Fiber direction estimation using constrained spherical deconvolution based on multi-model response function / 生物医学工程学杂志

Constrained spherical deconvolution can quantify white matter fiber orientation distribution information from diffusion magnetic resonance imaging data. But this method is only applicable to single shell diffusion magnetic resonance imaging data and will provide wrong fiber orientation information in white matter tissue which contains isotropic diffusion signals. To solve these problems, this paper proposes a constrained spherical deconvolution method based on multi-model response function. Multi-shell data can improve the stability of fiber orientation, and multi-model response function can attenuate isotropic diffusion signals in white matter, providing more accurate fiber orientation information. Synthetic data and real brain data from public database were used to verify the effectiveness of this algorithm. The results demonstrate that the proposed algorithm can attenuate isotropic diffusion signals in white matter and overcome the influence of partial volume effect on fiber direction estimation, thus estimate fiber direction more accurately. The reconstructed fiber direction distribution is stable, the false peaks are less, and the recognition ability of cross fiber is stronger, which lays a foundation for the further research of fiber bundle tracking technology.

Más allá del tensor de difusión: experiencia preliminar en tractografía HARDI-CSD y super-resolución en 1. 5T / Beyond the diffusion tensor model: preliminary experience inHARDI tractography-CSD and super-resolution 1. 5T

Introduction: For more than a decade the diffusion tensor imaging model has been widely used in order to resolve and represent the intracranial white-matter microanatomy. Howeverthere are numerous studies that have successfully demonstrated the limitations associated with DTI in trying to define crossing-fibre regions. Various models have been developed with the intention of overcoming these limitations. This is why our study focuses on the description and preliminary experience in the use of tractography based on high-angular-resolution-diffusion imaging (HARDI) using the constrained spherical deconvolution (CSD) technique. Methods: The data was acquired on a Philips Achieva 1.5T resonator using a diffusion weighted single-shot echoplanar sequence along 32 directions with a b-value of 1000s/mm2. The images were processed using FSL v5.0 and MRtrix v0.2.10 software. Results: We achieved tensor free high-angular-resolution-diffusion tractographic images that better represented the white-matter micro-architecture than those obtained from the tensor model. Additionally, it was possible to generate track-density images (TDI) with a final resolution more than 500 times that of the acquired data.

Introducción: Desde hace más de una década que el modelo de tensor de difusión ha sido ampliamente utilizado con el fin de resolver y representar la microanatomía de la sustancia blanca intra-cerebral. Sin embargo, no son pocos los estudios que han logrado demostrar las grandes desventajas que el modelo presenta al tratar de definir regiones de entrecruzamiento de fibras. Diversos modelos han sido desarrollados para ofrecer una solución consistente, capaz de representar dichas regiones con mayor grado de correlación anatómica. Es por ello que nuestro estudio se enfoca en la descripción y experiencia preliminar en el uso de tractografía basada en imágenes de difusión de alta resolución angular (HARDI) usando el modelo de deconvolución esférica restringida (CSD). Métodos: La adquisición se realizó en un resonador Philips Achieva 1.5T mediante secuencia de difusión single-shot echo-planar de 32 direcciones con un b-value de 1.000s/mm² procesamiento de las imágenes se realizó mediante software FSL v5.0 y MRtrix v0.2.10. Resultados: Se lograron tractografías libres de tensor de difusión de alta resolución angular que representan la micro-arquitectura de la sustancia blanca de mejor manera que con las generadas a partir del modelo de tensor. Adicionalmente, se logró generar imágenes de densidad tractográfica (TDI) con una resolución final de más de 500 veces a la de adquisición.