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Abstract Brain WM fiber orientation reconstruction is the throat of fiber imaging that set the premise of fiber tractgraphy. In conventional approaches, constraint conditions are dependent on prior information of fiber orientation, limiting the improvement of computation efficiency and precision. In this work, we presented a dictionary basic function based fiber orientation distribution function (fODF) estimation method. First, the uniform distribution of dictionary basis functions on unit sphere was established; second, spherical deconvolution (SD) model based on the dictionary basis functions was established; third, the coefficients of basic functions were directly obtained using nonnegative least square method. The proposed method successfully avoided the spurious peak, leases computation burden and eliminates higher order illposed inverse problem, which makes the sparsity and nonnegativity needed for the representation straightforwardly represented by weighted sums of basic functions. Experiments on simulated and clinical brain data both verified that computational complexity of the proposed method is much less than that of SuperCSD and QBI under the same conditions, such as, computational time of the method is a third as long as that of SuperCSD; the angular resolution of the method increases to a range of 20°, while SuperCSD is 40° and QBI is 60°. Because the method is simple and efficient, angular error does not change with such parameters as angle, and with the addition of the improvement of angular resolution and angular error performance, the probability of error fiber orientation reconstruction decreases to less than 8%, which will provide accurate local fiber orientation for fiber tractgraphy.
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