eprintid: 10129627 rev_number: 14 eprint_status: archive userid: 608 dir: disk0/10/12/96/27 datestamp: 2021-06-14 15:00:24 lastmod: 2021-09-17 22:57:07 status_changed: 2021-06-14 15:00:24 type: article metadata_visibility: show creators_name: Afzali, M creators_name: Nilsson, M creators_name: Palombo, M creators_name: Jones, DK title: SPHERIOUSLY? The challenges of estimating sphere radius non-invasively in the human brain from diffusion MRI ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F48 note: This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) abstract: The Soma and Neurite Density Imaging (SANDI) three-compartment model was recently proposed to disentangle cylindrical and spherical geometries, attributed to neurite and soma compartments, respectively, in brain tissue. There are some recent advances in diffusion-weighted MRI signal encoding and analysis (including the use of multiple so-called ’b-tensor’ encodings and analysing the signal in the frequency-domain) that have not yet been applied in the context of SANDI. In this work, using: (i) ultra-strong gradients; (ii) a combination of linear, planar, and spherical b-tensor encodings; and (iii) analysing the signal in the frequency domain, three main challenges to robust estimation of sphere size were identified: First, the Rician noise floor in magnitude-reconstructed data biases estimates of sphere properties in a non-uniform fashion. It may cause overestimation or underestimation of the spherical compartment size and density. This can be partly ameliorated by accounting for the noise floor in the estimation routine. Second, even when using the strongest diffusion-encoding gradient strengths available for human MRI, there is an empirical lower bound on the spherical signal fraction and radius that can be detected and estimated robustly. For the experimental setup used here, the lower bound on the sphere signal fraction was approximately 10%. We employed two different ways of establishing the lower bound for spherical radius estimates in white matter. The first, examining power-law relationships between the DW-signal and diffusion weighting in empirical data, yielded a lower bound of , while the second, pure Monte Carlo simulations, yielded a lower limit of and in this low radii domain, there is little differentiation in signal attenuation. Third, if there is sensitivity to the transverse intra-cellular diffusivity in cylindrical structures, e.g., axons and cellular projections, then trying to disentangle two diffusion-time-dependencies using one experimental parameter (i.e., change in frequency-content of the encoding waveform) makes spherical radii estimates particularly challenging. We conclude that due to the aforementioned challenges spherical radii estimates may be biased when the corresponding sphere signal fraction is low, which must be considered. date: 2021-08-15 date_type: published publisher: Elsevier BV official_url: https://doi.org/10.1016/j.neuroimage.2021.118183 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1870980 doi: 10.1016/j.neuroimage.2021.118183 language_elements: en lyricists_name: Palombo, Marco lyricists_id: MPALO05 actors_name: Palombo, Marco actors_name: Flynn, Bernadette actors_id: MPALO05 actors_id: BFFLY94 actors_role: owner actors_role: impersonator full_text_status: public publication: NeuroImage volume: 237 article_number: 118183 issn: 1053-8119 citation: Afzali, M; Nilsson, M; Palombo, M; Jones, DK; (2021) SPHERIOUSLY? The challenges of estimating sphere radius non-invasively in the human brain from diffusion MRI. NeuroImage , 237 , Article 118183. 10.1016/j.neuroimage.2021.118183 <https://doi.org/10.1016/j.neuroimage.2021.118183>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10129627/1/1-s2.0-S1053811921004602-main.pdf