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Medical magnetic resonance imaging and spectroscopy

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Hessian matrix-based structure tensor analysis for fiber enhancement and direction encoding
Author(s): Shangbin Chen, Xiangning Li, Anan Li, Jie Peng, Hui Gong, Qingming Luo
Source: 2nd IET International Conference on Biomedical Image and Signal Processing (ICBISP 2017), 2017, p. 5 (5 .) - 5 (5 .)
Type: ConferencePaper
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Neuroanatomical connectivity is crucial for understanding brain functions and diseases. Quantitative analysis of fiber orientation and structural connectivity at single axon resolution has led to great challenges. To address this issue, Hessian matrix based structure tensor (HST) has been introduced for quantifying fiber architecture. Optimized parameters and block based processing pipeline has enabled whole brain fiber analysis with tunable scale from micro- to macroscopic level. The HST method has allowed for deciphering fiber density, color-encoded orientation and fiber tractography. Tested with Thy1-GFP mice datasets acquired with micro-optical sectioning tomography system, the proposed method enabled effective characterization of single axon/dendrite and ensemble of fibers in 3D space. HST has demonstrated the power for fiber quantification and the rationale for validation of diffusion tensor imaging.

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Co-registration of diffusion tensor imaging and micro-optical imaging based on ants
Author(s): Hong Ni, Miao Ren, Qiuyuan Zhong, Hui Gong, Qingming Luo, Shangbin Chen
Source: 2015 IET International Conference on Biomedical Image and Signal Processing (ICBISP 2015), 2015, p. 6 . - 6 .
Type: ConferencePaper
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Diffusion tensor imaging (DTI) can provide important macroscopic structural information for mouse brain but is limited by the available imaging resolution and inferentia tractography. To validate DTI tractography, it is paramount to merge DTI images and microscopic images with true representation of fibers. Recently, we have developed a micro-optical sectioning tomography (MOST) system which enables neurite level resolution. By using Advanced Normalization Tools, we have aligned our MOST dataset to the template dataset of DTI from Duke University. To optimize the computing efficacy, lower resolution of two different modal images has been used to get a displacement field of diffeomorphism. Then the displacement field is extended to the deformation of full resolution images. This has been a flexible strategy for 3D nonlinear mouse brain registration across different modalities and different resolutions. Under this kind of co-registration, we can show very fine neural fiber architectures as foreground (MOST) on the background (DTI) of the fibre density map. By comparing the derived maps between DTI and MOST, we have realized that DTI technique encounters pitfall for resolving complex neural fibres. Image fusion of mouse DTI and MOST dataset should promote both neural circuits study and DTI applications.

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