Diffusion Models and Metrics
Before practicum on Friday, please complete following:
- Read [5. Fiber resolving methods]
- 5.1. Model-based methods
- 5.2. Model-free methods
- 5.3. Spherical deconvolution
- 5.4. Comparison
- Watch a video about diffusion tensor imaging
During practicum on Friday:
Review: Diffusion MRI Preprocessing (10 min)
Diffusion MRI models (10 min)
- Diffusion Models
|Model/Method||Full Name||Tool||Publications||B-table requirements||Handle Free Water||Quantify Restricted Diffusion||Resolve Multiple Fibers||Metrics|
|DTI (Basser, 1994)||Diffusion tensor imaging||353,000||B0, >= 1 b-value(s)||No (except for DTI-FWE)||No||No (except for Multi-Tensor)||fa, ad, rd, md|
|DKI (Jensen, 2005)||Diffusion kurtosis imaging||DKE||6,400||B0, >= 2 b-values||No||Yes||No (except for Multi-Tensor)||ak, rk, mk|
|NODDI (Zhang, 2012)||Neurite Orientation Dispersion and Density Imaging||NODDI Matlab Toolbox||4,680||B0, >= 2 b-values||Yes||Yes||No (except for Multi-Fiber NODDI )||iso, odi, ndi (icvf)|
|BSM (Behrens,2003)||Ball & Stick Model||BEDPOSTX,FSL||1,830||B0, >= 1 b-value(s)||Yes||Yes||Yes||qa, iso, rdi|
|Model-Free (Q-Space Imaging)|
|DSI (Wedeen, 2005)||Diffusion Spectrum Imaging||TrackVis||5,480||B0, >10 b-values on grid||No||No||Yes||-|
|QBI (Tuch,2004)||Q-ball Imaging||TrackVis||3,460||B0, 1 b-value||No||No||Yes||gfa|
|GQI (Yeh, 2010),QSDR (Yeh,2011)||Generalized Q-sampling Imaging,
Q-Space Diffeomorphic Reconstruction
|B0, >= 1 b-value(s)||Yes||Yes||Yes||qa, iso, rdi|
|CSD (Tournier,2007)||Constrained Spherical Deconvolution||MRtrix3, DIPY||4,010||1 b-value||No||No||Yes||afd|
|MSMT-CSD (Jeurissen,2014)||Multi-Shell, Multi-Tissue CSD||MRtrix3, DIPY||229||>= 2 b-values||Yes||Yes||Yes||afd|
for deriving voxel-wise metrics
- always include DTI metrics: (1) nearly all acquisition can fit a tensor model. (2) ITS metrics and related biophysics well known. (3) if a simpler method works, no need for more complicated one, unless the the purpose is getting better sensitivity/specificity.
- DKI, NODDI, GQI, and CSD provide more advanced metrics. BSM, DSI, QBI are usually used for fiber tracking.
- avoid using streamline counts as the metrics.
for mapping white matter tracts
- Both MSMT-CSD and CSD provides fiber orientation distribution (FOD). MSMT-CSD is more reliable than CSD.
- Both GQI, QBI, and DSI provides diffusion orientation distribution function (dODF). GQI can use all DWI acquisitions, whereas QBI and DSI have their specific acquisition requirements.
- GQI provides more conservative results, whereas CSD provides better sensitivity to crossing/branching fibers.
Hands-on: DTI, GQI, and QSDR Reconstruction (10 min)
- Download control subject 1 session 1 data from the SCA2 Diffusion Tensor Imaging study
- Reconstruction using DTI, GQI, QSDR
- Check and compare FIB files
- Batch SRC reconstruction
Metrics (10 min)
P. Mukherjee, J.I. Berman, S.W. Chung, C.P. Hess and R.G. Henry American Journal of Neuroradiology April 2008, 29 (4) 632-641; DOI: https://doi.org/10.3174/ajnr.A1051
- Microscopic metrics: anisotropy, diffusivities, and other voxel-based metrics
- Anisotropy: axonal density & myelination
- Isotropy: free water (not restricted) , cellularity (restricted)
- Macroscopic metrics: shape metrics
- Graph-based metrics: network measures
Hands-on: dMRI analysis (15 min)
- Region-based analysis
- Open a FIB file at Step T3
- Use [Region][Statistics] to get diffusion metrics from atlases (Documentation).
- Tract-based analysis
- Open a FIB file at Step T3 and map a white matter pathway (e.g. use autotrack)
- Get diffusion and shape metrics at CST using [Tracts][Statistics]
- Plot along CST metrics at z-direction or fiber direction. (Documentation)
- Insert other modalities
- Group-wise region/tract analysis using connectometry database
- Reconstruct all SRC files using QSDR
- Construct a database: samples
- Region-based analysis
- Tract-based analysis
Practicum assignment: ROI-based and Track-based analysis comparing SCA2 with controls
- Download SCA2 eddy-processed SRC Files. Ignore those in the follow-up folder.
- Reconstruct data at [Step T2 Reconstruction] using QSDR
- Create connectometry databases using [Step C1: Create a connectometry Database] and select
dti_faas the metric. The created database can be checked using [Step C2].
- Open the database at [Step T3: Fiber Tracking]
- Use region-based analysis to get fa at pons and compare between patients and controls.
- Use tract-based analysis to get fa at CST and compare between patients and controls.
- Cut CST into multiple segments and compare them between patients and controls.