This paper presents a platform to study the relationship between upper limb kinematic and biopotential measurements. The platform comprises of a haptic joystick, biopotential acquisition systems and 3D rendered virtua...
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ISBN:
(纸本)9781424479276
This paper presents a platform to study the relationship between upper limb kinematic and biopotential measurements. The platform comprises of a haptic joystick, biopotential acquisition systems and 3D rendered virtual tasks that require user interaction. The haptic joystick, named Tee-R, reproduces the pronation-supination and flexion-extension movements of the human arm, which are directly mapped to a 2D graphic display. The biopotential acquisition system is able to record electroencephalography (EEG) and electromyography (EMG) signals and synchronize them with kinematic data obtained from the Tee-R. The 3D virtual tasks are designed to obtain performance measurements from the user interaction. We include an example that depicts the possibilities of application for the study of event-related (de)synchronization (ERD/ERS) based on EEG during motor tasks.
We present a novel real-time implementation of local phase feature extraction from volumetric image data based on 3D directional (log-Gabor) filters. We achieve drastic performance gains without compromising the signa...
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ISBN:
(纸本)9781467364560
We present a novel real-time implementation of local phase feature extraction from volumetric image data based on 3D directional (log-Gabor) filters. We achieve drastic performance gains without compromising the signal-to-noise ratio by pre-computing the filters and adaptive noise estimation parameters, and streamlining the remainder of the computations to efficiently run on a multi-processor graphic processing unit (GPU). We validate our method on clinical ultrasound data and demonstrate a 15-fold speedup in computation time over state-of-the art methods, which could potentially facilitate a wide range of practical applications for real-time image-guided procedures.
We propose a biquaternion formalism to model diffusion tensor magnetic resonance imaging (DT-MRI) data. Unlike previous methods that use dimensionality reduction, we are able to process the full tensor in a holistic m...
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We propose a biquaternion formalism to model diffusion tensor magnetic resonance imaging (DT-MRI) data. Unlike previous methods that use dimensionality reduction, we are able to process the full tensor in a holistic manner while respecting the underlying manifold of the data. Using this approach, we introduce the Fourier transform and convolution for DT-MRI for the first time, which can be applied directly on the full tensor. This opens up a wide range of applications for DT-MRI image processing. Further, based on this formulation, we present a biquaternion gradient vector and edge detector for DT images. Preliminary results of applying the Fourier transform, convolution and edge detector on synthetic examples as well as real DT data show great promise in our approach for DT image processing.
Spatial patterns of activation statistics within anatomically-defined regions of interest (ROIs) in functional magnetic resonance imaging (fMRI) data were recently shown to be sensitive markers of brain activation cha...
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Spatial patterns of activation statistics within anatomically-defined regions of interest (ROIs) in functional magnetic resonance imaging (fMRI) data were recently shown to be sensitive markers of brain activation changes. Most current methods that analyze fMRI activation statistics largely ignore this. The accuracy and validity of the prevalent approach of spatial normalization of functional data is also being debated. In this paper we present a novel spherical harmonics based rotational, translation and scale invariant feature representation of fMRI data which allows for direct quantification of activation patterns within ROIs without any need for spatial normalization. We also present a novel parallel technique for quantifying anatomical properties of the ROIs where we employ a principal component based approach to reduce the effects of anatomical variability in the ROI on functional pattern analysis. We validate our proposed method and demonstrate its improved sensitivity over conventional methods using real and simulated fMRI data.
In earlier work, we have shown the importance of including 3D shape characteristics when analyzing regions of interest (ROIs) in magnetic resonance imaging (MRI) data. Spherical harmonics (SPHARM) based ROI shape desc...
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In earlier work, we have shown the importance of including 3D shape characteristics when analyzing regions of interest (ROIs) in magnetic resonance imaging (MRI) data. Spherical harmonics (SPHARM) based ROI shape descriptors were proposed and shown to provide important complementary information to traditionally used simple volumetric ROI measures. In this paper we extend our SPHARM shape parameterization technique by using functions defined on concentric spherical shells. We then propose the use of a novel radial transform to obtain unique features even under independent rotations of the constituting shells. These enhanced features enable the analysis of 3D ROIs with complex topologies including those with possible disconnections (e.g. ventricles). We validate the proposed 3D shape descriptors on synthetic data and demonstrate their sensitivity to subtle shape changes in the presence of inter-subject variability. We also apply our approach to real MRI data and detect significant shape changes in the left and right thalamus in Parkinson's disease (PD) patients when compared against normal volunteers, complementing the observed volumetric changes.
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