User:Jadrian Miles/Diffusion simulation: Difference between revisions
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New page: * Axon gauges in range 0.25 -- 10 μm (wzhou cite 1) * Hindered diffusion differs from free diffusion by a "tortuosity" value (Nicholson "Diffusion and related transport mechanisms" '01)... |
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* Axon gauges in range 0.25 -- 10 μm (wzhou cite 1) | * Axon gauges in range 0.25 -- 10 μm (wzhou cite 1). "Extra-thick" axons (~20μm) are rare; about 3% occurrence at most, and only in certain structures. | ||
* Hindered diffusion differs from free diffusion by a "tortuosity" value (Nicholson "Diffusion and related transport mechanisms" '01) | * Hindered diffusion differs from free diffusion by a "tortuosity" value (Nicholson "Diffusion and related transport mechanisms" '01) | ||
* Volume fraction is ~80% intra? (Nilsson MRI '08) | * Volume fraction is ~80% intra? (Nilsson MRI '08) | ||
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* τ<sub>intra</sub> ~= 306 ms (but authors claim it's a bad fit) (ibid) (exchange times; see page 178 and ref 35) | * τ<sub>intra</sub> ~= 306 ms (but authors claim it's a bad fit) (ibid) (exchange times; see page 178 and ref 35) | ||
Human histology would be useful: hand-compute volume fraction of intracellular fluid, extracellular fluid, and myelin, as well as axon gauges. | |||
What about parallel DWIs and microscale histology on a sheep or macaque brain? | |||
Parameters: | |||
* direction: 1, 2, n | |||
* directional coherence: ignore, scalar | |||
* exchange: ignore, constant, per-compartment | |||
* isotropic D: constant globally, constant intra / extra, per-compartment | |||
* gauge: constant, per-compartment, fixed distribution, distribution per-compartment | |||
* volume fraction: intra / extra, per-compartment | |||
The experiment: | |||
# Generate lots of detailed 1-voxel numerical phantoms and run diffusion simulation on them to generate DWIs in a dense sampling of q-space. Now you have ground truth (kinda). | |||
# For a given phantom, fix all parameters but two at known correct values and then analyze the ambiguity between the free parameters. Treat one as independent and vary its assumed value; find the value of the other that best recreates the DWIs. Your output is a dependent-variable value ''and'' a DWI error. | |||
# For each pair of parameters, scatter-plot (or multi-line plot) the relationship between them over all phantoms. | |||
# Generalize some ambiguity rules and theorize about 'em. | |||
Latest revision as of 01:08, 24 March 2009
- Axon gauges in range 0.25 -- 10 μm (wzhou cite 1). "Extra-thick" axons (~20μm) are rare; about 3% occurrence at most, and only in certain structures.
- Hindered diffusion differs from free diffusion by a "tortuosity" value (Nicholson "Diffusion and related transport mechanisms" '01)
- Volume fraction is ~80% intra? (Nilsson MRI '08)
- Extracellular free D ~= 1.08 μm2/ms (ibid)
- τintra ~= 306 ms (but authors claim it's a bad fit) (ibid) (exchange times; see page 178 and ref 35)
Human histology would be useful: hand-compute volume fraction of intracellular fluid, extracellular fluid, and myelin, as well as axon gauges.
What about parallel DWIs and microscale histology on a sheep or macaque brain?
Parameters:
- direction: 1, 2, n
- directional coherence: ignore, scalar
- exchange: ignore, constant, per-compartment
- isotropic D: constant globally, constant intra / extra, per-compartment
- gauge: constant, per-compartment, fixed distribution, distribution per-compartment
- volume fraction: intra / extra, per-compartment
The experiment:
- Generate lots of detailed 1-voxel numerical phantoms and run diffusion simulation on them to generate DWIs in a dense sampling of q-space. Now you have ground truth (kinda).
- For a given phantom, fix all parameters but two at known correct values and then analyze the ambiguity between the free parameters. Treat one as independent and vary its assumed value; find the value of the other that best recreates the DWIs. Your output is a dependent-variable value and a DWI error.
- For each pair of parameters, scatter-plot (or multi-line plot) the relationship between them over all phantoms.
- Generalize some ambiguity rules and theorize about 'em.