Down-sampling in diffusion MRI: a bundle-specific DTI and NODDI study

Federico Spagnolo; Susanna Gobbi; Enikő Zsoldos; Manon Edde; Matthias Weigel; Cristina Granziera; Maxime Descoteaux; Muhamed Barakovic; Stefano Magon

doi:10.3389/fnimg.2024.1359589

Down-sampling in diffusion MRI: a bundle-specific DTI and NODDI study

Front Neuroimaging. 2024 Mar 28:3:1359589. doi: 10.3389/fnimg.2024.1359589. eCollection 2024.

Authors

Federico Spagnolo¹, Susanna Gobbi¹, Enikő Zsoldos¹, Manon Edde^{2

3}, Matthias Weigel^{4

5

6

7}, Cristina Granziera^{4

5

6}, Maxime Descoteaux^{2

3}, Muhamed Barakovic¹, Stefano Magon¹

Affiliations

¹ Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, Basel, Switzerland.
² Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, QC, Canada.
³ Imeka Solutions Inc, Sherbrooke, QC, Canada.
⁴ Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland.
⁵ Department of Neurology, University Hospital Basel, Basel, Switzerland.
⁶ Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland.
⁷ Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland.

Abstract

Introduction: Multi-shell diffusion Magnetic Resonance Imaging (dMRI) data has been widely used to characterise white matter microstructure in several neurodegenerative diseases. The lack of standardised dMRI protocols often implies the acquisition of redundant measurements, resulting in prolonged acquisition times. In this study, we investigate the impact of the number of gradient directions on Diffusion Tensor Imaging (DTI) and on Neurite Orientation Dispersion and Density Imaging (NODDI) metrics.

Methods: Data from 124 healthy controls collected in three different longitudinal studies were included. Using an in-house algorithm, we reduced the number of gradient directions in each data shell. We estimated DTI and NODDI measures on six white matter bundles clinically relevant for neurodegenerative diseases.

Results: Fractional Anisotropy (FA) measures on bundles where data were sampled at the 30% rate, showed a median L₁ distance of up to 3.92% and a 95% CI of (1.74, 8.97)% when compared to those obtained at reference sampling. Mean Diffusivity (MD) reached up to 4.31% and a 95% CI of (1.60, 16.98)% on the same premises. At a sampling rate of 50%, we obtained a median of 3.90% and a 95% CI of (1.99, 16.65)% in FA, and 5.49% with a 95% CI of (2.14, 21.68)% in MD. The Intra-Cellular volume fraction (ICvf) median L₁ distance was up to 2.83% with a 95% CI of (1.98, 4.82)% at a 30% sampling rate and 3.95% with a 95% CI of (2.39, 7.81)% at a 50% sampling rate. The volume difference of the reconstructed white matter at reference and 50% sampling reached a maximum of (2.09 ± 0.81)%.

Discussion: In conclusion, DTI and NODDI measures reported at reference sampling were comparable to those obtained when the number of dMRI volumes was reduced by up to 30%. Close to reference DTI and NODDI metrics were estimated with a significant reduction in acquisition time using three shells, respectively with: 4 directions at a b value of 700 s/mm², 14 at 1000 s/mm², and 32 at 2000 s/mm². The study revealed aspects that can be important for large-scale clinical studies on bundle-specific diffusion MRI.

Keywords: DTI; MRI; NODDI; acquisition time; neurodegenerative diseases.

Grants and funding

U01 AG024904/AG/NIA NIH HHS/United States