Taipei Medical University

Taipei Veterans General Hospital

Other

National Central University

National Yang Ming Chiao Tung University

Kaohsiung Chang Gung Memorial Hospital

National Yunlin University of Science and Technology

Syu-Jyun Peng

Cheng-Chia Lee

Jang-Zern Tsai

Yu-Wei Chen

Kuo-Wei Wang

Huai-Che Yang

Hsiang-Yu Yu

Chien-Chen Chou

Yeh-Lin Kuo

Yen-Cheng Shih

Tse-Hao Lee

Jong-Ling Fuh

Tsung-Chun Lee

Hsiu-Po Wang

Hsin-Yung Chen

Chih-Ying Huang

Ya-Ju Tsai

Hsu-Huai Chiu

Fu-Yuan Shih

Hsiu-Mei Wu

Pi-Lien Hung

Jing-Yu Yang

Meng-Zong Tsai

Hsuan-Ting Chang

An operation method of a brain amyloid PET processing system includes steps as follows. The whole brain white matter amyloid PET image is extracted from the smoothed amyloid PET image in the range of the whole brain white matter mask of the normalized brain space, and the uptake value with the preset maximum ratio in the whole brain white matter amyloid PET image is calculated; in the smoothed amyloid PET image of the normalized brain space, one or more voxels in the range of the whole brain gray matter mask are marked and counted, in which each voxel uptake value of the one or more voxels is greater than the uptake value of the preset maximum ratio of the whole brain white matter amyloid PET image, and the one or more voxels are used for interpretation training and test of the classification of the machine learning.

Brain amyloid PET processing system and operation method thereof and non-transitory computer readable medium

An operating method of a ketogenic dietary evaluation system includes steps as follows. The electroencephalogram data of a responder group and the electroencephalogram data of a non-responder group are preloaded, in which each electroencephalogram datum includes electroencephalograms of channels. The electroencephalograms of the channels are preprocessed to obtain the preprocessed electroencephalograms of the channels. A connectivity matrix is obtained on a basis of the phase synchronization between each two of the preprocessed electroencephalograms of the channels. The connectivity matrix is sampled and analyzed through different frequency bands and different proportion threshold values to obtain graphical parameters. A predictive model is established on a basis of a reduction rate of a predetermined event of the responder group, a reduction rate of the predetermined event of the non-responder group and the parameters.

Ketogenic dietary evaluation system and operation method thereof

The present disclosure provides an operation method of a PET (positron emission tomography) quantitative localization system, which includes steps as follows. The PET image and the MRI (magnetic resonance imaging) of the patient are acquired; the nonlinear deformation is performed on the MRI and the T1 template to generate deformation information parameters; the AAL (automated anatomical labeling) atlas is deformed to an individual brain space of the patient, so as to generate an individual brain space AAL atlas, where the AAL atlas and the T1 template are in a same space; lateralization indexes of the ROIs of the individual brain space AAL atlas corresponding to the PET image normalized through the gray-scale intensity are calculated; the lateralization indexes are inputted into one or more machine learning models to analyze the result of determining a target.

PET quantitative localization system and operation method thereof

The present disclosure provides an operating method of a brain imaging neurological abnormality prediction system, which includes steps as follows. The T1-weighted image and the diffusion-weighted image of the patient are acquired; the image process is performed on the T1-weighted image and the diffusion-weighted image to obtain a smoothed brain standard space infarction image; the smoothed brain standard space infarction image is multiplied by and a weighted image for a post-processing to obtain a post-weight image; the post-weight image is inputted to the deep learning cross validation classification model of transfer learning to predict whether the neurological abnormality occurs within a predetermined period after the patient's brain disease.

Brain imaging neurological abnormality prediction system and operation method thereof

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.

Dopamine transporter check system and operation method thereof

The present disclosure provides an operating method of a system for analyzing brain tissue based on computerized tomographic imaging, and the operation method includes steps as follows. A computed tomography image of a subject is aligned to a predetermined standard brain space image, to obtain a first normalized test computed tomography image. A voxel contrast of the first normalized test computed tomography image is enhanced to obtain an enhanced first normalized test computed tomography image. The enhanced first normalized test computed tomography image is aligned to an average computed tomographic image of a control group to obtain a second normalized test computed tomography image. An analysis based on the second normalized test computed tomography image and a plurality of computerized tomographic images of the control group is performed to obtain a t-score map.

System for analyzing brain tissue components based on computed tomography image and operation method thereof

A medical image processing system includes a memory and a processor coupled to each other. The processor accesses and executes instructions which memory stores to perform the following: obtaining a plurality of brain MR images corresponding to a subject, wherein the brain MR images corresponds to a subject brain space; accessing a DBS targets atlas corresponding to a specific stimulation area; transforming the DBS targets atlas from a MNI brain space to the subject brain space based on a DARTEL algorithm; marking at least one coordinate having a largest Voxel value in the brain MR images based on the transformed DBS targets atlas; and storing the brain MR images being targeted with the at least one coordinate into a predetermined format corresponding to a guiding device so that the guiding device displays the brain MR images being targeted with the at least one coordinate for guidance in DBS procedure.

Medical image processing system and method thereof

A method for localizing an intracranial electrode in a subject's brain is provided. The intracranial electrode has at least one electrode contact. The method includes: acquiring a first brain image reconstructed from first image data acquired after electrode-implantation; acquiring a second brain image reconstructed from second image data acquired before the electrode-implantation; co-registering the first brain image and the second brain image to acquire spatial transformation parameters; extracting a first coordinate of the electrode contact from the first brain image; converting the first coordinate into a second coordinate in the second brain image by using the spatial transformation parameters; co-registering the second brain image and a universal brain atlas to define functional zones in the second brain image; and defining a corresponding functional zone where the second coordinate is located. Another alternative method and a system for localizing an intracranial electrode are also provided herein.

Method for localizing implanted intracranial electrode

The present disclosure provides an operating method of an automatic analysis system on magnetic resonance imaging (MRI), which includes steps as follows. Images are received from of the subject's brain from the MRI machine. Contrast-enhanced T1-weighted images and T2-weighted images are obtained from the images, and the pre-processing is performed on the images. The ratio of T2-weighted images to contrast-enhanced T1-weighted images is calculated to generate contrast-enhanced images. The unsupervised clustering is performed on the region of interest in the contrast-enhanced image to separate a cystic part and a non-cystic part so as to calculate the feature parameters. After radiosurgery is performed on the brain tumor corresponding to the region of interest, the volume change of the tumor is analyzed. The linear regression analysis of the feature parameters and the volume change of the tumor is performed for prognostic evaluation.

Automatic analysis system on magnetic resonance imaging and operation method thereof

A system for analyzing brain tissue components based on magnetic resonance image. The system includes a memory and a processor. The memory stores instructions. The processor accesses and executes the instructions to perform the following: extracting maps of tissue from a brain magnetic resonance imaging (MRI) corresponding to normal subjects; averaging the maps of tissue according to a number of the normal subjects to generate reference maps that correspond to different tissues; receiving a brain MRI sample having a targeted region; and analyzing the brain MRI sample based on the reference maps and the targeted region to generate an analysis result, in which the analysis result indicates a ratio of tissues in the targeted region of the brain MRI sample.

System and method for analyzing brain tissue components based on magnetic resonance image

Method and system for localizing implanted intracranial electrode

The present disclosure provides an operating method of an automatic brain infarction detection system on magnetic resonance imaging (MRI), which includes steps as follows. Images corresponding to different slices of a brain of a subject are received from the MRI machine. The image mask process is performed on first and second images of the images. It is determined whether the cerebellum image intensity and the brain image intensity in the first image are matched. When the cerebellum image intensity and the brain image intensity are not matched, the cerebellar image intensity in the first image is adjusted. The first image is processed through a nonlinear regression to obtain a third image. A neural network identify an infarct region by using the first, second and third images that are cut.

Automatic brain infarction detection system on magnetic resonance imaging and operation method thereof

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.

Magnetic resonance image analysis method and method for evaluating the risks of radiotherapy

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.

Magnetic resonance imaging white matter hyperintensities region recognizing method and system

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 I. A threshold is determined, which is the I+ 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.

Growing community of inventors

Hsinchu County, Taiwan

Syu-Jyun Peng