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• W2941895184 abstract "Purpose In diffusion‐weighted magnetic resonance imaging ( DW ‐ MRI ), the fiber orientation distribution function ( fODF ) is of great importance for solving complex fiber configurations to achieve reliable tractography throughout the brain, which ultimately facilitates the understanding of brain connectivity and exploration of neurological dysfunction. Recently, multi‐shell multi‐tissue constrained spherical deconvolution ( MSMT ‐ CSD ) method has been explored for reconstructing full fODF s. To achieve a reliable fitting, similar to other model‐based approaches, a large number of diffusion measurements is typically required for MSMT ‐ CSD method. The prolonged acquisition is, however, not feasible in practical clinical routine and is prone to motion artifacts. To accelerate the acquisition, we proposed a method to reconstruct the fODF from downsampled diffusion‐weighted images ( DWI s) by leveraging the strong inference ability of the deep convolutional neural network (CNN). Methods The method treats spherical harmonics ( SH )‐represented DWI signals and fODF coefficients as inputs and outputs, respectively. To compensate for the reduced gradient directions with reduced number of DWI s in acquisition in each voxel, its surrounding voxels are incorporated by the network for exploiting their spatial continuity. The resulting fODF coefficients are fitted with applying the CNN in a multi‐target regression model. The network is composed of two convolutional layers and three fully connected layers. To obtain an initial evaluation of the method, we quantitatively measured its performance on a simulated dataset. Then, for in vivo tests, we employed data from 24 subjects from the Human Connectome Project ( HCP ) as training set and six subjects as test set. The performance of the proposed method was primarily compared to the super‐resolved MSMT ‐ CSD with the decreasing number of DWI s. The fODF s reconstructed by MSMT ‐ CSD from all available 288 DWI s were used as training labels and the reference standard. The performance was quantitatively measured by the angular correlation coefficient ( ACC ) and the mean angular error (MAE). Results For the simulated dataset, the proposed method exhibited the potential advantage over the model reconstruction. For the in vivo dataset, it achieved superior results over the MSMT ‐ CSD in all the investigated cases, with its advantage more obvious when a limited number of DWI s were used. As the number of DWI s was reduced from 95 to 25, the median ACC ranged from 0.96 to 0.91 for the CNN , but 0.93 to 0.77 for the MSMT ‐ CSD (with perfect score of 1). The angular error in the typical regions of interest (ROIs) was also much lower, especially in multi‐fiber regions. The average MAE for the CNN method in regions containing one, two, three fibers was, respectively, 1.09°, 2.75°, and 8.35° smaller than the MSMT ‐ CSD method. The visual inception of the fODF further confirmed this superiority. Moreover, the tractography results validated the effectiveness of the learned fODF , in preserving known major branching fibers with only 25 DWI s. Conclusion Experiments on HCP datasets demonstrated the feasibility of the proposed method in recovering fODF s from up to 11‐fold reduced number of DWI s. The proposed method offers a new streamlined reconstruction procedure and exhibits promising potential in acquisition acceleration for the reconstruction of fODF s with good accuracy." @default.
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• W2941895184 date "2019-05-11" @default.
• W2941895184 modified "2023-10-17" @default.
• W2941895184 title "Fast learning of fiber orientation distribution function for <scp>MR</scp> tractography using convolutional neural network" @default.
• W2941895184 cites W1133283280 @default.
• W2941895184 cites W1502698477 @default.
• W2941895184 cites W1512887588 @default.
• W2941895184 cites W1530232861 @default.
• W2941895184 cites W1582774210 @default.
• W2941895184 cites W1806891645 @default.
• W2941895184 cites W1965762557 @default.
• W2941895184 cites W1965894642 @default.
• W2941895184 cites W1968179180 @default.
• W2941895184 cites W1975746998 @default.
• W2941895184 cites W1982877110 @default.
• W2941895184 cites W1983208069 @default.
• W2941895184 cites W1988954604 @default.
• W2941895184 cites W1998996817 @default.
• W2941895184 cites W1999890357 @default.
• W2941895184 cites W2000619457 @default.
• W2941895184 cites W2001611992 @default.
• W2941895184 cites W2001617943 @default.
• W2941895184 cites W2006096283 @default.
• W2941895184 cites W2010125850 @default.
• W2941895184 cites W2020519533 @default.
• W2941895184 cites W2022530159 @default.
• W2941895184 cites W2034252184 @default.
• W2941895184 cites W2036785860 @default.
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• W2941895184 cites W2070292918 @default.
• W2941895184 cites W2080198834 @default.
• W2941895184 cites W2086867325 @default.
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• W2941895184 cites W2111508341 @default.
• W2941895184 cites W2121140812 @default.
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• W2941895184 cites W2155482699 @default.
• W2941895184 cites W2161336914 @default.
• W2941895184 cites W2161824996 @default.
• W2941895184 cites W2165020904 @default.
• W2941895184 cites W2166496643 @default.
• W2941895184 cites W2213612645 @default.
• W2941895184 cites W2282892678 @default.
• W2941895184 cites W2318872361 @default.
• W2941895184 cites W2322371438 @default.
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• W2941895184 doi "https://doi.org/10.1002/mp.13555" @default.
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