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Abstract #4426

Improving neural soma imaging using the power spectrum of the free gradient waveforms

Maryam Afzali1, Marco Palombo2, Lars Mueller 1, Hui Zhang2, Daniel C Alexander2, Markus Nilsson3, and Derek K Jones1,4
1Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom, 2Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom, 3Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden, 4Mary MacKillop Institute for Health Research, Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria, 3065, Australia

Diffusion magnetic resonance imaging is a non-invasive technique to probe the microstructural features of tissue. Conventional diffusion encoding is unable to disentangle different microstructural features; therefore, multidimensional diffusion encoding has been proposed previously to solve this problem. Here we investigate different combinations of b-tensor encoding in a three-compartment model called SANDI. To estimate the size of soma in this model, we use frequency domain analysis because optimized b-tensor encoding waveforms do not provide a well-defined diffusion-time. The results show that different combinations of linear, planar, and spherical tensor encoding can improve the estimation of a specific parameter.

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