Yi-Ping Chao

Probabilistic topography of human corpus callosum using cytoarchitectural parcellation and high angular resolution diffusion imaging tractography

The function of the corpus callosum (CC) is to distribute perceptual, motor, cognitive, learned, and voluntary information between the two hemispheres of the brain. Accurate parcellation of the CC according to fiber composition and fiber connection is of upmost important. In this work, population‐based probabilistic connection topographies of the CC, in the standard Montreal Neurological Institute (MNI) space, are estimated by incorporating anatomical cytoarchitectural parcellation with high angular resolution diffusion imaging (HARDI) tractography. First, callosal fibers are extracted using multiple fiber assignment by continuous tracking algorithm based on q‐ball imaging (QBI), on 12 healthy and young subjects. Then, the fiber tracts are aligned in the standard MNI coordinate system based on a tract‐based transformation scheme. Next, twenty‐eight Brodmanns areas on the surface of cortical cortex are registered to the MNI space to parcellate the aligned callosal fibers. Finally, the population‐based topological subdivisions of the midsagittal CC to each cortical target are then mapped. And the resulting subdivisions of the CC that connect to the frontal and somatosensory associated cortex are also showed. To our knowledge, it is the first topographic subdivisions of the CC done using HARDI tractography and cytoarchitectonic information. In conclusion, this sophisticated topography of the CC may serve as a landmark to further understand the correlations between the CC, brain intercommunication, and functional cytoarchitectures. Hum Brain Mapp 2009.

Evaluation of the accuracy and angular resolution of q-ball imaging

Q-ball imaging (QBI) has been proposed for the mapping of multiple intravoxel fiber structures using the Funk-Radon transform on high angular resolution diffusion images (HARDI). However, the accuracy and the angular resolution of QBI to define fiber orientations and its dependence on diffusion imaging parameters remain unclear. The phantom models, made up of sheets of parallel capillaries filled with water, were designed to evaluate the accuracy and the angular resolution of QBI at different |q| values. With an inner diameter of 20 mum and an outer diameter of 90 mum, the capillaries afforded a restrictive environment compared with the diffusion measurement scale. Further, the angular resolutions of QBI at various |q| value were also quantified on the corpus callosum in the human brain. The full width at half maximum (FWHM) of the main lobe of normalized orientation distribution function (nODF) was calculated and adopted to quantify the angular resolution of QBI. With the phantom model, a higher |q| value resulted in worse accuracy but better angular resolution for QBI. The same trend where a higher |q| value yielded a better angular resolution was also observed in the human study. Upon comparison of QBI with T2WI, QBI with |q|=277 cm(-1) (b=3000 s/mm(2)) was found to be insufficient to differentiate capillaries crossing at 45 degrees . However, when encoding with |q|=320, 358, and 392 cm(-1) (b=4000, 5000, and 6000 s/mm(2)), the deviation angles between the primary ODF and the 45 degrees phantoms were -4.91 degrees +/-2.72 degrees , -1.37 degrees +/-2.32 degrees , and -0.69 degrees +/-1.54 degrees with adequate signal-to-noise ratio (SNR). These results were consistent with the FWHM-nODF, which showed that a |q| value of 320 cm(-1) was the threshold to resolve capillaries intersecting at 45 degrees . Additionally, it was demonstrated in both the phantom model and the human brain that QBI encoding with lower |q| values may result in underestimation of the orientations of the crossing fibers. In conclusion, QBI was found to accurately resolve crossing fiber orientations and was highly dependent on the selected |q| value.

Diffeomorphic Image Registration of Diffusion MRI Using Spherical Harmonics

Nonrigid registration of diffusion magnetic resonance imaging (MRI) is crucial for group analyses and building white matter and fiber tract atlases. Most current diffusion MRI registration techniques are limited to the alignment of diffusion tensor imaging (DTI) data. We propose a novel diffeomorphic registration method for high angular resolution diffusion images by mapping their orientation distribution functions (ODFs). ODFs can be reconstructed using q-ball imaging (QBI) techniques and represented by spherical harmonics (SHs) to resolve intra-voxel fiber crossings. The registration is based on optimizing a diffeomorphic demons cost function. Unlike scalar images, deforming ODF maps requires ODF reorientation to maintain its consistency with the local fiber orientations. Our method simultaneously reorients the ODFs by computing a Wigner rotation matrix at each voxel, and applies it to the SH coefficients during registration. Rotation of the coefficients avoids the estimation of principal directions, which has no analytical solution and is time consuming. The proposed method was validated on both simulated and real data sets with various metrics, which include the distance between the estimated and simulated transformation fields, the standard deviation of the general fractional anisotropy and the directional consistency of the deformed and reference images. The registration performance using SHs with different maximum orders were compared using these metrics. Results show that the diffeomorphic registration improved the affine alignment, and registration using SHs with higher order SHs further improved the registration accuracy by reducing the shape difference and improving the directional consistency of the registered and reference ODF maps.

Diffusion MRI Registration Using Orientation Distribution Functions

We propose a linear-elastic registration method to register diffusion-weighted MRI (DW-MRI) data sets by mapping their diffusion orientation distribution functions (ODFs). The ODFs were reconstructed using a q-ball imaging (QBI) technique to resolve intravoxel fiber crossing. The registration method is based on mapping the ODF maps represented by spherical harmonics which yield analytic solutions and reduce the computational complexity. ODF reorientation is required to maintain the consistency with transformed local fiber directions. The reorientation matrices are extracted from the local Jacobian and directly applied to the coefficients of spherical harmonics. The similarity cost of the registration is defined by the ODF shape distance calculated from the spherical harmonic coefficients. The transformation fields are regularized by linear elastic constraints. The proposed method was validated using both synthetic and real data sets. Experimental results show that the elastic registration improved the affine alignment by further reducing the ODF shape difference; reorientation during the registration produced registered ODF maps with more consistent principle directions compared to registrations without reorientation or simultaneous reorientation.

A multiple streamline approach to high angular resolution diffusion tractography.

Diffusion-weighted magnetic resonance imaging has the ability to map neuronal architecture by estimating the 3D diffusion displacement within fibrous brain structures. This approach has non-invasively been demonstrated in the human brain with diffusion tensor tractography. Despite its valuable application in neuroscience and clinical studies however, it faces an inherent limit in mapping fiber tracts through areas with intervoxel incoherence. Recent advances in high angular resolution diffusion imaging have surpassed this limit and have the ability to resolve the complex fiber intercrossing within each MR voxel. To connect the fiber tracts from a multi-fiber system, this study proposed a modified fiber assignment using the continuous tracking (MFACT) algorithm and a tracking browser to propagate tracts along complex diffusion profiles. The Q-ball imaging method was adopted to acquire the diffusion displacements. Human motor pathways with seed points from the internal capsule, motor cortex, and pons were studied respectively. The results were consistent with known anatomy and demonstrated the promising potential of the MFACT method in mapping the complex neuronal architecture in the human brain.

DTI-based Virtual Reality System for Neurosurgery

The relationship between tumor mass and peritumoral structures including peritumoral edema is important for planning surgical trajectory and crucial for diagnosis, tumor excision, and post-surgical outcome. The recent development of diffusion tensor MRI has shown its feasibility in grading tumor, monitoring therapeutic effects, and post-surgery outcome. To visualize the tumor mass and the peritumoral structure, a 3D virtual reality environment was developed. Neural tractography and peritumoral anatomy were integrated in this interaction VR system. Using a 3D controller, suitable surgical trajectory can be defined by manipulating the tumor mass, peritumoral microstructure, and brain tissues before neurosurgery. Post-surgery evaluation showed that this system was useful to design pre-surgerical plan and optimize therapeutic outcome.

Potential in reducing scan times of HARDI by accurate correction of the cross-term in a hemispherical encoding scheme

To reduce the scan time of high angular resolution diffusion imaging (HARDI) by using the hemispherical encoding scheme with the cross‐term correction.

Probabilistic Anatomical Connection Derived from QBI with MFACT Approach

Recently developed methods to resolve multiple fiber orientations within a voxel from high angular resolution diffusion imaging (HARDI) are applied to resolve complex neuronal connectivity. In the study, an extension of multiple streamline tractography was proposed to estimate the probabilistic fiber tracking between brain areas. Multi-fiber component in the conjunction of corticospinal tracts, corpus callosum, and superior longitudinal fasciculus were studied in this study. The extracted results from probabilistic tracking presented the potential to estimate the whole brain map of anatomical connection probability.

Differences in Early Dynamic Connectivity between Visual Expansion and Contraction Stimulations Revealed by an fMRI-Directed MEG Approach

The human visual system responds asymmetrically to visual motion stimuli in opposite directions due to the involvement of the same brain areas but different operating processes. The expansion mode is thought to invoke a vigilance mechanism, whereas the contraction mode does not.

Investigating the effects of streamline-based fiber tractography on matrix scaling in brain connective network

Investigating the brain connective network using the modern graph theory has been widely applied in cognitive and clinical neuroscience research. In this study, we aimed to investigate the effects of streamline-based fiber tractography on the change of network properties and established a systematic framework to understand how an adequate network matrix scaling can be determined. The network properties, including degree, efficiency and betweenness centrality, show similar tendency in both left and right hemispheres. By employing the curve-fitting process with exponential law and measuring the residuals, the association between changes of network properties and threshold of track numbers is found and an adequate range of investigating the lateralization of brain network is suggested. The proposed approach can be further applied in clinical applications to improve the diagnostic sensitivity using network analysis with graph theory.