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IVIM
Performs an analysis of MRI images using the Intravoxel Incoherent Motion (IVIM) model. The IVIM model is a technique used to quantify the diffusion and perfusion of water molecules in biological tissues. The output is a set of parameters that can be used to evaluate the diffusion and perfusion properties:
- S0: The signal intensity in the absence of diffusion weighting (b-value = 0). S0 represents the overall water content in the tissue being imaged and is related to the proton density.
- f: The perfusion fraction, which is the fraction of water molecules that undergo microcirculation within the capillaries and small vessels in the tissue. f is a measure of the relative contribution of the perfusion component to the DW-MRI signal.
- D*: The psudo-diffusion coefficient of the water molecules in the microcirculation compartment. It is independent of the direction of the diffusion-weighting gradients.
- D: The pure diffusion coefficient, which is the effective diffusion coefficient of the water molecules in the tissue. It is dependent on the direction of the diffusion-weighting gradients.
The fitting is done in the following steps:
- Estimate D and S0 (referred to as \(\textrm{S0}_1\)) using a linear fit of the high b-values, as defined in the settings.
- Estimate D* and S0 (referred to as \(\textrm{S0}_2\)) using a linear fit of the low b-values, as defined in the settings.
- Estimate f as \(\max\left(1-\frac{S0_2}{S0_1}, 0\right)\). where \(\max()\) is used to eliminate negative estimates of f.
- Estimate D* and f using a non-linear fit, keeping S0 and D fixed.
- Optional: Do a full model, non-linear fit, of all parameters, with the estimates in steps 1-4 as initial guess.
Inputs
Dataset
Input diffusion weighted dataset. Different b-values must be found in one or more channels of the dataset.
Type: Image, List, Required, Single
Mask
A 3D binary mask defining the region in the image where the IVIM parameter maps are calculated.
Type: Mask, List, Optional, Single
Outputs
S0
The estimated \(S0\) parameter map.
Type: Image
f
The estimated \(f\) parameter map.
Type: Image
D*
The estimated \(D^*\) parameter map.
Type: Image
D
The estimated \(D\) parameter map.
Type: Image
Convergence
Mask indicating where the fit has converged.
Type: Mask
Settings
Configure
Perfusion Upper b-value Float
Highest b-value used for estimating the perfusion part of the signal.
Diffusion Lower b-value Float
Lowest b-value used for estimating the diffusion part of the signal.
Full Nonlinear Fit Boolean
Perform a full model, non-linear fit of all parameters, with the estimates in steps 1-4 as initial guess.
Batch Size Integer
The number of voxels that are computed per batch. This is a trade-off between performance and memory consumption.
Keep Metadata Boolean
Keep or discard metadata in the parameter maps.
Metadata
B-values Source Selection
Select the b-value source for the calculation. The b-values can be taken from the metadata, or can be supplied as an input Numeric Array.
Values: Metadata, Numeric Array Input
B-value Tag Text
Name of the B-Value tag in the metadata.
Bounds
S0 Bounds Numbers
Bounds on S0, either empty if no bounds or on the format \([lower, upper]\).
f Bounds Numbers
Bounds on f, either empty if no bounds or on the format \([lower, upper]\).
D* Bounds Numbers
Bounds on \(D^*\), either empty if no bounds or on the format \([lower, upper]\).
D Bounds Numbers
Bounds on D, either empty if no bounds or on the format \([lower, upper]\).
Configure NonLinear
Tolerance Float
The threshold change in the objective function. Changes smaller than this value imply that the fitting has converged.
Return Convergence Map Boolean
Output a binary mask, where all true voxels have converged in the fit. This can be used to exclude voxels that did not converge.
Partial Fit, Max Iterations Integer
Maximum number of iterations for the partial fit, described in in step 4.
Full Fit, Max Iterations Integer
Maximum number of iterations for the full fit, described in in step 5.
See also
Keywords:
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