Journal article
Investigating time-independent and time-dependent diffusion phenomena using steady-state diffusion MRI
- Abstract:
-
Diffusion MRI is a leading method to non-invasively characterise brain tissue microstructure across multiple domains and scales. Diffusion-weighted steady-state free precession (DW-SSFP) is an established imaging sequence for post-mortem MRI, addressing the challenging imaging environment of fixed tissue with short T2 and low diffusivities. However, a current limitation of DW-SSFP is signal interpretation: it is not clear what diffusion ‘regime’ the sequence probes and therefore its potential to characterise tissue microstructure. Building on Extended Phase Graphs (EPG), I establish two alternative representations of the DW-SSFP signal in terms of (1) conventional b-values (time-independent diffusion) and (2) encoding power-spectra (time-dependent diffusion). The proposed representations provide insights into how different parameter regimes and gradient waveforms impact the diffusion sensitivity of DW-SSFP. I subsequently introduce an approach to incorporate existing biophysical models into DW-SSFP without the requirement of extensive derivations, with time dependence estimated via a Gaussian phase approximation representation of the DW-SSFP signal. Investigations incorporating free-diffusion and tissue-relevant microscopic restrictions (cylinder of varying radius) give excellent agreement to complementary analytical models and Monte Carlo simulations. Experimentally, the time-independent representation is used to derive Tensor and proof-of-principle NODDI estimates in a whole human post-mortem brain. A final SNR-efficiency investigation demonstrates the theoretical potential of DW-SSFP for ultra-high field microstructural imaging.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Version of record, pdf, 4.4MB, Terms of use)
-
- Publisher copy:
- 10.1038/s41598-025-87377-x
Authors
- Funder identifier:
- https://ror.org/029chgv08
- Grant:
- 222829/Z/21/Z
- Publisher:
- Springer Nature
- Journal:
- Scientific Reports More from this journal
- Volume:
- 15
- Issue:
- 1
- Article number:
- 3580
- Publication date:
- 2025-01-28
- Acceptance date:
- 2025-01-20
- DOI:
- EISSN:
-
2045-2322
- Language:
-
English
- Pubs id:
-
2081430
- Local pid:
-
pubs:2081430
- Deposit date:
-
2025-01-29
- ARK identifier:
Terms of use
- Copyright holder:
- Benjamin C. Tendler
- Copyright date:
- 2025
- Rights statement:
- Copyright © 2025, The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
If you are the owner of this record, you can report an update to it here: Report update to this record