorder of the brain based on the characteristic parameter of the one of the brain image regions via a prediction model associated with the neurological disorder.

Abstract: The present invention discloses a method for extracting AIF and an application thereof to DCE-MRI. The method comprises steps: contacting a target tissue with a contrast agent to obtain a plurality of images; using the plurality of images to work out the tissue concentration curve of the contrast agent in each voxel; calculating the purity of the tissue concentration curve of each voxel according to the tissue concentration curves; and extracting the voxel having the highest purity as the optimized arterial location; and extracting the tissue concentration curve of the voxel having the highest purity as the arterial input function. The extracted AIF is applied to a pharmacokinetic model to obtain associated pharmacokinetic parameters. The present invention not only improves the accuracy and reliability of the derived quantitative indexes but also promotes the efficiency of the quantitative analysis.

Abstract: The invention relates to the use of diffusion kurtosis imaging (DKI) in the diagnosis of Parkinson Disease related neurodegenerative disorders, including (but not limited to) Parkinson's disease (PD) and Parkinson plus syndromes.

Abstract: The present invention discloses a method for extracting AIF and an application thereof to DCE-MRI. The method comprises steps: contacting a target tissue with a contrast agent to obtain a plurality of images; using the plurality of images to work out the tissue concentration curve of the contrast agent in each voxel; calculating the purity of the tissue concentration curve of each voxel according to the tissue concentration curves; and extracting the voxel having the highest purity as the optimized arterial location; and extracting the tissue concentration curve of the voxel having the highest purity as the arterial input function. The extracted AIF is applied to a pharmacokinetic model to obtain associated pharmacokinetic parameters. The present invention not only improves the accuracy and reliability of the derived quantitative indexes but also promotes the efficiency of the quantitative analysis.

Abstract: The invention relates to the use of diffusion kurtosis imaging (DKI) in the diagnosis of Parkinson Disease related neurodegenerative disorders, including (but not limited to) Parkinson's disease (PD) and Parkinson plus syndromes.