Resolving degeneracy in diffusion MRI biophysical model parameter estimation using double diffusion encoding
- Submitting institution
-
The University of Leeds
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- UOA11-3873
- Type
- D - Journal article
- DOI
-
10.1002/mrm.27714
- Title of journal
- Magnetic Resonance in Medicine
- Article number
- -
- First page
- 395
- Volume
- 82
- Issue
- 1
- ISSN
- 0740-3194
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
4
- Research group(s)
-
C - BMH (Applied Computing in Biology, Medicine and Health)
- Citation count
- 25
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- We prove analytically that Double Diffusion Encoding (DDE) provides invariant information non-accessible from Single Diffusion Encoding (SDE), which makes parameter estimation of NODDIDA model injective. Computational experiments show DDE reduces the estimation bias and MSE on a 5D model parameter space. This paper for the first time formally elucidates why SDE leads to intrinsically ill-posed parameter estimation in DW-MRI fundamentally resolving this open problem. This research led to follow-on collaborations with S Jespersen (Aarhus University, DK) and D Novikov (NYU Langone), a 3-month visit from our PhD student S Coelho to NYU, and an oral presentation in ISMRM.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
