Karimi et al., 2021 - Google Patents
A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imagingKarimi et al., 2021
View HTML- Document ID
- 471353216573688031
- Author
- Karimi D
- Vasung L
- Jaimes C
- Machado-Rivas F
- Khan S
- Warfield S
- Gholipour A
- Publication year
- Publication venue
- Medical image analysis
External Links
Snippet
Accurate modeling of diffusion-weighted magnetic resonance imaging measurements is necessary for accurate brain connectivity analysis. Existing methods for estimating the number and orientations of fascicles in an imaging voxel either depend on non-convex …
- 238000010801 machine learning 0 title abstract description 24
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R33/00—Arrangements or instruments for measuring magnetic variables
- G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
- G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
- G01R33/48—NMR imaging systems
- G01R33/54—Signal processing systems, e.g. using pulse sequences, Generation or control of pulse sequences ; Operator Console
- G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
- G01R33/563—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution of moving material, e.g. flow contrast angiography
- G01R33/56341—Diffusion imaging
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10088—Magnetic resonance imaging [MRI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10084—Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
- G06T7/0014—Biomedical image inspection using an image reference approach
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US11967072B2 (en) | Three-dimensional object segmentation of medical images localized with object detection | |
Lin et al. | Fast learning of fiber orientation distribution function for MR tractography using convolutional neural network | |
Park et al. | Segmentation of perivascular spaces in 7 T MR image using auto-context model with orientation-normalized features | |
Karimi et al. | A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging | |
Li et al. | Fast and robust diffusion kurtosis parametric mapping using a three-dimensional convolutional neural network | |
Gupta et al. | Cardiac MR perfusion image processing techniques: a survey | |
Karimi et al. | Learning to estimate the fiber orientation distribution function from diffusion-weighted MRI | |
Scheufele et al. | Fully automatic calibration of tumor-growth models using a single mpMRI scan | |
Rajasekaran et al. | Advanced brain tumour segmentation from mri images | |
EP2147330B1 (en) | Image processing method | |
Tax et al. | Sheet Probability Index (SPI): Characterizing the geometrical organization of the white matter with diffusion MRI | |
Vakli et al. | Predicting body mass index from structural MRI brain images using a deep convolutional neural network | |
Zhang et al. | Learning-based structurally-guided construction of resting-state functional correlation tensors | |
Li et al. | BrainK for structural image processing: creating electrical models of the human head | |
Jha et al. | Single-shell to multi-shell dMRI transformation using spatial and volumetric multilevel hierarchical reconstruction framework | |
Aja-Fernández et al. | Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies | |
Tantisatirapong | Texture analysis of multimodal magnetic resonance images in support of diagnostic classification of childhood brain tumours | |
Kaden et al. | Variational inference of the fiber orientation density using diffusion MR imaging | |
CN116369891A (en) | Method and device for predicting development progress of mild cognitive impairment and computer equipment | |
Descoteaux et al. | Deterministic and probabilistic Q-Ball Tractography: from diffusion to sharp fiber distribution | |
Çetingül et al. | Estimation of local orientations in fibrous structures with applications to the Purkinje system | |
Jiang et al. | Brain tumor segmentation in multi-parametric magnetic resonance imaging using model ensembling and super-resolution | |
Gu | Advanced analysis of diffusion MRI data | |
Demir et al. | Online agglomerative hierarchical clustering of neural fiber tracts | |
El-Rafei et al. | Automatic segmentation of the optic radiation using DTI in healthy subjects and patients with glaucoma |